Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review
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paper_title: Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

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Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.
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paper_title: Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine

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Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based post-classification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.
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paper_title: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

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Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.
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paper_title: Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

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Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer MODIS satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination R 2 of 0.97490 and 0.92298, whereas the root mean square errors RMSEs were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.
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paper_title: Use of Remote Sensing Data for Estimation of Aman Rice Yield

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Weather related crop losses have always been a concern for farmers, governments, traders and policy makers for the purpose of balanced food supplies, demands, trade, and distribution of aid to nations in need. This paper discusses the utility of Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices as proxies for modelling inter annual variation in Aman rice (AR) yield in Bangladesh and for early estimation. We compare annual local and hybrid AR yield with VH Indices computed for each week during 1991-2005. A strong correlation was found between AR yield and VH during the period of AR development that occurs during one/two months in advance of harvest (early October to early November). Stepwise principal components regression (PCR) was used to construct a model to estimate yield as a function of critical-period VH indices. The model reduced the yield prediction error variance by 97% and 92% compared with a prediction of average local Aman rice (LAR) and hybrid Aman rice (HAR) yield for each year respec- tively.
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paper_title: Estimation of paddy field area using the area ratio of categories in each mixel of Landsat TM

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Abstract It is important to estimate the area of a paddy field accurately in order to evaluate the production of rice and the methane flux from paddy fields, which contributes to the greenhouse effect. Paddy fields are irrigated and a single crop is cultivated in them. Therefore, paddy fields are easy to classify compared to upland fields and forests. However, mixels decrease the accuracy of classification and increase the error in area estimation. To overcome these problems, we developed a model to estimate the area of paddy fields in pixels from Landsat TM data. We applied this model to verification sites in Hokkaido and Miyagi Prefecture. In these verifications of our model, we were able to estimate paddy field areas with 1-4 per cent and 3-3 per cent overall error, respectively. These results show that we can apply our model to practical use.
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paper_title: Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data

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An operational crop yield model was developed by introducing realtime satellite imagery into a Geographical Information System (GIS) and the Crop Specific Water Balance (CSWB) model of the Food and Agriculture Organization (FAO). Input databases were developed with three different resolutions; agro-ecological zone (AEZ) polygons, 7.6 km and 1.1 km pixels; from archived satellite data commonly used by Early Warning Systems (EWS) to simulate maize yield and production in Kenya from 1989 to 1997. Simulated production results from the GIS-based CSWB model were compared to historical maize production reports from two Government of Kenya (GoK) agencies. The coefficients of determination (r 2) between the model and GoK district reports ranged from 0.86 to 0.89. The results indicated the 7.6 km pixel-by-pixel analysis was the most favorable method due to the Rainfall Estimate (RFE) input data having the same resolution. The GIS-based CSWB model developed by this study could also be easily expanded for use in othe...
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paper_title: Detection and estimation of mixed paddy rice cropping patterns with MODIS data

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Abstract In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level ( P
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paper_title: Rice Mapping and Monitoring Using ENVISAT ASAR Data

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Radar remote sensing technology has become an important method for stable and long-time rice monitoring for its capability to operate in all weather conditions. In this letter, ENVISAT advanced synthetic aperture radar (ASAR) alternative-polarization VV/HH data were used for rice monitoring in the Xinghua rice experiment site in the middle of Jiangsu Province. First, a threshold classification method was developed for mapping rice growth area according to the different characteristic of backscatter coefficients between paddy rice and other land surface objects. Then, relational models were built for retrieving rice growth parameters from ASAR images based on correlation analysis between backscatter coefficients and field measurements. Meanwhile, an optical multispectral image was used as ancillary data for rice parameters retrieval. As expected, the retrieved rice growth parameters were consistent with those of field measurements.
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paper_title: Crop yield estimation model for Iowa using remote sensing and surface parameters

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Abstract Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction. Minimization of least square loss function has been carried out through iterative convergence using pre-defined empirical equation that provided acceptable lower residual values with predicted values very close to observed ones ( R 2 = 0.78) for Corn and Soybean crop ( R 2 = 0.86) for Iowa state. The crop yield prediction model discussed in the present paper will further improve in future with the use of long period dataset. Similar model can be developed for different crops of other locations.
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paper_title: Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

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The visible band of Landsat ETM+ (Band 1, Band 2, and Band 3) showed a weak exponential relationship to rice age; however, the reflective infrared band of Landsat ETM+ (Band 4 and B5) and the entire vegetation index showed a strong exponential relationship to rice age. Rice Growth Vegetation Index (RGVI) developed in this study is the best vegetation index compared to existing vegetation indices. The relationship between rice age and RGVI has shown the highest determination coefficient (R 2 ) of 0.9045. The RGVI then used to develop a model to rice plant mapping. Quantitative comparison of the rice plant area between analysis results and reference data showed a linear relationship, with the equation y = 0.920x - 3.841 and R² = 0.971, where y is the rice plant area of reference data, and x is the rice plant area of the analysis results of the Landsat ETM+. The standard error of this estimation was 43.04 ha.
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paper_title: Mapping Indonesian Rice Areas Using Multiple- Temporal Satellite Imagery

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There is a growing demand for rice with increase in population. As rice is still the major staple food in Indonesia, the task of increasing rice production continues to engage the attention of national planners. Various methods of rice areas estimation can provide different information Satellite data, provides information periodically, wide coverage area, can be used as a source of information on the condition of rice areas. This study has an objective of using multi-temporal satellite imagery from the Moderate Resolution Imaging Spectrometer (MODIS) to map the Indonesian rice paddies area. The algorithm was based on temporal profiles of vegetation strength and water content, using electromagnetic surface reflectance in visible to near infrared range. The results obtained from the analysis were compared to national statistics. Estimated Indonesian regional rice area was 8.27 million ha, which agrees with published values. The model performance was dependent on rice ecosystems. Good linear relationships between the model results and the national statistics were observed for all types of rice fields.
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paper_title: Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis

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Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre SPOT VEGETATION SPOT VGT data 1998–2008 were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index NDVI classes generated from the iterative self-organizing data analysis technique algorithm ISODATA classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns 94% overall accuracy, 0.93 Kappa coefficient. We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.
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paper_title: Using NOAA AVHRR and landsat TM to estimate rice area year-by-year

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Remote sensing has been widely used to estimate crop area and production. Forecasting the crop area year-by-year is the key step to realizing crop production forecasting. In this letter, a methodology to calculate rice area using NOAA AVHRR and Landsat TM data is introduced. The rice area in 1992 was estimated using Landsat TM data. 'Rice' pixels in 1992 and 1994 were calculated and the rice area change trend determined using NOAA AVHRR data for the two years. With a linear statistical model, the rice area of 1994 was forecast. Its accuracy was 84.5 per cent compared to the data released by the Agricultural Investigation Team(AIT) of Hubei province. The same method was applied to estimate the rice area of 1995. The accuracy was 91.6 per cent compared with AIT data.
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paper_title: Yield Trends Are Insufficient to Double Global Crop Production by 2050

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Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.
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paper_title: Rice yield forecasting models using satellite imagery in Egypt

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Abstract Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI) with leaf area index (LAI) were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI) did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI) produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI) combined with leaf area index (LAI) produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90 days after planting in any similar environmental conditions and agricultural practices.
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paper_title: Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

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Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
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paper_title: Mapping rice areas of South Asia using MODIS multitemporal data

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Our goal is to map the rice areas of six South Asian countries using moderate- resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74%ofthepopulationthatlivesonlessthan$2.00aday.Thepopulationoftheregionisgrowing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. C 2011 Society of Photo-Optical Instrumentation Engineers
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paper_title: The Future of Rice Production, Consumption and Seaborne Trade: Synthetic Prediction Method

