title: Report from the Commission on remote sensing applied to agricultural statistics during the period 1994-1998

eurovoc_concepts:
action programme
agricultural statistics
project evaluation
remote sensing

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text: COMMISSION OF THE EUROPEAN COMMUNITIES Brussels, 29. 10. 1998 COM(1998) 613 final REPORT FROM THE COMMISSION on remote sensing applied to agricultural statistics during the period 1994-1998 Contents 1. INTRODUCTION PART I - OPERATIONAL ACTIVITIES 1 ACTIVITY A: REGIONAL INVENTORIES 1. 1 Objective and methods 1. 2 Progress of the work 1. 3 Financial aspects 1. 4 Conclusions and policy 2 ACTIVITY B: EUROPEAN RAPID ESTIMATES 2. 1 Objective and methods 2. 2 Progress of the work 2. 3 Working procedures and the main developments 2. 4 The role of the ground surveys : 2. 5 Use of financial resources 2. 5. 1 Images 2. 5. 2 Image processing and estimate production 2. 5. 2. 1 Project execution and management 2. 5. 2. 2 Project launch 2. 5. 2. 3 Preparations for the season 2. 5. 2. 4 Acquisition and preliminary processing of satellite data 2. 5. 2. 5 Interpretation of satellite data 2. 5. 2. 6 Current situation 2. 5. 2. 7 Training 2. 5. 2. 8 Maintenance 5 7. 7 7 7 8 8 10 10 10 11 12 13 14 14 14 15 15 15 15 15 16 16 2. 5. 2. 9 Internal project control 2. 5. 2. 10 Reports 2. 5. 2. 11 Product delivery 2. 5. 2. 12 Cost of image processing and production chain for estimates 2. 5. 3 Software maintenance 2. 5. 4 Ground surveys 2. 5. 5 Financial management 2. 6 Results obtained and error analysis 2. 7 Evaluation of the action : 2. 8 Conclusions PART II - ACTIVITIES FOR DEVELOPMENT 3 ACTIVITY C: THE ADVANCED AGRICULTURAL INFORMATION SYSTEM. 3. 1 C. l. Vegetation condition indices 3. 2 C. 2. Yield prediction models 3. 2. 1 Meteorological data 3. 2. 2 Pedological data and agronomic parameters: 3. 2. 3 Yield prediction: 3. 2. 4 Information technology: 3. 3 C. 3: Integrated agricultural information system and MARS bulletin 3. 4 C. 4. Integrated system at national and regional level 4 ACTIVITY D: MONITORING AND PREDICTION OF HARVESTS OUTSIDE THE COMMUNITY 5 ACTIVITY E: APPLICATION OF NEW METHODS OR SENSORS 5. 1 Pilot project for uses of microwave images in estimating acreages 16 16 16 16 17 18 18 19 20 22 24 24 24 25 26 26 27 27 27 29 30 31 31 5. 2 Re-engineering of Activity B (REACT_B) 5. 3 The aeropalynological model 32 33 6 CONCLUSIONS CONCERNING THE ACTIVITIES BEING DEVELOPED 34 ANNEXES 1. 2. 3. 4. 5. 6. 7 MARS Activity B acreage estimates for the main crops and deviations in relation to Eurostat (1994): EUR-12 MARS Activity B acreage estimates for the main crops and deviations in relation to Eurostat (1995): EUR-12 MARS Activity B acreage estimates for the main crops and deviations in relation to Eurostat (1996): EUR-15 MARS Activity B acreage estimates for the main crops and deviations in relation to Eurostat (1997): EUR-15 Operational activity contractors List of MARS publications 1994-1998 Abbreviations used 34 34 35 36 37 38 39 48 Introduction On 26 September 1988, the Council adopted Decision 88/503/EEC adopting a pilot project on remote sensing applied to agricultural statistics (better known as the MARS project). This pilot project was intended to develop and test analysis techniques and methods to improve the information on agriculture in the European Union by incorporating more space data in the Community statistical information system. The development project was planned to last ten years, but the above decision covered only the first five years (1989-1993). At the end of that five-year period, and in accordance with that Council Decision, a report on the first phase of the pilot project (1989-1993) was drawn up and submitted to the Council, together with a proposal for continuing the applications which had become operational. On that basis, on 14 November 1994 the Council adopted Decision n° 94/753/EC on the continued application of remote sensing to agricultural statistics during the period 1994 to 1998\ The purpose of the applications covered by that decision was more particularly: • • to facilitate the use of remote sensing by interested Member States with a view to improving their national agricultural statistics systems (Activity A - Regional inventories); to provide estimates, before harvest, of acreages and potential production of the main crops at European and, if possible, national level within the limits of the financial resources available (Activity B - European rapid estimates). Article 5 of Council Decision 94/753/EC stipulates that the Commission shall submit to the European Parliament and to the Council, by 31 July 1998, a report on the implementation of the measures and on the use of the financial resources put at its disposal for the purpose, "including, where necessary, proposals on the continued application of remote sensing to agricultural statistics". Part I of this report is devoted to those activities. The Council also noted the Commission's intention to continue, during implementation of the 4tn framework programme for research and development, a number of measures forming part of the MARS project but requiring further research before they could be applied at European level. This concerns the following activities: 1 O. J. n°L 273 of 05. 10. 1988. 2 Document COM(94) 332 final of 19. 09. 1994. 3 O. J. n° L 299 of 22. 11. 1994, page 27. establishing an advanced agricultural information system at Community level by combining information obtained by various actions developed and tried out during the programme (Activity C - Integrated System); devising a method similar to that developed for the European Union to allow forecasts and early estimates of harvests in certain regions bordering the European Union, particularly East European countries and possibly the Maghreb countries, in liaison with the programmes for co-operation with these countries (Activity D - Monitoring and prediction of harvests outside the Community); • continued research on the potential use of new sensors, such as radar, for statistical applications, to complement the high-resolution satellites and meteorological satellites currently used in the MARS project (Activity E - Application of new methods or sensors). Part II of this report contains a brief description of these activities, carried out by the Ispra Joint Research Centre (JRC) in association with the Directorate General for Agriculture (DG VI) and the Statistical Office of the European Communities (Eurostat). PART I - Operational activities 1. ACTIVITY A: REGIONAL INVENTORIES 1. 1 Objective and methods The purpose of this activity is to meet the need for accurate, objective annual information on acreages and yields at regional level for the main crops of interest. The methodology combines "high resolution" satellite data (covering the whole area of interest) and data obtained by "area sampling" surveys on the ground. Development and evaluation related mainly to the regression estimator method. The action was organised in two separate parts: • objective ground observation with a sampling plan established or refined by remote sensing; • the automatic classification of spatial data, improving the estimates of the ground surveys by a regression method. Remote sensing also supplies the survey officer with documents enabling him to identify the exact location of plots on the ground. 1. 2 Progress of the work During the pilot phase of the MARS project, this action comprised three separate phases: • 1988-1989: during this phase a strictly identical methodology was applied to 5 regions; • 1990-1991: during this period, local adjustments were made and the active participation of the regions or national authorities was required; • 1992-1993: during this phase the project was used to support national or regional initiatives. At the end of phase 1 of the pilot project on remote sensing applied to agricultural statistics, responsibility for implementing this activity had been largely transferred to the Member States. However, in accordance with Council Decision 94/753/CE, technical assistance and finance could be provided under the Community budget, particularly as regards aid for technical statistics, the execution of pilot studies aimed at encouraging the use of area surveys to improve national agricultural statistics, and improvements to software. During phase 2 the Spanish Ministry of Agriculture, Fisheries and Food (MAPA) and the Portuguese statistical services received assistance from the Commission in financing two projects in this sphere of activity. In particular, MAPA received a subsidy to develop a land-based panel for the early estimation of acreages and production of the main crops. The Portuguese Ministry of Agriculture received Community finance to carry out a project on area survey methodology applied to the production of statistics on land use. In both cases, the Community contributed around 40% of the total cost of the project. In connection with this activity, the JRC provided technical assistance for several European Union Member States and for countries taking part in the PHARE programme. In particular, Slovenia, Poland and the Czech Republic received technical assistance with regard to statistical methodology and sampling plans (cf. for some technical support project Activity D). The JRC also provided Mediterranean countries for the purpose of establishing regional inventories (notably Tunisia and Turkey) and arranged scientific exchanges with Morocco on this subject. 