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The Synthetic Prediction Method (SPM) is used to determine the factors affecting the production, consumption and transportation of rice in Asia, Africa, Europe, the Americas, and Oceania. Rice defi cits, surpluses, and balances are investigated worldwide. Attention is given to shortages of rice in Africa and how to alleviate them. The requirements for seaborne trade of rice from Asia to Africa are investigated and estimated. Rice is the staple food for about one half of the world’s population, and it supplies 20% of the calories consumed worldwide. The International Rice Research Institute (IRRI 2000) estimated food supplies relative to the population of the world and predicted that 800 million tons of rice will be required in 2025. The Food and Agriculture Organization (FAO, 2000) estimated rice production and consumption for each country through 2005. However, long-term estimates were not made. Tsujii (1997) devised a method to make accurate predictions regarding production and consumption, especially for Japan and China. Yap (1997) conducted research on the main factors for production and consumption. He predicted worldwide rice consumption in developed and developing countries through 2010. In this paper, predictions for per capita consumption are made for developed countries. Production predictions are based on harvest area and yield per hectare. The focus of these predictions is on developing countries. Schwartz (1991) showed that worldwide rice production and consumption were expected to grow at a slower rate in the 1990s than they did in the 1970s and 1980s. Her predictions dealt with rice consumption and Asian trade, and she demonstrated that Asian trade affects the worldwide rice trade. These predictions all concerned worldwide rice production and consumption for rice-producing countries. Trade volume was estimated on the basis of the difference in production and consumption. No attempt was made to predict the number of ships from each area that would be involved in the maritime transportation of rice. The present study is based on rice production and consumption and supply and demand relative to several specifi c rice-growing areas of the world. In addition, 1998 population data from the United Nations and the U.S. were used to forecast consumption through 2030. Finally, worldwide calculations of surpluses, defi cits, and requirements for the seaborne transportation of rice were undertaken. This study identifi es the areas of rice scarcity, those of rice surpluses, and their balance. Ultimately, the focus of this study is to provide basic information concerning worldwide food with the goal of contributing to solve the looming food problem.
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paper_title: What it will take to Feed 5.0 Billion Rice consumers in 2030

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Major advances have occurred in rice production due to adoption of green revolution technology. Between 1966 and 2000, the population of densely populated low income countries grew by 90% but rice production increased by 130% from 257 million tons in 1966 to 600 million tons in 2000. However, the population of rice consuming countries continues to grow and it is estimated that we will have to produce 40 more rice in 2030. This increased demand will have to be met from less land, with less water, less labor and fewer chemicals. To meet the challenge of producing more rice from suitable lands we need rice varieties with higher yield potential and greater yield stability. Various strategies for increasing the rice yield potential being employed include: (1) conventional hybridization and selection procedures, (2) ideotype breeding, (3) hybrid breeding, (4) wide hybridization and (5) genetic engineering. Various conventional and biotechnology approach are being employed to develop durable resistance to diseases and insect and for tolerance to abiotic stresses. The availability of the rice genome sequence will now permit identification of the function of each of 60,000 rice genes through functional genomics. Once the function of a gene is identified, it will be possible to develop new rice varieties by introduction of the gene through traditional breeding in combination with marker aided selection or direct engineering of genes into rice varieties.
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paper_title: Estimation of cropped area and grain yield of rice using remote sensing data

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Abstract Studies for estimating cultivated area and yield of rice using remote sensing data acquired from the Landsat Thematic Mapper (TM) system with six bands (1, 2, 3,4, 5,7) were conducted. Three images taken on different days were visually and digitally analysed for the estimation of rice cultivated area. This was obtained with over 90 per cent classification accuracy. False colour composite of 5R, 4G, 3B was selected for the identification of rice cultivated area at maturity stage. Band combination of 1, 3, 4 and 5 was selected as an appropriate subset in the digital analysis for the estimation of rice areas. An attempt was made to develop a relationship between reflectance values and actual grain yield using an image of the fully ripened stage of the crop. High correlation was observed with reflectance values of some bands and yields. A computerized plant process model was adopted for the simulation of rice growth and yield. This was used for estimating yield per unit area.
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paper_title: Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

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Abstract In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.
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paper_title: Extraction of rice-planted area using a self-organizing feature map

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We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.
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paper_title: Large-area rice yield forecasting using satellite imageries

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Abstract Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice ( Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha −1 for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha −1 provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.
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paper_title: Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine

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Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based post-classification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.
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paper_title: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

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Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.
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paper_title: Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

paper_content:

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer MODIS satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination R 2 of 0.97490 and 0.92298, whereas the root mean square errors RMSEs were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.
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paper_title: Estimation of paddy field area using the area ratio of categories in each mixel of Landsat TM

paper_content:

Abstract It is important to estimate the area of a paddy field accurately in order to evaluate the production of rice and the methane flux from paddy fields, which contributes to the greenhouse effect. Paddy fields are irrigated and a single crop is cultivated in them. Therefore, paddy fields are easy to classify compared to upland fields and forests. However, mixels decrease the accuracy of classification and increase the error in area estimation. To overcome these problems, we developed a model to estimate the area of paddy fields in pixels from Landsat TM data. We applied this model to verification sites in Hokkaido and Miyagi Prefecture. In these verifications of our model, we were able to estimate paddy field areas with 1-4 per cent and 3-3 per cent overall error, respectively. These results show that we can apply our model to practical use.
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paper_title: Detection and estimation of mixed paddy rice cropping patterns with MODIS data

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Abstract In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level ( P
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paper_title: Rice Mapping and Monitoring Using ENVISAT ASAR Data

paper_content:

Radar remote sensing technology has become an important method for stable and long-time rice monitoring for its capability to operate in all weather conditions. In this letter, ENVISAT advanced synthetic aperture radar (ASAR) alternative-polarization VV/HH data were used for rice monitoring in the Xinghua rice experiment site in the middle of Jiangsu Province. First, a threshold classification method was developed for mapping rice growth area according to the different characteristic of backscatter coefficients between paddy rice and other land surface objects. Then, relational models were built for retrieving rice growth parameters from ASAR images based on correlation analysis between backscatter coefficients and field measurements. Meanwhile, an optical multispectral image was used as ancillary data for rice parameters retrieval. As expected, the retrieved rice growth parameters were consistent with those of field measurements.
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paper_title: Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

paper_content:

The visible band of Landsat ETM+ (Band 1, Band 2, and Band 3) showed a weak exponential relationship to rice age; however, the reflective infrared band of Landsat ETM+ (Band 4 and B5) and the entire vegetation index showed a strong exponential relationship to rice age. Rice Growth Vegetation Index (RGVI) developed in this study is the best vegetation index compared to existing vegetation indices. The relationship between rice age and RGVI has shown the highest determination coefficient (R 2 ) of 0.9045. The RGVI then used to develop a model to rice plant mapping. Quantitative comparison of the rice plant area between analysis results and reference data showed a linear relationship, with the equation y = 0.920x - 3.841 and R² = 0.971, where y is the rice plant area of reference data, and x is the rice plant area of the analysis results of the Landsat ETM+. The standard error of this estimation was 43.04 ha.
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paper_title: Mapping Indonesian Rice Areas Using Multiple- Temporal Satellite Imagery

paper_content:

There is a growing demand for rice with increase in population. As rice is still the major staple food in Indonesia, the task of increasing rice production continues to engage the attention of national planners. Various methods of rice areas estimation can provide different information Satellite data, provides information periodically, wide coverage area, can be used as a source of information on the condition of rice areas. This study has an objective of using multi-temporal satellite imagery from the Moderate Resolution Imaging Spectrometer (MODIS) to map the Indonesian rice paddies area. The algorithm was based on temporal profiles of vegetation strength and water content, using electromagnetic surface reflectance in visible to near infrared range. The results obtained from the analysis were compared to national statistics. Estimated Indonesian regional rice area was 8.27 million ha, which agrees with published values. The model performance was dependent on rice ecosystems. Good linear relationships between the model results and the national statistics were observed for all types of rice fields.
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paper_title: NDWI : A normalized difference water index for remote sensing of vegetation liquid water from space

paper_content:

The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as (ϱ(0.86 μm) − ϱ(1.24 μm))(ϱ(0.86 μm) + ϱ(1.24 μm)), where ϱ represents the radiance in reflectance units. Both the 0.86-μm and the 1.24-μm channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 μm is negligible. Weak liquid absorption at 1.24 μm is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86–1.24 μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-μm channel is very different from that contained in the red channel near 0.66 μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.
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paper_title: Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis

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Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre SPOT VEGETATION SPOT VGT data 1998–2008 were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index NDVI classes generated from the iterative self-organizing data analysis technique algorithm ISODATA classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns 94% overall accuracy, 0.93 Kappa coefficient. We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.
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paper_title: Using NOAA AVHRR and landsat TM to estimate rice area year-by-year

paper_content:

Remote sensing has been widely used to estimate crop area and production. Forecasting the crop area year-by-year is the key step to realizing crop production forecasting. In this letter, a methodology to calculate rice area using NOAA AVHRR and Landsat TM data is introduced. The rice area in 1992 was estimated using Landsat TM data. 'Rice' pixels in 1992 and 1994 were calculated and the rice area change trend determined using NOAA AVHRR data for the two years. With a linear statistical model, the rice area of 1994 was forecast. Its accuracy was 84.5 per cent compared to the data released by the Agricultural Investigation Team(AIT) of Hubei province. The same method was applied to estimate the rice area of 1995. The accuracy was 91.6 per cent compared with AIT data.
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paper_title: Mapping rice areas of South Asia using MODIS multitemporal data

paper_content:

Our goal is to map the rice areas of six South Asian countries using moderate- resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74%ofthepopulationthatlivesonlessthan$2.00aday.Thepopulationoftheregionisgrowing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. C 2011 Society of Photo-Optical Instrumentation Engineers
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paper_title: Estimation of cropped area and grain yield of rice using remote sensing data

paper_content:

Abstract Studies for estimating cultivated area and yield of rice using remote sensing data acquired from the Landsat Thematic Mapper (TM) system with six bands (1, 2, 3,4, 5,7) were conducted. Three images taken on different days were visually and digitally analysed for the estimation of rice cultivated area. This was obtained with over 90 per cent classification accuracy. False colour composite of 5R, 4G, 3B was selected for the identification of rice cultivated area at maturity stage. Band combination of 1, 3, 4 and 5 was selected as an appropriate subset in the digital analysis for the estimation of rice areas. An attempt was made to develop a relationship between reflectance values and actual grain yield using an image of the fully ripened stage of the crop. High correlation was observed with reflectance values of some bands and yields. A computerized plant process model was adopted for the simulation of rice growth and yield. This was used for estimating yield per unit area.
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paper_title: Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

paper_content:

Abstract In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.
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paper_title: Extraction of rice-planted area using a self-organizing feature map

paper_content:

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.
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paper_title: Estimation of rice-planted area in the tropical zone using a combination of optical and microwave satellite sensor data

paper_content:

The area planted with rice in Indramayu Province, Indonesia was estimated using satellite sensor data. We determined the arable land area using Landsat Thematic Mapper (TM) data acquired in the dry season and identified newly rice-planted fields using JERS-1 Synthetic Aperture Radar (SAR) data acquired in the rainy season (planting season). Two rice crops per year are common and rice is not planted simultaneously in this region. Since the planted area changes monthly, some asumptions were necessary for estimating the planted area. Our estimates suggested that the actual rice-planted area in Indonesia was 12-14% smaller than that reported by the Indonesian government.
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paper_title: Monitoring rice areas using LANDSAT MSS data

paper_content:

Abstract A specialized system to monitor the rice growing areas of New South Wales has been developed. The monitor has been run for 2 years with accurate and consistent results. The monitor system starts with a land-cover mask. Each image is classified and the result used to update the mask using agronomic criteria. The criteria can, and often will, be changed between the different phonological stages of the crop. The system, with incremental revision of a land-cover mask, is quite different in approach to that adopted in multi-temporal image classification as utilized in LACIE and AgRISTARS.
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paper_title: County-level rice area estimation in southern China using remote sensing data

paper_content:

Rice acreage estimation is a key aspect of assessing rice production. A method of estimating rice acreage at the county level is explored, using data from the HJ-1A/B Chinese Environmental Satellite for Hunan Province, which has complex rice cropping patterns. The method combines supervised and unsupervised classification using a mixed-pixel decomposition model. The rice acreage estimation results were validated by interpretation of RapidEye images for early-season rice and ground survey data for medium-season and late-season rice. The results showed a good correlation between the estimates derived from RapidEye and from HJ CCD data for pure rice pixels (R 2 =0.99 , 0.99, and 0.97 for early-, medium-, and late-season rice). The discrepancy was <10% at the plot level (6.5×6.5 km ) for early-season rice, while it was 12.20 and 12.36% at the plot level (1×1 km ) for medium-season and late-season rice. These results suggested that the method proposed in this study is capable of rice acreage estimation at the county level. The method can also be used in mountainous regions and areas of fragmented planting.
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paper_title: Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

paper_content:

The visible band of Landsat ETM+ (Band 1, Band 2, and Band 3) showed a weak exponential relationship to rice age; however, the reflective infrared band of Landsat ETM+ (Band 4 and B5) and the entire vegetation index showed a strong exponential relationship to rice age. Rice Growth Vegetation Index (RGVI) developed in this study is the best vegetation index compared to existing vegetation indices. The relationship between rice age and RGVI has shown the highest determination coefficient (R 2 ) of 0.9045. The RGVI then used to develop a model to rice plant mapping. Quantitative comparison of the rice plant area between analysis results and reference data showed a linear relationship, with the equation y = 0.920x - 3.841 and R² = 0.971, where y is the rice plant area of reference data, and x is the rice plant area of the analysis results of the Landsat ETM+. The standard error of this estimation was 43.04 ha.
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paper_title: Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

paper_content:

The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season and at harvesting times could be used to map different land uses and rice planted at different times (cohorts), and to infer which rice growth stages were present earlier in the season. We performed a maximum likelihood supervised classification and evaluated the robustness of the classifications with the transformed divergence separability index, the kappa coefficient and confusion matrices. Rice was distinguished from other land uses with 98% accuracy and rice cohorts were discriminated with 84% accuracy (three classes) or 94% (two classes). Our study showed that optical remote sensing can reliably identify potential malaria mosquito breeding habitats from space. In the future, these 'crop landscape maps' could be used to investigate the relationship between cultivation practices and malaria transmission.
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paper_title: Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process

paper_content:

National Taiwan University, Spatial Information Research Center, No. 1, Sec. 4,Roosevelt Road, Taipei 10617 TaiwanAbstract. Most paddy rice fieldsin Asia are comprised of small parcelsof land, and theweatherconditionsduringthegrowingseasonareusuallycloudy.Thisstudydevelopsageographicinfor-mationsystem (GIS) object-basedpostclassification(GOBPC)thatcombines low-cost remotelysensed and GIS data to precisely map paddy rice fields in the intensively cultivated but frag-mented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral imageshave an 8-meter resolution and a one-day recurrence, making them ideal for mapping suchareas. Multitemporal images are examined to distinguish the different growth characteristicsbetween paddy rice and other types of ground cover. The pixel-based hybrid classification tech-nique is used with both the unsupervised and supervised approach to distinguish the paddy ricefields from their surroundings. In addition to the pixel-based approach, we also use GOBPC todealwithover-fragmentedparcelsoflandandtoreducetheincidenceofmisclassificationcausedby speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-basedtechnique.TheKappaindexofagreementobtainedwiththeGOBPCreaches0.095to0.291,andthere is a statistically significant improvement in the user and producer accuracy for all theclasses (z > 1.96) with McNemar’s test in the four study areas. The proposed GOBPC approachis shown to be useful in highly fragmented rice growing areas and may have the potential forother agricultural applications.
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paper_title: Rice crop area estimation of an administrative division in China using remote sensing data

paper_content:

In crop production estimation using remotely sensed data, it is necessary to estimate the crop area and its yeild per unit area in a particular administrative division. Two approaches were adopted: (1) the target image of the study area was cut with the administrative boundary, land cover/use classification was processed and crop identification and area calculation were carried out, (2) land cover/use classification was carried out first and then the study area was cut from the image with administrative boundary, and area was estimated. We call these two methods strategy A (cut and classify) and strategy B (classify and cut), respectively. In this paper, the two strategies were applied to rice crop area estimation. The results indicate that strategy B is better than strategy A in the unsupervized classification-cluster process and the rice crop area was estimated with an accuracy over 81% for semi-late rice and 90% for early rice.
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paper_title: Application of Remote Sensing and Geographic Information System (GIS) Techniques for Monitoring of Boro Rice Area Expansion in Bangladesh

paper_content:

Information on existing food growing area and crop yield and how they are changed over the period of time are prerequisite for proper food security planning and management. In Bangladesh, Boro rice (winter rice) plays a vital role in maintaining the food security of the country. In the present study, multitemporal NOAA-AVHRR 1.1 km resolution digital satellite data have been used to study the extension of Boro rice (winter rice) area in Bangladesh during 1989 to 1999. Study on the amplitude and pattern of the NOAA- AVHRR derived Normalized Difference Vegetation Index (NDVI) in relation to phenology and growth of the crop shows that both amplitude and the pattern of the NDVI are sensitive to the crop condition and type. Pixel based analysis has been performed using a digital linear area transformation technique. Arc/Info GIS has been used to obtain crop area statistics. The study has been supplemented by data on historical crop yield, rainfall, ground water level and irrigation. Spatio-temporal analysis of time series NOAA-AVHRR data shows significant increase in Boro rice (winter rice) areas over the country during the study period. Due to more profit and less hazard in winter season, farmers are cultivating more of this rice by replacing the traditional crop varieties. Such an extension of Boro rice (winter rice) area eventually associates a significant increase of utilization of ground water in addition to other sources for irrigation. It is has been found in literature that such an exhaustive use of ground water has resulted in a gradual decline of ground water level in some places over the study area. ::: Keywords: Boro rice (winter rice), Crop acreage, Irrigation, Ground water, Satellite, NOAA-AVHRR, NDVI.
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paper_title: Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain

paper_content:

The use of satellite imagery data to accurately map agricultural land use in semi-arid Africa has proven difficult due to the small size of cropped fields, low vegetative cover within and outside cropped areas, and high correlation between the seasonal trajectories in the signatures of cropped and uncropped surfaces. This paper evaluates the potential of computer-aided classification of multi-temporal imagery data to map traditionally-managed rice fields in Sahelian West Africa. SPOT-XS imagery data covering the southwestern corner of the Inland Niger Delta of Mali were acquired on three dates in 1988. These dates encompass the pre-flood period when ploughed surfaces contrast strongly with surrounding land surface and the first half of the flood period. Two alternative classification approaches were evaluated. Supervised and unsupervised classification of a database formed by principal component reduction proved unsuccessful as a result of the high spectral heterogeneity of ploughed and unploughed surface...
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paper_title: Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

paper_content:

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a ‘peak detector algorithm’, to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlat...
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paper_title: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

paper_content:

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.
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paper_title: Assessment of rice fields by GIS/GPS-supported classification of MODIS data.

paper_content:

The new Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite image offers a large choice of opportunities for operational applications. The 1-km Advanced Very High Resolution Radiometer (AVHRR) image is not suitable for retrieval of field level parameter and Landsat data are not frequent enough for monitoring changes in crop parameters during the critical crop growth periods. A methodology to map areas of paddy fields using MODIS, geographic information system (GIS) and global position system (GPS) is introduced in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy. A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. By integration of MODIS with GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.7% percent compared with the statistical data of the Agricultural Bureau of Zhejiang Province.
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paper_title: Development of a Remote Sensing-Based “Boro” Rice Mapping System

paper_content:

Abstract: Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping ― boro ‖ rice ( i.e. , cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets ( i.e. , land use map, crop calendar, and ground-based rice production information) during the period 2007–2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007–2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007–2009; and (iii) model calibration using data from the period 2007–2009 and its validation using data from the period 2010–2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (
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paper_title: SAR signature investigation of rice crop using RADARSAT data

paper_content:

The current study investigates the potential of multi‐temporal RADARSAT ScanSAR Narrow Beam B (SCNB) data to monitor rice crop growth and condition where special emphasis was given to the signature analysis of the crop. The study area is located in the Baleshwar and Bhadrak districts of Orissa. The temporal variations of radar backscatter of all land‐cover classes were analysed as a function of time. The analysis of the Synthetic Aperture Radar (SAR) backscatter coefficient (σ0) of rice crop shows significant temporal behaviour and a large dynamic range during its growth period, which is due to the interaction of microwave radiation with the crop canopy, increasing from the transplanting stage to the reproductive stage. This temporal variation of SAR backscatter clearly differentiates rice fields from other land‐cover classes. Separability studies among different class pairs carried out using t‐test and Bhattacharya distance show that all the rice classes are separable from each other except the early ric...
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paper_title: Application of multitemporal ERS synthetic aperture radar in delineating rice cropping systems in the Mekong river delta

paper_content:

The aim of this study is to use multitemporal ERS-2 satellite synthetic aperture radar (SAR) images in delineating and mapping areas under different rice cropping systems in the Mekong river delta, Vietnam. Five change index maps were generated from the six images acquired between June and December, 1996. Using a 3-dB threshold, the pixels in each change index map were classified into one of three classes: increasing, decreasing or constant backscattering. The five change index maps were used to generate a composite map with 243 possible change classes. After discarding the minority classes, and merging the dominant classes with similar backscatter time series, the thematic classes of rice cropping systems practices in the study area were delineated.
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paper_title: Rice monitoring and production estimation using multitemporal RADARSAT

paper_content:

Abstract Rice monitoring and production estimation has special significance to China, as rice is the staple grain and accounts for 42% of the crop production in this country. Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Synthetic Aperture Radar (SAR) is thus anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions. It also provides revisit schedules suitable for agricultural monitoring. This paper presents the results of a study examining the backscatter behavior of rice as a function of time using multitemporal RADARSAT data acquired in 1996 and 1997. A rice-type distribution map was produced, showing four types of rice with different life spans ranging from 80 days to 120–125 days. The life span of a rice crop has significant impact on the yield, as well as on the taste and quality of the rice, with the longer growing varieties having the best taste and the highest productivity. The rice production of three counties and two administrative regions, totaling 5000 km 2 , was estimated in this study. The accuracy of the rice classification was found to be 91% (97% after postclassification filtering) providing confidence that multitemporal RADARSAT data is capable of rice mapping. An empirical growth model was then applied to the results of the rice classification, which related radar backscatter values to rice life spans. These life spans could then be used to sum up the production estimates, which were obtained from agronomic models already in use for rice by local agronomists. These models related the yield of rice to their life span based on empirical observations for each type of rice. The resulting productivity estimate could not be compared to any other existing data on yield production for the study-area, but was well received by the local authorities. Based on the studies carried out in the Zhaoqing test site since 1993, it is suggested that rice production estimates require three radar data acquisitions taken at three different stages of crop growth and development. These three growth stages are: at the end of the transplanting and seedling development period, during the ear differentiation period, and at the beginning of the harvest period. Alternatively, if multiparameter radar data is available, only two data acquisitions may be needed. These would be at the end of the transplanting and seedling development period, and at the beginning of the harvest period. This paper also proposes an operational scenario for rice monitoring and production estimation.
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paper_title: CLASSIFICATION OF MULTI-TEMPORAL SAR IMAGES FOR RICE CROPS USING COMBINED ENTROPY DECOMPOSITION AND SUPPORT VECTOR MACHINE TECHNIQUE

paper_content:

This paper presents a combined Entropy Decomposition and Support Vector Machine (EDSVM) technique for Synthetic Aperture Radar (SAR) image classification with the application on rice monitoring. The objective of this paper is to assess the use of multi-temporal data for the supervised classification of rice planting area based on different schedules. Since adequate priori information is needed for this supervised classification, ground truth measurements of rice fields were conducted at Sungai Burung, Selangor, Malaysia for an entire season from the early vegetative stage of the plants to the ripening stage. The theoretical results of Radiative Transfer Theory based on the ground truth parameters are used to de. ne training sets of the different rice planting schedules in the feature space of Entropy Decomposition. The Support Vector Machine is then applied to the feature space to perform the image classification. The effectiveness of this algorithm is demonstrated using multi-temporal RADARSAT-1 data. The results are also used for comparison with the results based on information of training sets from the image using Maximum Likelihood technique, Entropy Decomposition technique and Support Vector Machine technique. The proposed method of EDSVM has shown to be useful in retrieving polarimetric information for each class and it gives a good separation between classes. It not only gives significant results on the classification, but also extends the application of Entropy Decomposition to cover multi-temporal data. Furthermore, the proposed method offers the ability to analyze single-polarized, multi-temporal data with the advantage of the unique features from the combined method of Entropy Decomposition and Support Vector Machine which previously only applicable to multi-polarized data. Classification based on theoretical modeling is also one of the key components in this proposed method where the results from the theoretical models can be applied as the input of the proposed method in order to de. ne the training sets.
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paper_title: Images difference of ASAR data for rice crop mapping in Fuzhou, China

paper_content:

This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.
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paper_title: Rice field mapping and monitoring with RADARSAT data

paper_content:

The aim of this paper is to assess the use of RADARSAT data for rice field mapping and monitoring. The specific characteristics of RADARSAT data, i.e. the radar polarization, the spatial resolution, the range of incidence angles and the repeat cycle, have been studied. Experimental data have been collected over one of the most important irrigated flood plains of Indonesia and analysed along with RADARSAT data. The radar backscatter coefficient sigma of rice fields appears to have a significant temporal variation. However, the dynamic range of RADARSAT data is found lower than that of the ERS data due to a higher backscatter at HH than at VV polarization at early stages. At a higher incidence angle (42 compared with 23), the sigma of the fine resolution F3 mode reaches a saturation level earlier compared with the standard resolution mode, which appears consequently to be most appropriate for rice field mapping. The mapping result, based on the temporal change of the radar backscatter, was found to have an ...
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paper_title: Early estimation of rice area using temporal ERS-1 synthetic aperture radar data a case study for the Howrah and Hughly districts of West Bengal, India

paper_content:

Abstract The characteristic temporal backscattering signature of rice crop grown under flooded condition was used to estimate rice acreage for a region in West Bengal, India. To date ERS-1 Synthetic Aperture Radar (SAR) data, one acquired within 30 days of transplantation and another after 30-40 days was found to be optimum for early estimation of rice acreage. The rice crop was found to be distinctly separable from forest, tree vegetation, village/urban areas. Misclassification of rice was observed mainly with water, waterlogged areas and fallow fields.
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paper_title: The identification of rice fields using multi-temporal ERS-1 C band SAR data

paper_content:

In this paper, an approach for the use of spaceborne SAR data for rice field identification, as well as for land use classification, is demonstrated, by using multitemporal data acquired over the Ohgata-mura area, Akita, Japan with ERS-1 C-band SAR, in 1992. Two techniques for reducing the influence of speckle on the accuracy of rice field identification were tested. A comparison of the SAR results with land use survey data collected in 1993, and with Landsat TM data, shows the potential of the present approach.
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paper_title: Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

paper_content:

This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China.
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paper_title: A neural network integrated approach for rice crop monitoring

paper_content:

Within Asia, rice is a main source of nutrition and provides between 30 and 70% of the daily calories for half the world's population. The importance of rice production demands an effective rice crop monitoring system to provide food security for this region. Recent research has proven radar's capabilities in rice crop monitoring. Radar backscatter increases significantly during a short period of vegetation growth, but large spatial variations in rice crop growth occur due to shifting in the crop calendar. The significant increase in radar backscatter over a short period of time can be used to differentiate rice fields from other land covers. The inter‐field variations can be used to derive information on local farmer practices and to enhance rice field mapping and yield prediction. The rice crop monitoring system developed in this project was based on these variations as applied to a neural network classification. The system delineated rice production areas for one wet and one dry season, and was able to...
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paper_title: Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China

paper_content:

Mapping rice cropping areas with optical remote sensing is often a challenge in tropical and subtropical regions because of frequent cloud cover and rainfall during the rice growing season. Synthetic aperture radar (SAR) is a potential alternative for rice mapping because of its all-weather imaging capabilities. The recent Phased Array-type L-band SAR (PALSAR) sensor onboard the Advanced Land Observing Satellite (ALOS) acquires multipolarization and multitemporal images that are highly suitable for rice mapping. In this pilot study, we demonstrate the feasibility of this sensor in mapping the rice planting area in Zhejiang Province, southeast China. High-resolution ALOS/PALSAR images were acquired at three rice growing stages (transplanting, tillering and heading) and were applied in a support vector machine (SVM) classifier to map rice and other land use surfaces. The results show that, based on the 1:10 000 land use/land cover (LULC) survey map, the rice fields can be mapped with a conditional Kappa val...
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paper_title: ANALYSIS OF TEMPORAL SAR AND OPTICAL DATA FOR RICE MAPPING

paper_content:

This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.
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paper_title: Integrating SAR and optical imagery for regional mapping of paddy rice attributes in the Poyang Lake Watershed, China

paper_content:

The development of agricultural monitoring tools is a focus of the Group on Earth Observations for the Global Earth Observation System of Systems (GEOSS). This requires combining synthetic aperture radar (SAR) and optical satellite sensors to provide more meaningful information in an operational context. The goal of this research was to characterize paddy rice agro-ecological attributes in an operational context. A decision tree framework combined multitemporal and multiscale PALSAR, MODIS, and Landsat observations to map rice extent, hydroperiod, crop calendar, and cropping intensity. The study was carried out in the Poyang Lake Watershed, Jiangxi Province, China. A multiseason field campaign was carried out to calibrate algorithms and validate the rice maps. The field data corresponded relatively well with the remote sensing metrics and validation found that the derived rice paddy maps possessed a high overall accuracy of 89%. The rice maps indicate that the watershed has 25% rice agriculture with 85% o...
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paper_title: Paddy rice mapping of the Caspian Sea coast using microwave and optical remotely sensed data

paper_content:

In this research, an attempted has been made to map paddy rice of the southern coastal areas of Caspian Sea using multi-temporal Radarsat and Landsat TM optical images acquired in different seasons of the year 1998. Two major tasks including land use map preparation and Change detection studies were implemented. Due to the presence of clouds, some parts of the TM based land use map were not completed. So, land use maps prepared with the use of Radarsat and TM fused images derived based on multiplicative, Brovey and principal component algorithms. For change detection studies, image differencing algorithm used to derive the changed areas. Most of the changed areas were observed to be within agricultural lands (Paddy fields). Results of this study show that fused image based on Brovey algorithm has not only eliminated the cloudy areas of TM data but also has more information on land use land cover in comparison with Radarsat and TM images alone. Comparison between land use change map and image derived from change detection studies showed a high correlation between agricultural lands (Paddy fields) and changed areas within the change image. The results of this study could be useful to agricultural and land use land cover planners.
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paper_title: Synergistic use of multi-temporal Radarsat SAR and AWiFS data for Rabi rice identification

paper_content:

A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.
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paper_title: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

paper_content:

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.
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paper_title: Principles Of Synthetic Aperture Radar

paper_content:

Synthetic Aperture Radar (SAR) is an active microwave imaging method.It operates independently of Sun illumination and cloud coverage. Currentspaceborne systems use wavelengths of 3 to 25 cm and achieve resolutionsof 10 to 50 m. The paper attempts to explain the basic SAR imaging principlesusing a minimum of mathematics. Emphasis is put on the particular propertiesof SAR images that should be understood before interpreting these data.
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paper_title: Rice monitoring and production estimation using multitemporal RADARSAT

paper_content:

Abstract Rice monitoring and production estimation has special significance to China, as rice is the staple grain and accounts for 42% of the crop production in this country. Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Synthetic Aperture Radar (SAR) is thus anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions. It also provides revisit schedules suitable for agricultural monitoring. This paper presents the results of a study examining the backscatter behavior of rice as a function of time using multitemporal RADARSAT data acquired in 1996 and 1997. A rice-type distribution map was produced, showing four types of rice with different life spans ranging from 80 days to 120–125 days. The life span of a rice crop has significant impact on the yield, as well as on the taste and quality of the rice, with the longer growing varieties having the best taste and the highest productivity. The rice production of three counties and two administrative regions, totaling 5000 km 2 , was estimated in this study. The accuracy of the rice classification was found to be 91% (97% after postclassification filtering) providing confidence that multitemporal RADARSAT data is capable of rice mapping. An empirical growth model was then applied to the results of the rice classification, which related radar backscatter values to rice life spans. These life spans could then be used to sum up the production estimates, which were obtained from agronomic models already in use for rice by local agronomists. These models related the yield of rice to their life span based on empirical observations for each type of rice. The resulting productivity estimate could not be compared to any other existing data on yield production for the study-area, but was well received by the local authorities. Based on the studies carried out in the Zhaoqing test site since 1993, it is suggested that rice production estimates require three radar data acquisitions taken at three different stages of crop growth and development. These three growth stages are: at the end of the transplanting and seedling development period, during the ear differentiation period, and at the beginning of the harvest period. Alternatively, if multiparameter radar data is available, only two data acquisitions may be needed. These would be at the end of the transplanting and seedling development period, and at the beginning of the harvest period. This paper also proposes an operational scenario for rice monitoring and production estimation.
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paper_title: Application of Remote Sensing and Geographic Information System (GIS) Techniques for Monitoring of Boro Rice Area Expansion in Bangladesh

paper_content:

Information on existing food growing area and crop yield and how they are changed over the period of time are prerequisite for proper food security planning and management. In Bangladesh, Boro rice (winter rice) plays a vital role in maintaining the food security of the country. In the present study, multitemporal NOAA-AVHRR 1.1 km resolution digital satellite data have been used to study the extension of Boro rice (winter rice) area in Bangladesh during 1989 to 1999. Study on the amplitude and pattern of the NOAA- AVHRR derived Normalized Difference Vegetation Index (NDVI) in relation to phenology and growth of the crop shows that both amplitude and the pattern of the NDVI are sensitive to the crop condition and type. Pixel based analysis has been performed using a digital linear area transformation technique. Arc/Info GIS has been used to obtain crop area statistics. The study has been supplemented by data on historical crop yield, rainfall, ground water level and irrigation. Spatio-temporal analysis of time series NOAA-AVHRR data shows significant increase in Boro rice (winter rice) areas over the country during the study period. Due to more profit and less hazard in winter season, farmers are cultivating more of this rice by replacing the traditional crop varieties. Such an extension of Boro rice (winter rice) area eventually associates a significant increase of utilization of ground water in addition to other sources for irrigation. It is has been found in literature that such an exhaustive use of ground water has resulted in a gradual decline of ground water level in some places over the study area. ::: Keywords: Boro rice (winter rice), Crop acreage, Irrigation, Ground water, Satellite, NOAA-AVHRR, NDVI.
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paper_title: Using NOAA AVHRR and landsat TM to estimate rice area year-by-year

paper_content:

Remote sensing has been widely used to estimate crop area and production. Forecasting the crop area year-by-year is the key step to realizing crop production forecasting. In this letter, a methodology to calculate rice area using NOAA AVHRR and Landsat TM data is introduced. The rice area in 1992 was estimated using Landsat TM data. 'Rice' pixels in 1992 and 1994 were calculated and the rice area change trend determined using NOAA AVHRR data for the two years. With a linear statistical model, the rice area of 1994 was forecast. Its accuracy was 84.5 per cent compared to the data released by the Agricultural Investigation Team(AIT) of Hubei province. The same method was applied to estimate the rice area of 1995. The accuracy was 91.6 per cent compared with AIT data.
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paper_title: Remote sensing imagery in vegetation mapping : a review

paper_content:

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.
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paper_title: Development of a Remote Sensing-Based “Boro” Rice Mapping System

paper_content:

Abstract: Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping ― boro ‖ rice ( i.e. , cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets ( i.e. , land use map, crop calendar, and ground-based rice production information) during the period 2007–2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007–2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007–2009; and (iii) model calibration using data from the period 2007–2009 and its validation using data from the period 2010–2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (
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paper_title: On the Performance of Filters for Reduction of Speckle Noise in SAR Images off the Coast of the Gulf of Guinea

paper_content:

Synthetic Aperture Radar (SAR) imagery to monitor oil spills are some methods that have been proposed for the West African sub-region. With the increase in the number of oil exploration companies in Ghana (and her neighbors) and the rise in the coastal activities in the sub-region, there is the need for proper monitoring of the environmental impact of these socio-economic activities on the environment. Detection and near real-time information about oil spills are fundamental in reducing oil spill environmental impact. SAR images are prone to some noise, which is predominantly speckle noise around the coastal areas. This paper evaluates the performance of the mean and median filters used in the preprocessing filtering to reduce speckle noise in SAR images for most image processing algorithms.
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paper_title: Review Paper on Crop Area Estimation Using SAR Remote Sensing Data

paper_content:

Crop detection is a critical and evaluative first step towards almost agricultural monitoring systems. The ability to identify crop type makes it possible to estimate the agricultural area allocated under crop and hence compute relevant statistics providing essential information for crop control of area-based subsidies The capabilities of SAR for discriminating crop type have been previously explored. The main aim is to brief the mechanism of crop acreage estimation and area monitoring using SAR data, Envisat-ASAR, or polarimetric SAR with multi-temporal and multi polarized datasets and review the research progress on crop estimation and area monitoring using microwave remote sensing.
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paper_title: Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

paper_content:

Abstract In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.
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paper_title: Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process

paper_content:

National Taiwan University, Spatial Information Research Center, No. 1, Sec. 4,Roosevelt Road, Taipei 10617 TaiwanAbstract. Most paddy rice fieldsin Asia are comprised of small parcelsof land, and theweatherconditionsduringthegrowingseasonareusuallycloudy.Thisstudydevelopsageographicinfor-mationsystem (GIS) object-basedpostclassification(GOBPC)thatcombines low-cost remotelysensed and GIS data to precisely map paddy rice fields in the intensively cultivated but frag-mented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral imageshave an 8-meter resolution and a one-day recurrence, making them ideal for mapping suchareas. Multitemporal images are examined to distinguish the different growth characteristicsbetween paddy rice and other types of ground cover. The pixel-based hybrid classification tech-nique is used with both the unsupervised and supervised approach to distinguish the paddy ricefields from their surroundings. In addition to the pixel-based approach, we also use GOBPC todealwithover-fragmentedparcelsoflandandtoreducetheincidenceofmisclassificationcausedby speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-basedtechnique.TheKappaindexofagreementobtainedwiththeGOBPCreaches0.095to0.291,andthere is a statistically significant improvement in the user and producer accuracy for all theclasses (z > 1.96) with McNemar’s test in the four study areas. The proposed GOBPC approachis shown to be useful in highly fragmented rice growing areas and may have the potential forother agricultural applications.
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paper_title: Use of Remote Sensing Data for Estimation of Aman Rice Yield

paper_content:

Weather related crop losses have always been a concern for farmers, governments, traders and policy makers for the purpose of balanced food supplies, demands, trade, and distribution of aid to nations in need. This paper discusses the utility of Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices as proxies for modelling inter annual variation in Aman rice (AR) yield in Bangladesh and for early estimation. We compare annual local and hybrid AR yield with VH Indices computed for each week during 1991-2005. A strong correlation was found between AR yield and VH during the period of AR development that occurs during one/two months in advance of harvest (early October to early November). Stepwise principal components regression (PCR) was used to construct a model to estimate yield as a function of critical-period VH indices. The model reduced the yield prediction error variance by 97% and 92% compared with a prediction of average local Aman rice (LAR) and hybrid Aman rice (HAR) yield for each year respec- tively.
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paper_title: Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

paper_content:

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
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paper_title: Rice monitoring and production estimation using multitemporal RADARSAT

paper_content:

Abstract Rice monitoring and production estimation has special significance to China, as rice is the staple grain and accounts for 42% of the crop production in this country. Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Synthetic Aperture Radar (SAR) is thus anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions. It also provides revisit schedules suitable for agricultural monitoring. This paper presents the results of a study examining the backscatter behavior of rice as a function of time using multitemporal RADARSAT data acquired in 1996 and 1997. A rice-type distribution map was produced, showing four types of rice with different life spans ranging from 80 days to 120–125 days. The life span of a rice crop has significant impact on the yield, as well as on the taste and quality of the rice, with the longer growing varieties having the best taste and the highest productivity. The rice production of three counties and two administrative regions, totaling 5000 km 2 , was estimated in this study. The accuracy of the rice classification was found to be 91% (97% after postclassification filtering) providing confidence that multitemporal RADARSAT data is capable of rice mapping. An empirical growth model was then applied to the results of the rice classification, which related radar backscatter values to rice life spans. These life spans could then be used to sum up the production estimates, which were obtained from agronomic models already in use for rice by local agronomists. These models related the yield of rice to their life span based on empirical observations for each type of rice. The resulting productivity estimate could not be compared to any other existing data on yield production for the study-area, but was well received by the local authorities. Based on the studies carried out in the Zhaoqing test site since 1993, it is suggested that rice production estimates require three radar data acquisitions taken at three different stages of crop growth and development. These three growth stages are: at the end of the transplanting and seedling development period, during the ear differentiation period, and at the beginning of the harvest period. Alternatively, if multiparameter radar data is available, only two data acquisitions may be needed. These would be at the end of the transplanting and seedling development period, and at the beginning of the harvest period. This paper also proposes an operational scenario for rice monitoring and production estimation.
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paper_title: A neural network integrated approach for rice crop monitoring

paper_content:

Within Asia, rice is a main source of nutrition and provides between 30 and 70% of the daily calories for half the world's population. The importance of rice production demands an effective rice crop monitoring system to provide food security for this region. Recent research has proven radar's capabilities in rice crop monitoring. Radar backscatter increases significantly during a short period of vegetation growth, but large spatial variations in rice crop growth occur due to shifting in the crop calendar. The significant increase in radar backscatter over a short period of time can be used to differentiate rice fields from other land covers. The inter‐field variations can be used to derive information on local farmer practices and to enhance rice field mapping and yield prediction. The rice crop monitoring system developed in this project was based on these variations as applied to a neural network classification. The system delineated rice production areas for one wet and one dry season, and was able to...
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paper_title: Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

paper_content:

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.
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paper_title: An integrated approach to land cover classification: An example in the Island of Jersey

paper_content:

A land cover map of Jersey was created using remotely sensed images recorded by satellite. This map brought together a range of disparate techniques, developed in isolation and mostly applied experimentally, integrating multisensor, multitemporal, enhanced spatial resolution data within an object-oriented integrated Geographical Information System (GIS) for an applications-driven, operational programme. It was developed under the Classification of Environment with Vector- and Raster-Mapping (CLEVER-Mapping) project: this improved approach to operational land cover mapping used information on the subdivision of the landscape into land parcels to help classify remotely sensed images on a per-parcel basis. The object-oriented approach allowed the use of remotely sensed information which relates directly to ground features, and the application of improved knowledge-based corrections using a range of external data. Unlike a conventional map, the parcel-based approach produced a GIS database containing classifi...
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paper_title: Rice yield forecasting models using satellite imagery in Egypt

paper_content:

Abstract Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI) with leaf area index (LAI) were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI) did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI) produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI) combined with leaf area index (LAI) produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90 days after planting in any similar environmental conditions and agricultural practices.
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paper_title: Use of vegetation index and meteorological parameters for the prediction of crop yield in India

paper_content:

Monsoon rainfall distribution over the Indian sub-continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non-linear iterative multivariate optimization approach (quasi-Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R2>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.
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paper_title: Large-area rice yield forecasting using satellite imageries

paper_content:

Abstract Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice ( Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha −1 for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha −1 provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.
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paper_title: Rice yield forecast based on satellite and meteorological data

paper_content:

A methodological approach to forecasting rice yield based on using MODIS satellite data in combination with meteorological information is given. Underlying the approach is modeling the accumulation of rice biomass during growth and development. Rice yield is predicted in the middle of the growing season.
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paper_title: Use of vegetation index and meteorological parameters for the prediction of crop yield in India

paper_content:

Monsoon rainfall distribution over the Indian sub-continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non-linear iterative multivariate optimization approach (quasi-Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R2>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.
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paper_title: Synergy of Remote Sensing and Modeling for Estimating Ecophysiological Processes in Plant Production

paper_content:

AbstractInformation on the ecological and physiological status of crops is essential for growth diagnostics and yield prediction. Within-field or between-field spatial information is required, especially with the recent trend toward precision agriculture, which seeks the efficient use of agrochemicals, water, and energy. The study of carbon and nitrogen cycles as well as environmental management on local and regional scales requires assessment of the spatial variability of biophysical and ecophysiological variables, scaling up of which is also needed for scientific and decision -making purposes. Remote sensing has great potential for these applications because it enables wide-area, non¬destructive, and real-time acquisition of information about plant ecophysiological conditions. With recent advances in sensor technology, a variety of electromagnetic signatures, such as hyperspectral reflectance, thermal-infrared tempera¬ture, and microwave backscattering coefficients, can be acquired for both plants and e...
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paper_title: A scheme for regional rice yield estimation using ENVISAT ASAR data

paper_content:

Information on rice growing areas and rice production is critical for most rice growing countries to make state and economic policies. However, the areas where rice crop is cultivated are often cloudy and rainy, which entails the use of radar remote sensing data for rice monitoring. In this paper, a practical scheme to integrate multi-temporal and multi-polarization ENVISAT ASAR data into rice crop model for regional rice yield estimation has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the observed multi-temporal rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000 to predict rice yield. The assimilation method re-initializes the model with optimal input parameters, allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e., the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced. The scheme was validated over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% on average and with a root mean square error of approximately 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.
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paper_title: Coupling radar data and rice growth model for yield estimation

paper_content:

The objective of this paper is to assess the use of satellite radar data to estimate rice yield. The approach consists in combining the information retrieved from radar data with a rice crop growth model to predict the final yield. Data acquired by different spaceborne radar systems have been evaluated in terms of their ability to provide rice crop parameters. The radar backscattering coefficient at C-band (5.3 GHz) measured by ERS, and to a lesser extent, RADARSAT, have been proven to be related to rice plant age, height and biomass. The rice growth model ORYZA1 was used to simulate rice growth with sowing date and biomass values retrieved from ERS and RADARSAT SAR data as input parameters. Test sites in both tropical and temperate regions were selected to develop the methodology. The simulated yields were found to lie within 10% from in-situ measured yield when ERS data were used and 15% with RADARSAT data. These results appear very promising for rice yield estimation. The work needs to be extended to a wide range of environmental conditions in order to estimate the method robustness.
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paper_title: Remote sensing and crop production models: present trends

paper_content:

Abstract The use of remote sensing as a tool for estimating crop production on a regional scale has been suggested for quite a while. Since the use of remotely sensed information to directly estimate crop production is questionable (primarily due to the indirect link between remotely sensed data and crop state variables), crop models may be used as a companion tool to remote sensing. Various types of crop models (statistical, deterministic, semi-empirical) are described, and specific methods (forcing, recalibration, statistical correction) are described for introducing remotely sensed information into models. The appropriance of combining different model types and sources of remotely sensed information, problems related to time scales, and the need for robust and independent phenological routines are also discussed.
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paper_title: Estimation rice yield based on integration remote sensing information and crop model

paper_content:

Crop model is a powerful tool in crop growth monitoring and yield forecasting, however crop model is developed based ::: on single point scale, due to regional differentiation、field variation and other reasons lead to input parameters and initial ::: conditions which required by crop model simulation are hard to obtain, the application of crop model has been greatly ::: limited in the regional scale, the introduction of remote sensing will solve this problem, remote sensing is combined with ::: the crop model WOFOST, using the state variable retrieved by remote sensing to optimize crop model simulation, ::: revaluing the sensitive parameters and initial conditions which needed in crop model on the region scale, in order to take ::: the advantage of crop model in the area.This study is on the basis of adaptive adjustment and amendment of crop model ::: WOFOST, build a winter wheat growth simulation model which is suitable for Yucheng, Shandong; Using the field ::: experiment data calibration and validation the WOFOST model, discussed the method which combined crop simulation ::: model and remote sensing under water stress level, using remote sensing calibrated some key processes of crop ::: simulation or reinitialize、parameterize the crop simulation model in order to achieve the optimization model; Explored ::: some reasonable and practical method of remote sensing information application in crop simulation at regional scale, ::: with more research, make it possible to monitor regional crop growth and forecast the output.
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paper_title: Use of Remote Sensing Data for Estimation of Aman Rice Yield

paper_content:

Weather related crop losses have always been a concern for farmers, governments, traders and policy makers for the purpose of balanced food supplies, demands, trade, and distribution of aid to nations in need. This paper discusses the utility of Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices as proxies for modelling inter annual variation in Aman rice (AR) yield in Bangladesh and for early estimation. We compare annual local and hybrid AR yield with VH Indices computed for each week during 1991-2005. A strong correlation was found between AR yield and VH during the period of AR development that occurs during one/two months in advance of harvest (early October to early November). Stepwise principal components regression (PCR) was used to construct a model to estimate yield as a function of critical-period VH indices. The model reduced the yield prediction error variance by 97% and 92% compared with a prediction of average local Aman rice (LAR) and hybrid Aman rice (HAR) yield for each year respec- tively.
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paper_title: Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices

paper_content:

This study explores the potential application of NOAA/AVHRR based satellite indices to estimate the soybean yield for Brazil. NOAA AVHRR GVI (Global Vegetation Index) weekly maximum composite NDVI (Normalized Difference Vegetation Index) data sets with a resolution of 16 km for the period of 1985 to 1998 (except 1995 due to missing data) provided by NOAA/NESDIS were used in this study. Nine soybean yield models, including eight principal production states and the country, were constructed using observed yield data and NDVI and/or TCI (Temperature Condition Index) data from 1985 to 1995. The data period of 1996 to 1998 was used to evaluate the model performance. The crop yield is generally affected by technological improvements through time and by the annual weather fluctuation. The contribution of technological improvements was approximated by trend term and weather-related fluctuations of yield around the trend were estimated through AVHRR-based indices. The results showed that four states had significan...
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paper_title: Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

paper_content:

Abstract We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat ( Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha −1 ) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha −1 ), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha −1 , respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single models.
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paper_title: Advanced methods of plant disease detection. A review

paper_content:

Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.
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Title: Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review

Section 1: Introduction
Description 1: Provide an overview of the significance of rice cultivation, current methods for rice area mapping and production forecasting, and the need for remote sensing technology.

Section 2: Remote Sensing-Based Methods for Mapping Rice Area
Description 2: Review various remote sensing-based approaches for mapping rice areas, including the use of optical and microwave sensors.

Section 3: Optical Remote Sensing-Based Mapping Methods
Description 3: Discuss the application of optical remote sensing methods in mapping rice areas, detailing specific satellite sensors and their effectiveness.

Section 4: Microwave Remote Sensing-Based Mapping Methods
Description 4: Explore the use of microwave remote sensing methods for rice area mapping, including the benefits and limitations of these methods.

Section 5: Integration of Optical and Microwave Remote Sensing-Based Mapping Methods
Description 5: Examine studies that combine both optical and microwave remote sensing methods to enhance rice area mapping accuracy.

Section 6: Limitations of Remote Sensing-Based Mapping Methods
Description 6: Outline the challenges and limitations associated with current remote sensing-based methods for rice area mapping.

Section 7: Optical Remote Sensing-Based Forecasting Methods
Description 7: Describe the use of optical remote sensing in forecasting rice yield and the various vegetation indices used for this purpose.

Section 8: Microwave Remote Sensing-Based Forecasting Methods
Description 8: Discuss the application of microwave remote sensing in forecasting rice yield and their empirical results.

Section 9: Limitations of Remote Sensing-Based Forecasting Methods
Description 9: Identify the limitations and challenges specific to forecasting rice yields using remote sensing methods.

Section 10: Synergy between Remote Sensing-Based Methods and Other Ones
Description 10: Investigate the integration of remote sensing methods with meteorological parameters and crop growth models to improve rice yield forecasting.

Section 11: Synergy between Remote Sensing-Based Methods and Meteorological Parameters
Description 11: Provide examples of studies that integrate meteorological data with remote sensing for enhanced yield prediction.

Section 12: Synergy between Remote Sensing-Based Methods and Crop Growth Model
Description 12: Discuss the integration of crop growth models with remote sensing data for better yield estimation and production forecasting.

Section 13: Limitations of Synergy between Remote Sensing-Based Methods and Other Ones
Description 13: Highlight the limitations and challenges in combining remote sensing with meteorological data and crop growth models.

Section 14: Concluding Remarks
Description 14: Summarize key findings, potential future directions, and the operational challenges of using remote sensing for mapping rice area and forecasting production.