1. 3 Financial aspects Table 1 shows the EU's contribution to the two projects mentioned above. Table 1 - EU contribution to "Activity A" (ECU) Ministry of Agriculture (Spain). Ministry of Agriculture (Portugal) Total 1997 1996 60,911 65,880 60,150 121,061 65,880 1998 -- - Total 126,791 60,150 186,941 1. 4 Conclusions and policy In phase 2 of the project the "Regional inventories" activity was confined to financial and technical assistance for national statistical services requesting it, within the limit of the available resources. The regional inventories are still a rapid, objective and often more efficient system, in terms of accuracy, when specific handicaps make farm surveys more difficult or less reliable. The scientific methodology developed could be used the harmonisation of land use estimates for the whole of the European Union. in The area survey system is particularly appropriate for the PHARE countries, since it is difficult to maintain an up-to-date list of farmers, owing to the current farming reforms. This approach may however also prove useful for the whole of the European Union in meeting a growing need for information on land use, the impact of agricultural activity on the environment, the territorial dimension of agricultural policy and rural development measures, etc. Given the high cost of the images, exhaustive annual coverage in high-resolution images and use of the regression estimator method are not a viable way of producing statistics at EU level and are therefore not recommended. That could change in the future, either with the arrival of new sensors and a reduction in the price of the images, or if statistics are produced in combination with cartographic products: hence the desirability of maintaining a technological watch on methodological aspects and the introduction of new sensors, including radar. We should also make better use at European level of the potential offered by area surveys to obtain information on land use and on other statistical variables with a territorial dimension, for which traditional methods are less efficient. 2. ACTIVITY B: EUROPEAN RAPID ESTIMATES 2. 1 Objective and methods The purpose of Activity B is to provide an early, objective estimate of changes in the acreages of the main annual crops at European level by interpreting high- resolution satellite images obtained on a sample of 60 sites (40x40 km in size) distributed over the whole of the European Union. The method is based partly on estimating for each site the surface changes against the previous year and partly on extrapolating these changes at European level on the basis of the previous year's official statistics on acreages (source: Eurostat). The method does not use any ground data for the current year but incorporates the previous year's ground observations, also permitting a retrospective check on the method's validity. This activity has also supplied qualitative information during the agricultural year. 2. 2 Progress of the work In phase 1 of the MARS project, this activity was set up in three stages: • 1987-1988: finalisation of detailed methodology; • 1989-1991 : establishment of the system and entry into production; • 1992-1993: full-scale test of the activity (53 sites). During phase 2 this activity became operational. The main stages were as follows: • 1994: Transitional year pending the Council decision; no significant changes in methodology or organisation of the work. • 1995: Call for tenders for the purpose of awarding the contract for photo interpretation and acreage estimation. • 1995: First effective year of the Council decision. Establishment of the GRIPS and ORCA software by the JRC, for image correction, processing and classification respectively. Establishment of seven new sites to extend the activity to the three new Member States. implementation of • 1996: Extension of the activity to 60 sites, with 7 covering the three new Member States (Austria, Finland and Sweden). • 1996-1997: Establishment of the CAMEL software for the current situation task. • 1997: Adaptation of the GRIPS software to 1RS satellite images. • 1998: Final year of implementation of the Council decision. Early use of windows for obtaining images to enhance the efficiency of the estimates in terms of stability and earliness. 10 2. 3 Working procedures and the main developments During phase 2 of the MARS project, working procedures for Activity B were essentially no different from those in phase 1. Nevertheless, although this activity was considered operational, several significant changes were made to both the system and the tools: • Replacement of the software used up to 1995 by software developed under the supervision of the JRC, namely: GRIPS, for the correction of satellite images; ORCA, for the classification and photo-interpretation of the images; CAMEL for analysis of the results and the data processing necessary to prepare the final estimates on the basis of the classified images. GRIPS and ORCA were used throughout phase 2. The CAMEL software was used from 1997 onwards. By using these software packages it was possible to rationalise tasks and gain control over all phases of the execution of the activity, thus ensuring quality control over the whole production chain. The GRIPS software was adapted between 1997 and 1998 to the new 1RS satellite images (up to then, the only satellites used were SPOT and LANDSAT). • Introduction of 7 new sites for the three new Member States, namely three sites in Sweden, two in Finland and two in Austria. One effect of this was better representation of spring crops. • Establishment of a "radiometric knowledge base" to help the photo interpreters, taking account of the spectral signatures and climatic conditions observed in the past. This knowledge base is updated and extended each year, thus improving the system's quality. The main results of Activity B were included in the MARS bulletin (see Activity C). Between 1994 and 1998, the salient points were as follows: • 1994 The activity continued in this transitional year between the research and operational stages. It followed the same procedures as in 1993. Altogether, 155 images were obtained on 53 sites. • 1995 Altogether, 153 images were obtained, covering 53 sites. The results of the estimates were published in 7 MARS bulletins during the season. During this year the activity proceeded satisfactorily despite some specific problems encountered in validating the estimates: 11 - very large changes in the acreages of certain crops of interest, such as sunflower, partly owing to the effects of the CAP, - over-estimation of the change in the acreages of certain crops such as colza, owing to the fact that this crop is poorly represented in the sample, - the severe drought in Spain and Portugal, which made it very difficult to interpret the photographs and led to dubious estimates for a large proportion of summer crops. In the case of sunflower and colza, the error increased the smaller the number of sites containing colza and sunflower, so that estimates are greatly influenced by relocation of these crops in Europe, as happened following the CAP reform. • 1996 The number of sites processed was 59 out of 60 (there being no images for the Koping site), and the whole of the European Union was covered for the first time. Altogether, 171 images were obtained. The results were published in 7 MARS bulletins during the agricultural year. • 1997 Altogether, 183 images were obtained covering all 60 sites. The main results were included in six MARS bulletins and three intermediate reports. • 1998 Although the final results were not available by the time this report was written, it is worth mentioning that a number of changes were made to meet the need for improvements to results and management: - acquisition dates were rationalised to improve discrimination between winter and spring crops, to achieve earlier stability in cereal estimates and to identify winter colza more accurately. This was made possible by the database of sowing dates produced from ground survey data associated with Activity B. - adaptation of the GRIPS software to process the images from the Indian IRS-1C satellite, which will increase the likelihood of obtaining images on the sites on the set dates. 2. 4 The role of the ground surveys Implementation of Activity B during the second phase of the MARS project benefited from the results of the ground surveys conducted on the same sites with point sampling in a number of segments (on average, 16 segments 1400x1400 metres per site). The purpose of these surveys (known as «Action 6» during phase 1) was as follows: 12 a) b) c) d) to supply photo interpreters with ground information to establish the reference system for each site for year n-1 and to validate the results obtained via satellite data at the end of year n; to obtain direct estimates of acreages and yield forecasts for the main crops at European level, independently of remote sensing techniques; to supply acreage and yield data for the main annual crops for each site, to be used as a statistical model for extrapolating the results; to supply additional information on certain aspects which come into play in the agro-meteorological model (sowing, flowering and harvest dates, crop rotation, plans for the next year's sowings, etc. ). All the results of these surveys were stored in a database at the JRC and had a positive impact on other aspects. For example, the ground surveys were used to produce a database on spectral signatures and provide real data on sowing dates, etc. 2. 5 Use of financial resources Table 2 below gives a summary of expenditure on the operational implementation of Activity B from 1994 to 1998. It reveals that the overall cost of this activity for the EU budget during the period was ECU 11. 8 million, or an average of ECU 2. 4 million per annum, though it was closer to ECU 2. 1 million in 1997 and 1998. This decline, which is all the more marked in comparison with the start of the period if we take account of the increased number of sites, is due mainly to cost savings on image processing for 1997 and 1998 following a call for tenders put out in 1995 for the award of this contract. The increase in 1996 is due to investment in information technology in that year by the contractor selected. Table 2 - Total expenditure on "Activity BM 4 Processing Images Software maintenance Ground surveys Total 1996 1994 632,007 1995 1. 323,470 1,125,000 1,357,807 666,400 633,719 102,952 581,966 Total 5,474,658 3,247,581 427,970 2,611,619 2,510,330 2,248,638 2,709,125 2,153,473 2,140,262 11,761,828 1998 837,614 660,000 161,360 481,288 1997 830,767 655,455 163,658 503,593 489,919 554,853 Broadly speaking, the operating costs can be divided into four types: • purchase of images, • expenditure on image processing and production of estimates, • software maintenance, • ground surveys. 4 The number of images for 1998 is estimated. 13 Here is a brief description of each type of expenditure. 2. 5. 1 Images On average, 3 images per site are processed in the course of each season; in practice, all the images are obtained between the beginning of April and the end of September. If an image is to be usable, there must be very few clouds. In some parts of Europe, cloudy weather may make it impossible to obtain any images during highly interesting periods in crop development. Table 3 - Statement of images used by Activity B Year 1994 1995 1996 1997 1998* total satellite LANDSAT 18 16 12 57 40 143 SPOT 137 149 159 126 120 691 (*) Forecast TOTAL 1RS Images 155 165 17. 1 183 180 854 20 20 Costs 632007 633719 666400 655455 660000 3247581 During 1994 to 1997 a total of 674 images were obtained and processed. During those years, two image suppliers were competing to supply the most suitable images at any time: Spot Image, which has exclusive rights to the images from the European satellite, SPOT, and Eurimage, which has exclusive rights to the images from the American LANDSAT satellite. To some extent, the SPOT and LANDSAT images are interchangeable. However, in practice the only way of ensuring that an image is obtained when and where necessary is to obtain the first available image for the chosen sites in the periods selected. The possibility of programming the SPOT satellite considerably increases the likelihood of obtaining good images from that satellite for Activity B, which explains the large number of images obtained from SPOT as opposed to LANDSAT. Very recently (April 1998), GAF/Euromap - the exclusive supplier of images obtained by the Indian satellite, IRS-1C - also re-entered this market. About 180 images are to be bought in 1998 from the three suppliers, as indicated in Table 3. 2. 5. 2 Image processing and estimate production The term "processing" covers all the practical operations which take place following acquisition of the image and are necessary to prepare the estimates which the Commission expects. For a better understanding of the nature and level of expenditure on this, the sections below will give a brief description of the activities involved in "processing" the images. 2. 5. 2. 1 Project execution and management Management activities concern the organisation of the project as a whole, mobilising the necessary equipment and human resources and planning the timetable. During the execution phases, the management is responsible for the 14 operation of the production process, co-ordination between successive years and resolving problems and contingencies. 2. 5. 2. 2 Project launch This action takes place during the preparatory phase and concerns the launch of the entire production chain, i. e. the installation of the data processing equipment and commissioning of the software and the communications network. When a long-term production chain is installed (several consecutive years), expenditure on this type of activity is charged to the first year only. 2. 5. 2. 3 Preparations for the season This work can be divided into two main parts: validation and site preparation. Validation consists in evaluating the quality of the photo-interpretation results by comparing them with ground surveys conducted on each of the sites in the previous season. Site preparation consists in collating all the information on previous agricultural years; this information is preserved to improve the knowledge base on the sites (crop rotation tables, site profile, image reference system, definition of the regions, etc. ) 2. 5. 2. 4 A cquisition and preliminary processing of satellite data When the image is received, it is checked to ensure that it is acceptable (date taken, image quality, rate of cloud cover, etc. ). Next, the image undergoes a series of processes aimed at eliminating any influence due to atmospheric conditions, the satellite photography parameters, the type of satellite and the local topography. These processes may be roughly classified into two types: radiometric and geometric; they are carried out using the GRIPS software owned by the Commission. 2. 5. 2. 5 Interpretation of satellite data This fundamental phase comprises several operations which we can divide into six consecutive stages: a) data integration, if the preceding correction phase has been validated; b) automatic classification, by allocating the image to one of about thirty homogeneous classes; c) segment analysis for thematic allocation of each plot in the segment; d) preparation of the land use map by incorporating each segment in the automatic classification; e) transmission to the current situation unit; f) preservation of the results. Stages b) and c) are carried out using the ORCA software owned by the Commission. 2. 5. 2. 6 Current situation The purpose of the current situation task is to estimate the acreages of each of the 17 crops of interest at European level, every two weeks from April onwards. The estimate is based on extrapolation of the data obtained by processing a maximum of four images per site. "Current situation system" software and some other auxiliary packages are used in this phase. 15 2. 5. 2. 7 Training Training is divided into two stages. The first comprises initial training, to establish the production team. It is followed by on-the-job training to meet a need to develop the production tools and therefore to modify operating methods. 2. 5. 2. 8 Maintenance Maintenance ensures that all the data processing equipment involved in the production chain and provided by the entity responsible for image processing and estimate production is available at all times. 2. 5. 2. 9 Internal project control This activity is meant to ensure that the production chain operates efficiently at all times, that deadlines are met and that the set quality standards are maintained during the various estimate preparation phases. 2. 5. 2. 10 Reports. The main product of this activity is the supply of estimates of changes in crops of interest for the Commission. These estimates are delivered during the season and in real time, in various forms: tables by geographical level, tables by crop, reports of the current situation regarding acreages, etc. 2. 5. 2. 11 Product delivery This activity consists in providing the Commission almost instantaneously with all auxiliary data ahead of the statistical results produced by the processing chain. This information represents a vast, harmonised historical database which can be used for other research or other applications. 2. 5. 2. 12 Cost of image processing and production chain for estimates Table 4 sets out expenditure on image processing during the operational phase. NB: in 1994 and 1995 the processing concerned the 53 sites of the EU of 12, as the current 60 sites did not form part of the operational phase until 1996. 16 Table 4 - Breakdown of expenditure on "Processing" PRODUCTION Project execution and management Project launch Preparations for the season Collection and preliminary processing of satellite data Interpretation of satellite data Current situation Training Maintenance Equipment F R U J L C l l U N l R UL REPORTS PRODUCTS ISPRA EXPERT TOTAL 1996 1994 1995 1,280,170 1,081,700 1,144,841 62,474 552,919 106,082 90,000 — 123,300 90,000 — 123,300 1997 612,624 64,172 1998 621,399 66,007 109,135 106,033 49,500 49,500 32,205 33,026 33,914 388,800 76,500 — 50,400 501,670 388,800 76,500 — 50,400 303,200 218,967 92,474 31,688 48,031 64 3 61 32,895 26,000 19,516 17,300 96,194 — 1,323,470 1,125,000 1,357,807 26,000 17,300 — 225,222 108,797 231,959 111,483 72,272 «- 72,003 — 66 1 57 6 0 6 89 33,395 20,084 98,506 830,767 33,957 20,695 100,874 837,614 For the years 1994 and 1995 the Commission had used the contractual framework of the pre-operational phase, but from 1996 onwards these activities were entrusted to companies outside the Commission, selected by tendering procedures based on free competition. The higher expenditure in 1996 was due to the launch activities described earlier. 2. 5. 3 Software maintenance to the JRC Produced at the request of the Joint Research Centre, the GRIPS and ORCA software was supplied in October 1994 and August 1995 respectively. The GRIPS software is used for the radiometric and geometric rectification of the SPOT and LANDSAT images. The ORCA software is used to back up photo interpretation in Activity B. Recently, GRIPS was adapted to process IRS/LISS images incorporated in Activity B at the start of 1998. The purpose of maintenance is to ensure that these two software packages operate properly in the processing and estimate production phases. It is entrusted to a specialist firm. There are two types of maintenance: corrective and adaptive. The former is intended to correct operating anomalies either in the hardware or in the operating system. The latter covers research and the implementation of any kind of modification to the software requested by the users. ORCA also uses two software packages, CLIPS and ARKEMIES, in its development and operation; the licences are owned by external firms. Use of these two packages by ORCA entails licensing and maintenance costs. 17 Table 5 - Cost of using the GRIPS and ORCA software GRIPS software Corrective maintenance Adaptive maintenance GRIPS total ORCA software Arkemie licence Clips licence Computer configuration Corrective maintenance Maintenance of the computer configuration Adaptive maintenance Corrective maintenance (ARKEMIE and CLIPS) Total ORCA Total GRIPS and ORCA 1996 1997 1998* 13,415 8,200 21,615 23,000 20,664 43,664 23,000 48,186 71,186 18,175 10,935 32,730 19,497 55,000 55,000 3,125 3,125 23,049 52,869 9,000 9,000 81,337 119,994 90,174 102,952 163,658 161,360 2. 5. 4 Ground surveys As already stated, the images alone are not enough for reliable determination of the various types of land use. We have also mentioned the need to correct the results of the automatic image classification and the importance of establishing a well-documented historical database for the segments forming part of each site. This means that we need auxiliary information of the "ground survey" type. These surveys are conducted either by private firms or by statistical authorities of the Member States, selected on the basis of open tendering. The table below gives detailed information on the cost of ground surveys per annum and per Member State. Table 6 - Cost of ground surveys in ECU (1994-1998) Sites in: 1994* -- 14,400 35,650 -- 1995* -- 14,270 33,630 -- 75,063 99,315 21,900 (1) 74,998 20,550 107,363 Austria Benelux Denmark Finland France Germany Greece Ireland Italy Portugal Spain Sweden United Kingdom Total (1) The costs are included in those of the United Kingdom; (*) 53 sites 135,250 104,132 24,600 (1) 83,821 20,600 89,409 -- 46,991 554,853 1996 25,800 8,500 34,265 28,960 77,458 101,739 22,000 (1) 49,940 18,300 113,130 53,734 48,140 581,966 1997 22,980 14,250 23,330 27,563 65,650 78,173 20,500 13. 440 48,760 16,682 85,233 51,000 36,032 503,593 1998 ~ 28,157 10,340 25,777 30,461 56,790 78,538 18,600 11. 670 48,950 17,000 76,620 44,500 33,885 481,288 -- 42,830 489,919 2. 5. 5 Financial management Moving on to the operational phase has also meant changes to the management of Activity B. Apart from 1994, which is a transitional year between the research phase and the operational phase of the project, Activity B was financed mainly by appropriations available under heading B2-513 of the general budget («Restructuring of agricultural survey systems »). However, some of the ground surveys were conducted using appropriations available under heading B6-792 («Activities providing scientific and technical support for Community policies on a competitive basis »). During this phase, several measures were initiated to reduce the operating costs of Activity B while keeping within the limits imposed by the methodology. These efforts focused on three main areas where it was considered possible to cut costs: image purchase, processing and ground surveys. The table below shows the changes in the average cost per site during 1994 to 1998: Table 7 - Changes in the average cost per site (ECU thousands) Processing Images Ground surveys Total per site 1994 1995 1996 1997 1998 25. 0 11. 9 10. 5 47. 4 21. 2 12. 0 9. 2 42. 4 22. 6 11. 1 9. 7 43. 4 13. 8 10. 9 8. 4 33. 2 14. 0 11. 0 8,0 33. 0 As we can see, the average cost per site dropped from ECU 47,400 in 1994 to ECU 33,000 in 1998, a reduction of around one third. The cut in costs was greatest for processing (down 44. 4%) and more modest for images (down 12. 6%) and ground surveys (down 23. 8%). 2. 6 Results obtained and error analysis The results of the acreage estimates obtained by Activity B are set out in the MARS bulletin, published monthly from January to October by the Joint Research Centre. This Bulletin also contains an examination of the agro-meteorological situation and predicted yields for the main crops. This bulletin is normally prepared very quickly (within a few days of the latest images being obtained) and forwarded promptly to the Commission departments concerned, as well as to members of the Eurostat working party on "Vegetable product statistics", and to a number of experts in the various Member States. Each year, in accordance with Council Decision 94/753/EC, the Commission also submits to the Standing Committee on Agricultural Statistics a report on the implementation of that Council decision. When this report is discussed by the working party on "Vegetable product statistics", the Commission also has the opportunity to present and discuss the results of Activity B and the procedures for conducting that activity. Annexes 1 to 4 show all the results of Activity B compared to the Eurostat official statistics. The error analysis for 1994 to 1997 is outlined below. This analysis is based on a comparison of the acreage estimates for the various crops as obtained from Activity B against the Eurostat official statistics (generally available on 15 May in the following year on the CRONOS database). Although this approach is not, strictly speaking, a genuine validation of the results of Activity B, since even the official statistics are not error-free, it is nevertheless useful to have an 19 idea of the discrepancies between the two sources and hence the margin of error in the acreage estimates produced by Activity B. The table below gives a general view of the average percentage deviations between the monthly estimates obtained by Activity B and the official figures for 1994 to 1997 (the figures for 1998 were not yet available when this report was written). Table 8 - Average monthly deviation of Activity B between 1994 and 1997 (%) APRIL MAY 1. 2 1. 1 1. 6 Cereals Wheat Common wheat Durum wheat Barley Maize Colza Sunflower Rice Beet 1. 0 0. 9 1. 0 30 2. 7 3. 8 8. 3 16. 0 9. 4 5. 7 JUNE 1. 4 1. 8 2. 7 3. 6 3. 1 4. 1 12. 5 7. 3 11. 7 4. 0 JULY AUG. SEPT. OCT. Average 1. 1 1. 5 1. 8 1. 1 1. 8 1. 9 1. 0 1. 6 1. 7 1. 2 1. 8 1. 8 1. 0 1. 6 1. 7 3. 9 2. 4 4. 9 14. 4 6. 9 9. 3 3. 8 3. 9 2. 4 4. 9 13. 5 6. 7 9. 2 5. 6 3. 9 2. 2 4. 9 12. 3 6. 3 7. 9 4. 9 3. 8 2. 2 4. 7 12. 3 6. 9 7. 9 4. 6 3. 7 2. 6 4. 3 12. 1 8. 7 9. 7 5. 0 4. 0 2. 9 2. 7 11. 7 10. 8 12. 2 6. 3 The last column in Table 8 shows the average error over the whole period for the main crops. For cereals as a whole, we find that the average error is 1. 1% but it increases to 1. 8% in the case of common wheat, 2. 6% for barley, 3. 7% for durum wheat and 4. 3% for maize. The error is even greater for sunflower (averaging 8. 7%) and colza (12. 1%), and is also very marked in the case of beet (5. 0%) and crops which are poorly represented at the sites such as rice (average 9. 7%). Moreover, we must not lose sight of the fact that, even though the margin of error is smaller for cereals than for other crops in percentage terms, a 1. 1% error nevertheless represents an area of around 400,000 hectares, roughly equivalent to 2. 5 million tonnes of cereals. Even this margin of error may be considered excessive, given the requirements concerning the management of this market. Over the years there has admittedly been a slight improvement in the margin of error for winter crops. However, the estimation error is still too high for spring crops and oilseeds. 2. 7 Evaluation of the action The report on Activity B at the end of phase 2 of the MARS project presents both strengths and weaknesses. The strengths may be summarised as follows. • It is technically possible, via remote sensing, to obtain rapid estimates of changes in the acreage of the main crops at European level. This can be done by monitoring a relatively small sample of sites, at least for the main cereal crops. • In terms of introducing technological innovation and setting up more modern systems for collecting agricultural statistics, the MARS project and Activity B in particular are certainly a success. Apart from the remote sensing applications mentioned above, which were developed to meet the needs of the Commission, there are currently many applications, not only in several EU Member States but 20 also in some East European countries which are aiming to improve national statistical systems by using the lessons learnt from the MARS project. • The whole technical chain of Activity B, data acquisition and analysis, in relation to operational production systems and results, is also a success in so far as it has made the information sought available within a very short time after acquisition of the basic data (satellite images). • The establishment of Activity B as part of the MARS project has generated other applications outside agricultural statistics, such as those relating to supervision of the acreages declared by farmers under the CAP, and has shown that new applications are also possible, e. g. in monitoring the condition of forests or the agri-environmental situation. In addition to these strengths, the MARS project and more particularly Activity B are still encountering certain limits to the application of remote sensing to agricultural statistics as regards estimating acreages. The weaknesses may be summarised as follows: • The sample used does not yield reliable estimates at national level, even for the largest countries. • Although Activity B permits a correct overall assessment of the change in cereal acreages as a whole at an earlier date than the official statistics, it performs less well in the case of more specific crops (beet, maize, colza, etc. ). This is partly because the sample used offers a less satisfactory level of representation for individual crops, and partly because in certain cases the different crops are difficult to separate. Furthermore, remote sensing cannot identify the destination of the product or the purpose for which it is grown: common wheat or durum wheat, grain maize or fodder maize, food or non-food colza, subsidised fallow land (set aside) or land lying fallow under a system of crop rotation, etc. • While the initial acreage estimates supplied by Activity B, i. e. those available at the beginning of May or June, are generally earlier than the official statistics, they have not yet been stabilised and are not very reliable, given the small number of images and sites on which they are based and the greater difficulty in separating the different crops at this stage. It is not until the end of June that the information becomes relatively stable. Nevertheless, at that stage the spring crop acreages are still not easy to record by remote sensing. • The images are still expensive, despite some savings made in recent years, and it has not been possible to achieve any significant reduction in the cost except for Eastern Europe, where there is less demand for satellite images. This constraint, together with the conflicts between programming and acquisition, considerably restricts any scope for increasing the number of sites in order to improve the representation of crops of interest and thus reduce the margin of error in the estimates. • Despite the adaptations made over the years (e. g. , better integration of ground surveys and remote sensing estimates), the system suffers from excessive rigidity 21 in the face of the possible changes in the location of crops and the innovations which one would like to introduce (for instance, Activity B did not detect the relocation of colza production in 1994 following the CAP reform). 2. 8 Conclusions In the light of the foregoing, it does not seem desirable simply to renew Activity B of the MARS project using the same methods and actions as in phase 2. Despite the improvements made in recent years, with the scientific and financial resources currently available, the acreage estimates produced by remote sensing with the methodology hitherto applied are not entirely satisfactory, either in terms of lead times or as regards reliability, particularly at the level of the specific crops of interest to those administering the Common Agricultural Policy. Representation could certainly be improved by increasing the number of sites, but the costs would then outweigh the benefits (e. g. the total cost of Activity B for 100 sites is around ECU 5 million in the first year and almost ECU 4 million once it is up and running. It is uncertain whether 100 sites would be sufficient to achieve satisfactory representation). Finally under current conditions, the scope for reducing the cost of images and ground surveys seems relatively limited. However, it does not seem desirable to abandon Activity B, particularly on account of the following: • • • • the Commission has devoted substantial financial resources to this subject in order to gain a unique technological lead in this field at both European and world level; the applications may not yet have been fully exploited; the needs which gave rise to the project (to provide an independent means of producing early estimates of areas and yields) still exist; to abandon Activity B would probably jeopardise other potential applications in different areas of agricultural statistics (e. g. , monitoring the condition of forests, agri-environmental indicators, etc. ); the JRC ought to maintain a technically skilled team to provide technical support for the Commission, or for isolated analyses which might be needed (e. g. analysis of the impact of drought on harvests, etc. ). Taking account of these various points, the JRC should be instructed to examine alternative methods or significant improvements to the current approach, with the aim of increasing the reliability and lead times of the area estimates while keeping within the bounds of the current financial constraints. Work on this subject began in 1997 and is intended to continue after 1998. It covers various alternative methodological approaches including major modifications to the conventional approach to remote sensing, new sampling methods, etc. This research needs to continue for several years before we can move on, if appropriate, to a new operational phase. If this work leads to alternatives to the current method which 22 prove satisfactory in terms of data reliability, lead times and costs, we should ultimately consider funding them out of the Commission's operating appropriations. 23 PART II - Activities for development 3. ACTIVITY C: THE ADVANCED AGRICULTURAL INFORMATION SYSTEM Activity C is divided into 4 sub-objectives: • C. l: to complete current development concerning the establishment of vegetation condition indices. • C. 2: to set up yield prediction models (transition to the operational stage of agro- meteorological models). • C. 3: to create an integrated agricultural information system at European Union level by combining the information obtained from Activity B and Activities C. l and C. 2. • C. 4: to examine how and under what conditions an integrated system can be set up at national and regional level. Below is a brief description of the work done during phase 2 of the MARS project on these various activities. 3. 1 C. l Vegetation condition indices Continuing the work of phase 1, this action aimed to set up an operational chain to process and store the daily images of the NOAA-AVHRR meteorological satellite and to extract data on the condition of vegetation. The phase 2 work focused mainly on the following aspects: Setting up a receiving station at the JRC (task completed). Installing hardware capable of processing more than one image at a time (task completed). Parametric maintenance of the SPACE software processing (permanent task). for preliminary image Installing the SPACE system on PC (task completed). Improving filtering and synthesis techniques for the NDVI (Normalised Difference Vegetation Index)^ (task completed but implementation in progress). Improving the derived thematic cartography for qualitative use of the NDVI in analysis of the current situation (task completed). 5 This index indicates the photosynthesis activity of plants. 24 Developing numerical statistical models for the yield forecasts derived from the NDVI (task in its final test and validation year). Developing a model to estimate biomass on the basis of the NDVI (study completed and now being applied). The production chain provides a cartographic view of the main factors disrupting the farming year: drought, delayed or early plant development, etc. Future policy On the basis of the results and validations during 1998 it will be possible to assess the extent to which this sub-objective can be considered operational. The same approach will be applied in future to the VEGETATION sensor launched in March 1998. The advantage of this European sensor is that it permits easier parametric management of the images to extract the vegetation indices. The sensor was in fact designed from the start for the observation and monitoring of small-scale vegetation, and not for meteorological observations, as was the NOAA-AVHRR. 3. 2 C. 2 Yield prediction models Yield prediction is one of the key objectives assigned to the project. This particularly concerns the yields of the main annual crops, but also those of the main perennial crops such as vines and olives. The yield prediction models developed are based on the agro-meteorological simulation models incorporated in the CGMS (Crop Growth Monitoring System) designed, applied and improved by the JRC during phases 1 and 2. To develop the model it was necessary to establish a data base on soils, climate, plants and yields. This data base was updated year by year and extended during phase 2. As planned in phase 1, the yield predictions are obtained from a statistical correlation between the historical yield series and the historical series of quantitative parameters obtained from the following sub-models: • A potential évapotranspiration sub-model. • A global radiation sub-model. • A sub-model for the spatial interpolation of meteorological data. • A sub-model for usable water reserves for each crop. • A sub-model for the physiological growth of crops. Each of these sub-models and the associated database were improved by specific research and actions, in particular: 25 3. 2. 1 Meteorological data As in phase 1, historical meteorological data were acquired over a fairly long period of 25 to 30 years. The data collected are those normally available on the GTS network (Global Telecommunications System) and used as input data for the models or sub-models. These are daily data which concern more particularly: rainfall, temperature (maximum, minimum ), water vapour pressure or relative humidity, average wind speed, hours of sunshine or cloud cover. In phase 2 the number of stations with historical records was doubled to around 700 (in the extended area of the European continent and the Maghreb region, see Activity D). Other improvements made were as follows: Improvements to the network of meteorological stations used (700 in phase 1, of which 350 had historical records and 350 only current data, 2000 in phase 2 including 700 with historical records). (Task completed). Improvement in data interpolation techniques (study completed and in experimental phase). Thematic improvement in derived products such as the production of maps showing meteorological extremes, the risk of frost (on going task). Improvement in the algorithms for calculating parameters such as global radiation (study completed and in experimental phase). Improvements to the presentation of results incorporated in MARS Bulletins (Activity C. 3). 3. 2. 2 Pedological data and agronomic parameters: The final data used in the statistical models and in connection with Activity C. 3 are as follows: abnormal meteorological conditions, development indices for the ten-day period and since the beginning of the vegetation cycle, namely:. biomass; seed production; water reserves; phenological stage; water balance; excess water; leaf index. 26 The main crops of interest are: wheat, barley, seed maize, colza, sunflower, sugar beet, potatoes, peas and protein beans. The principal improvements made in phase 2 are as follows: Introduction of information from the soil map of the European Soil Bureau (JRC), (task completed on the EU of 15). Maintenance of the agronomic parameters relating to each crop (sowing date, harvest, phenological dates, etc. ). (Task in progress). Thematic extension of the agro-meteorological model to include vines and olives. This gave rise to a separate project known as OLIWIN 1 and 2. The results were incorporated in the MARS bulletin in 1997 and 1998. Validation of the results will follow in 1998. Improvements to the presentation of the results incorporated in the MARS Bulletins (Activity C. 3). 3. 2. 3 Yield prediction: Improvement and validation of statistical models for the calibration of yield data obtained from the model (task still in progress in 1998). An initial study on the accuracy of the results was published by the JRC. 3. 2. 4 Information technology: Improvement in access to data and interfaces with users. Installation on PC of the CGMS Model. The Support Group for Agrometeorology (SuGrAm), set up in 1990, made it the possible to validate the network of meteorological stations and interpolation method, as well as the sub-models for calculating potential évapotranspiration and global radiation. This group is co-ordinated by the World Meteorological Organisation. During phase 2, this group formed an essential scientific reference point for continued improvements to the CGMS and validation of all kinds of associated results. 3. 3 C. 3 Integrated agricultural information system and MARS bulletin All data relating to the monitoring of vegetation, growth simulation, monitoring of meteorological extremes and auxiliary data (timetable of field work, knowledge base concerning crops, etc. ) available under the MARS project are combined in a "current situation" analysis to establish the expected yields for the main crops at national and European level. The current situation task is carried out by the JRC at the end of each month during the farming season. 27 On the basis of the current situation analysis, the numerical indications obtained from the various statistical models are validated and published in the MARS bulletin, which represents the end product of this activity. The acreage estimates obtained by Activity B are also incorporated in the MARS bulletin, as supplied by the external contractor responsible for this activity. The exchange of information between Activity B and Activities C. l and C. 2 has proved useful in explaining the results obtained. Since 1994, 31 MARS bulletins have been published and 6 (plus a fax version) are planned for 1998. The print run averages 300 copies. They are circulated to the Commission's Directorate General for Agriculture (DG VI) and the other interested DGs, Eurostat, the Member States (representatives of delegations in the Eurostat Working Party on "Plant product statistics") and to other international bodies concerned such as the FAO. The following are some of the improvements made in recent years: In 1997 the bulletin included the results of the OLIWIN project, i. e. the results of the analysis of olives and vines. From the end of 1996 the results of Activity D (prediction of harvests outside the Community) were also included in the bulletin. With these data it was possible to produce initial experimental predictions for wheat yields in central and east European countries (PHARE and TACIS countries), Turkey, and the Maghreb countries (see Activity D). An internal report was produced which examined the error in analysis of the current situation and the various associated statistical models. The results show that the forecasts are earlier and the error is smaller than for other available sources. In 1998, extension of the model to include countries bordering on the EU of 15 (countries covered by the PHARE and TACIS programme) and the Maghreb countries will be completed with the introduction of other crops of interest. Table 9 -1997 results for the EU of 15 April May June July Aug. Sept. Oct. Eurostat* Cereals Estimated yield % deviation Total wheat Estimated yield % deviation Common wheat Estimated yield % deviation Dunimwhca. Estimated yield 5. 43 0. 64 5. 71 3. 50 6. 4 2. 70 2. 7 5. 44 0. 79 5. 7 3. 20 6. 4 3. 10 2. 5 % deviation 18. 90 11. 30 Barley Estimated yield % deviation Seed maize Estimated yield % deviation * Final figures in May 1998 4. 4 0. 30 8. 4 -6. 1 4. 5 2. 50 8. 4 -6. 1 5. 46 1. 24 5. 72 3. 60 6. 5 4. 00 2. 4 6. 20 4. 5 2. 60 8. 5 -5. 2 5. 41 0. 33 5. 69 3. 10 6. 4 3. 30 2. 4 7. 90 4. 5 1. 60 8. 5 -5. 5 5. 42 0. 51 5. 63 2. 00 6. 4 2. 30 2. 4 4. 70 4. 5 1. 50 8. 6 -3. 5 5. 42 5. 41 5. 39 0. 39 5. 63 2. 00 6. 4 2. 30 2. 4 4. 70 4. 5 1. 60 8. 6 -4. 2 0. 36 5. 63 2. 00 6. 4 2. 30 2. 4 4. 70 4. 5 1. 60 8. 5 -4. 5 5. 52 6. 24 2. 26 4. 41 8. 95 28 3. 4 C. 4 Integrated system at national and regional level Following various contacts with a number of EU Member States, the systems available under the MARS project were transferred and adapted to the real situation in the various regions. Under the Hydre project and its continuation, Pop-SICILY, the agro-meteorological system was transferred to the local authorities and adapted for the monitoring and statement of regional water resources. The regions taking part were Sicily and Sardinia in Italy, Languedoc-Roussillon in France and Andalucia in Spain. Via co-operation on request, the agro-meteorological system was adapted and transferred to the Finnish statistical service. 29 4. ACTIVITY D: MONITORING AND PREDICTION OF HARVESTS OUTSIDE THE COMMUNITY The purpose of Activity D was to perfect and extend methods developed by the other MARS project activities to countries bordering on the EU, particularly central and east European countries and the Maghreb region, in order to predict harvests in those countries. The main developments in this field were as follows: In 1996 a first bulletin was published which, by way of experiment, included the first extensions of the CGMS model, now known as System D. This bulletin referred to the agro-meteorological aspects of wheat growing in the countries mentioned. In 1997 the analysis was incorporated in Activity C. 3 and the first yield and production forecasts were supplied for 12 countries outside the EU of 15, including Russia, Ukraine, the PHARE programme countries and the Maghreb region, plus other qualitative information on harvest trends in 14 other countries. In 1998 the results will be extended to other crops of interest such as barley and maize. 30 5. ACTIVITY E: APPLICATION OF NEW METHODS OR SENSORS 5. 1 Pilot project for uses of microwave images in estimating acreages In 1996 a pilot project was launched on the incorporation of microwave (radar) images obtained from the ERS 1 and 2/SAR satellites into the system of quick estimates of agricultural acreages. The advantage of using radar is that images can be obtained even in cloudy conditions. Also the information obtained is related to the geometry and ruggedness of the targets rather than the radiometric content, as in the case of optical sensors. This offers the advantage that the images can be correlated with crop development sooner than with optical sensors. Research on the use of radar has led to a pilot application spread over two years: • 1996: a larger-scale feasibility study to introduce radar images recorded by the European satellite ERS-2 was completed. The application simulated an Activity B at a number of sites in northern Europe. In view of the encouraging results, it was possible to move on to the next phase. • 1997: the application was extended to all 60 sites of Activity B. The project aimed to improve the lead times and accuracy of agricultural acreage estimates by synergy between optical/infra-red data obtained during Activity B of the MARS project and the ERS/SAR radar data. In the project, almost 300 radar images were obtained at the 60 Activity B sites during the winter of 1996 and the spring of 1997. Acreage estimates were obtained for the main groups of crops as early as January. The acreage estimates derived by ERS/SAR radar for the 1997 seasons were compared with those of Activity B (which uses only optical images) and the results of ground surveys. The results of the two pilot studies showed that the ERS/SAR data for the autumn, winter and spring can produce useful information for estimating acreages of crops sown. However, the application gives better results in areas composed of large fields with greater uniformity in terms of farming practices and soil texture and structure. In more disparate areas, the results have to be integrated with auxiliary information on the variables mentioned. Additional research has shown that, by the combined use of optical data and ERS/SAR radar, it is possible to complete chronological optical image series which are incomplete owing to cloud problems. Future policy New sensors with different characteristics will be available in the near future. These could create scope for improving the MARS project methods. The use of all-weather radar, sensitive to geometric variations in the terrain, could bring the acreage estimates forward to before the spring. A pilot project using ERS/SAR radar has also shown the potential effectiveness of using remote sensing to speed up winter estimates. 31 The new very high resolution sensors, available within the next few years, should subsequently enhance the value of the methodology by reducing the uncertainty of discrimination between certain crops. 5. 2 Re-engineering of Activity B (REACTS) In 1997 a project for examining all possible improvements to the current methods of applying remote sensing to agricultural statistics (e. g. improvement in classification methods) was launched. This project was dubbed REACT_B (i. e. re-engineering of Activity B). The REACTB project was launched in January 1997 with a list of users' requirements. These requirements are the benchmark for analysing the performance of the current approach. The plan of work provides for updating the whole chain of Activity B: image acquisition, image processing, image interpretation and classification. However, the accent will be on sampling in conjunction with the use of satellite mini-images rather than entire scenes, and on liaison with other MARS project activities. A statistical study has shown that it is feasible to use sites smaller than the current ones. However, operational use has yet to be demonstrated. An increase in the number of sites should make the sample more representative as regards crops of interest and improve the estimate accuracy. The two aspects, « representativity » and « accuracy » are critical for the needs of users. The various sampling scenarios are now being evaluated with the image suppliers to check the technical feasibility of such an approach. A large-scale exercise will produce data for one EU region (the Bavarian site, NUTS II). For this test zone, all ancillary data have been acquired and incorporated in a geographical information system. Images produced by operational sensors (SPOT, LANDSAT, IRS-IC and ERS/SAR) have been obtained for comparative analysis of the performance of the various classifications. The tests are based on comparison with the results of a ground survey covering the whole zone. On the basis of this feasibility study and the data obtained, the techniques and methods used will be improved as part of a detailed evaluation in 1998. Operational tests in parallel with Activity B will be conducted at the sites concerned. The RADARSAT and SPOT 4 satellites, and possibly the RESOURS satellite, will provide other information of use to the study. 32 One very important element of the project is the evaluation of the cost/benefit ratio, which will have to be acceptable before any operational system is implemented. 5. 3 The aeropalynological model This research activity was completed in 1997. The purpose of this project was to supply grape harvest predictions (or wine production predictions) based on the conditions during flowering of the vines. These conditions were monitored in terms of quality and quantity of plant fertilisation using a pollen sensing method. By using a reliable and reproducible technique for sampling and analysing atmospheric pollen flows it is possible to produce early harvest predictions about three months before the grapes are picked. However, it is necessary to obtain an expert agronomist's opinion to validate the laboratory results used. The proposed method can be used to produce valid predictions both for the zones sampled and at national and Community level. At local level, the method provides a totally satisfactory answer in around 50% of situations. The effect of post-flowering conditions, which can greatly modify the potential present at the time of flowering, is the main cause of the deviations found. It is therefore essential for the first early predictions obtained by pollen analysis using agro-meteorological models to be updated during the season. But there are also - often clearly identified - profiles in which this method cannot be used. On a national scale, the results are considered satisfactory where an adequate network of pollen sensing posts is established. From the pattern of the pollen emission curve it is also possible to identify precisely the dates of the flowering period and, via an overall assessment of phenological homogeneity in the zone sampled, to arrive at an initial approximation of the vintage's potential. The activity was conducted with the support of CEMAGREF, Montpellier University and various national agencies in the countries considered. In conclusion, these methods can now be considered operational, but they can continue only if they are taken over by national operational entities. The JRC has arranged this transfer, but success will depend on the presence of a body which is both technically competent and able to afford the cost of the operation. 33 6. CONCLUSIONS CONCERNING THE ACTIVITIES BEING DEVELOPED After two five-year cycles of research, significant results have been obtained: • The agro-meteorological part (low-resolution model and satellite data) can be considered satisfactory and operational, even though there is still scope for improvement. The results are available at European, national and regional level. • A number of limitations in Activity B, particularly representativity at national level and for certain products, seem capable of being overcome by the use of new sensors and new sampling methods, but their operational use has yet to be demonstrated. • Some methods, such as the aeropalynological model, can be considered only if they are taken on by national authorities. 34 MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS IN RELATION TO EUROSTAT (1994): EUR-12 ANNEX 1 1994 APRI L MAY JUNE - -. *">,. Ctri^r^^t^&l' TM<mJte*j$BbL- WOO ha 32371,9 478,7 15418,9 137,4 Common wnea|^«#Hj«U- ; 12408,7 164,0 3014,2 % 0,3 1000 ha v 32630,1 % 1,1 1000 ha l 32759,2 % 1,5 1,5 1,5 0,9 0,9 1,3 4,3 736,9 15531,2 249,8 12519,4 274,7 3017,1 2,3 23 1,6 1,8 2,2 4,4 866,0 15514,1 232,7 125563 311,6 2962,2 2,7 2,1 1,5 2,1 2,5 2,5 Durum wheaf **^«§fliii;u ,,,* Vtfwtat , •vFit'lv^^^k^' ^&^ra^^BpR"^ < •'^^^4 j'^^p&'i Cofia * ^' ^JjSBwSi' r "*'\ dcviat. cstim. Sunflower -22,5 -0,74 -19,6 -0,6 -74,5 -2,5 10182,0 469,5 3746,9 74,9 2374,2 -118,7 3310. 7 403,4 -0,1 4,8 -0,5 2,0 9,5 -4,8 2,0 13,9 10222,7 510,3 3618,9 -53,1 2341,6 -151,2 3180,9 273,6 0,3 5,3 -3,9 -1,4 8,0 -6,1 -2,0 9,4 10253,3 540,9 3709,2 37,3 2283,1 -209,7 0,6 5,6 -1,5 1,0 53 -8,4 2771,9 -14,6 -135,4 -4,7 JULY lOOOha^r] 32436,4 -'i"SSSB' ]-J0MHd^. 32436,4 \W;l 0,5 y^A'*,; 0,5 , '. - SEPT ii#:! 0,4 '#jri§. ha\y$. 32404,1 OCT, IpOQ^ha^ 32404,1 Zz. %^ 0,4 Eurostat. •* ::f||| -1,18 -MX:< 31893,20 Eurostat (000 ha) Aver, dev iation fe'199? 32275,03 WOO ha % 543,2 15414,8 133,4 12470,2 225,5 2947,7 -89,0 10141,2 428,8 3615,1 -56,9 2333,0 -159,9 24863 -421,0 1,7 1,5 0,9 1,4 1,8 2,0 -2,9 -0,5 4,4 -4,0 -1,5 7,6 -6,4 -23,4 -14,5 543,2 1,7 510,9 15417,6 136,2 12470,2 225,5 2950,6 1,5 0,9 1,4 1,8 2,1 -86,1 -2,83 10141,2 428,8 3615,1 -56,9 2335,1 -157,7 -0,5 4,4 -4,0 -1,5 7,7 -6,3 15408,3 126,9 12457,9 213,2 2953,5 -83,2 10141,2 428,8 3554,8 -117,1 2335,1 -157,7 1,6 1,5 0,8 13 1,7 2,2 -2,7 -0,5 4,4 -5,6 -3,2 7,7 -6,3 510,9 15420,5 139,0 12470,2 225,5 2953,5 -83,2 10141,2 428,8 3562,4 -109,6 2333,0 -159,9 1,6 1,5 0,9 1,4 1,8 2,2 -2,7 -0,5 4,4 -5,4 -3,0 7,6 -6,4 0,62 15281,42 15187,98 -0,43 12244,69 12298,03 5,08 3036,73 2889,95 -4,71 9712,43 10192,17 -2,49 3671,99 3765,72 14,97 2492,82 2168,18 2483,0 -23,5 2512,2 -22,6 2502,5 -22,9 -10,43 2907,27 3245,80 -424,2 -14,6 -395,0 -13,6 -404,8 -13,9 613,44 1,92 172,05 1,13 238,29 1,95 7133 235 464,28 4,78 77,49 2,11 161,14 6,46 365,09 12,56 estim. = areas (x 1000 ha) and % change in relation to previous year estimated by Activity B deviat. = difference between the Activity B estimate and the Eurostat official figures % = change in area in relation to previous year EUROSTAT final figures in May 1998 (x 1000 ha) 35 MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS IN RELATION TO EUROSTAT (1995): EUR-12 ANNEX 2 1995 APRIL MAY JUNE JULY AUG. OCT. Eurostat WOO ha % WOO ha % 1000 ha 32499,2 1,9 324673 1,8 32307,8 % 13 WOOha 32275,9 -430,8 -1,3 -462,7 15569,5 1,9 15569,5 CereaJi. "< w. „;esrtin. ;. •:'^^^^^^^^L ™flw'?!?8S!^. • r^^^^^^^'Cl^^^'. ' commonincites cstim; Durum wheat, '. '%'; Barley. ",; lestim. tfeviat:. Vesftin. M a t M ' ^ ^ l ^ n ^v cram. Sunflower-. \estim» • ;^^^^^#M<- 1000 ha 32850,0 111,4 15780,4 % 3,0 0,3 33 -239,4 15712,0 -160,1 -1,0 -228,6 12820,2 16,3 4,7 0,1 2957,8 -2,6 -179,0 -5,71 9935,8 127,0 3532,4 -115,3 2532,7 -43,7 2840,4 366,4 23 1,3 -3,8 -3,2 1,6 -1,7 - 23 14,8 12710,0 -93,9 30003 -136,4 9731,9 -76,9 36683 20,6 3076,1 499,7 2886,9 412,9 -0,7 2,8 -1,4 3,8 -0,7 -1,2 -4,4 0,2 -0,8 -0,1 0,6 23,4 19,4 -0,7 16,7 -271,3 15767,6 -173,0 12783,5 -20,4 2982,1 -154,7 9741,6 -67,2 3642,6 -5,1 3116,0 539,6 2831,7 357,7 -0,8 3,2 -1,1 4,4 -0,2 -1,8 -4,9 03 -0,7 -0,8 -0,1 25,0 20,9 -2,6 14,5 % 1,2 -1,4 1,9 -2,3 3,3 -1,2 -3,9 -371,1 12648,8 -155,1 29183 -218,4 -6,96 9819,3 10,5 3539,8 -107,9 3190,8 614,4 1,1 0,1 -3,6 -3,0 28,0 23,8 SEPT. lOOOha % 1,4 32339,7 -398,9 15618,6 -322,0 12697,7 -106,1 29183 -218,4 9838,7 29,9 3539,8 -107,9 3138,5 562,0 -1,2 2,2 -2,0 3,7 -0,8 -3,9 -7,0 13 0,3 -3,6 -3,0 25,9 21,8 1000 ha 32339,7 -398,9 156093 -331,2 12685,5 -118,4 29213 -215,4 9838,7 29,9 3569,2 -78,6 3141,0 564,5 % 1,4 -1,2 2,1 -2,1 3,6 -0,9 -3,8 -6,9 13 0,3 -2,8 -2,2 26,0 21,9 -371,1 12648,8 -155,1 29183 -218,4 9858,1 49,3 3580,2 -67,6 3230,7 654,3 -2,3 3,3 -1,2 -3,9 -7,0 1,5 0,5 -2,5 -1,9 29,6 25,4 Eurostat (000 ha) 1994 1995 31893,20 32738,60 Aver, deviation 1000 ha % % 2,65 431 15940,59 15281,42 4,57 12803,86 12244,69 3,29 3136,74 3036,73 0,99 9808,79 9712,43 -0,66 3647,74 3671,99 3 35 2576,43 2492,82 350,76 1,07 291,95 1,83 108,81 0,85 194,25 6,19 66,45 0,68 82,62 2,26 532,23 20,66 261,24 10,56 2427,6 -16,5 2386,9 -17,9 2375,2 -183 2314,2 -20,4 -14,90 2474,00 2907,27 -46,4 -1,9 -87,1 -3,5 -98,8 -4,0 -159,8 -6,5 estim. = areas (x 1000 ha) and % change in relation to previous year estimated by Activity B deviat. = difference between the Activity B estimate and the Eurostat official figures % = change in area in relation to previous year EUROSTAT final figures in May 1998 (x 1000 ha) 36 MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS IN RELATION TO EUROSTAT (1996): EUR-15 ANNEX 3 1996 Cereals Total wheat estlih. deviat. estim. deviat. i '•', estim. Common wheat , -. vfij,? : deviat. Durum wheat Barley Maize '. \ '-" : Colza Sunflower esnm. deviat. estim. deviat. estim. deviat. estim. deviat. estim. deviat. APRIL MAY JUNE JULY AUG. SEPT. OCT. Eurostat Eurostat (000 ha) 1000 ha 36606,8 -332,1 16950,9 -11,1 13747,7 -14,1 3202,9 % 2,9 -0,9 2,4 -0,1 2,5 -0,1 1,8 2,4 0,07 11678,9 236,0 3937,3 -236,5 2914,0 296,1 2955,5 510,9 6,1 2,1 4,5 -5,7 2,1 11,3 18,1 20,9 1000 ha 37033,7 94,8 17026,0 64,0 14029,3 267,6 2992,1 -208,4 11502,8 59,8 4016,4 -157,4 2814,1 196,2 % 4,1 0,3 2,8 0,4 4,6 1,9 -4,9 -6,5 4,5 0,5 6,6 -3,8 -1,4 7,5 2214,8 -11,5 -229,9 -9,4 JOOOha 36998,1 59,2 17471,0 509,1 14404,9 643,1 3061,3 -139,2 11106,6 -336,4 3877,0 -296,8 2822,6 204,8 2447,5 2,9 % 4,0 0,2 5,5 3,0 7,4 4,7 -2,7 -4,3 0,9 -2,9 2,9 -7,1 -1,1 7,8 -2,2 0,1 WOO ha 36998,1 % 4,0 1000ha. 36962,5 59,2 17352,7 390,7 14163,5 401,7 3187,1 -13,4 11282,7 -160,3 3813,0 -360,9 2865,4 247,6 2445,0 0,4 0,2 4,8 2,3 5,6 2,9 1,3 -0,4 2,5 -1,4 1,2 -8,6 0,4 9,5 -2,3 0,0 23,7 17354,7 392,7 14150,0 388,3 3202,9 2,4 11293,7 -149,3 3828,0 -345,8 2842,6 224,8 2430,0 -14,6 % 3,9 0,1 4,8 2,3 5,5 2,8 1,8 0,07 2,6 -1,3 1,6 -8,3 -0,4 8,6 -2,9 -0,6 1000 ha 37069,2 130,4 173443 382,4 14136,6 374,9 3206,0 5,5 11304,7 -138,3 3843,1 -330,7 2811,2 193,4 2425,0 -19,6 % 4,2 0,4 4,7 2,3 5,4 2,7 1,9 0,2 2,7 -1,2 2,0 -7,9 -1,5 7,4 -3,1 -0,8 WOO ha 37069,2 130,4 17330,9 368,9 14123,2 361,4 3206,0 5,5 11293,7 -149,3 3850,6 -323,2 2799,8 182,0 2425,0 -19,6 % 4,2 0,4 4,7 2,2 5,3 2,6 1,9 0,2 2,6 -1,3 2,2 -7,7 -1,9 7,0 -3,1 -0,8 % 3,83 1996 36938,86 1995 35575,08 2,44 16961,96 16558,59 2,61 13761,77 13412,36 1,72 3200,49 3146,24 3,96 11442,99 11007,49 10,78 4173,80 3767,74 -8,28 2617,83 2854,02 -2,32 2444,61 2502,55 Aver, deviation % WOO ha 151,59 0,41 348,98 2,06 390,72 2,84 94,92 2,97 193,41 1,69 300,57 7,20 223,77 8,55 212,08 8,68 estim. = areas (x 1000 ha) and % change in relation to previous year estimated by Activity B deviat. = difference between the Activity B estimate and the Eurostat official figures % = change in area in relation to previous year EUROSTAT final figures in May 1998 (x 1000 ha) 37 ANNEX 4 MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS IN RELATION TO EUROSTAT (1997): EUR-15 APRIL MAY JUNE JULY AUG. SEPT. OCT. Eurostat Eurostat (000 ha) A v e r, d e v i a t i on 1997 Cereals Total wheat 1000 ha 37736,7 -334,5 17446,6 204,5 estim deviat. estim. deviat. Common wheat estim. 14215,9 Durum wheat Barley Maize Colza Sunflower deviat. estim. deviat. estim. deviat. estim. deviat. estim. deviat estim. deviat. 195,8 3230,3 8,3 0,26 11739,4 -169,0 4272,7 -87,2 2478,0 -304,3 2755,1 421,3 2,6 -1,4 2,4 -2,0 -5,3 -10,9 12,7 18,1 % 2,2 -0,9 2,9 1,2 3,3 1,4 0,9 1000 ha 38220,6 149,4 17296,3 54,2 14059,0 38,9 3237,3 15,3 12237,1 328,7 4233,9 -126,0 2606,0 -176,3 2592,8 259,0 % 3,5 1000 ha 38209,6 0,4 2,0 0,3 2,2 0,3 1,2 0,5 6,9 2,8 1,4 -2,9 -0,5 -6,3 6,1 11,1 138,3 17334,6 92,6 14101,7 81,6 3232,8 10,9 12054,0 145,6 4280,2 -79,7 2696,4 -86,0 2500,6 166,9 % 3,4 0,4 2,2 0,5 2,5 0,6 1,0 0,3 5,3 1,2 2,6 -1,8 3,0 -3,1 2,3 7,1 1000 ha 38294,5 % 3,7 WOO ha 38298,2 % 3,7 1000 ha ; 38039,6 % 3,0 WOO ha 37991,6 223,3 173293 87,2 14105,8 85,7 3223,2 1,3 12119,3 210,9 4337,8 -22,1 2696,1 -86,3 2447,1 113,3 0,6 2,2 0,5 2,5 0,6 0,7 0,0 5,9 1,8 3,9 -0,5 3,0 -3,1 0,1 4,9 227,0 17320,3 78,2 14076,9 56,8 3243,4 0,6 2,1 0,5 2,3 0,4 13 21,4 0,66 12146,7 238,3 43253 -34,6 2748,2 -34,2 2331,2 -2,6 6,2 2,0 3,6 -0,8 5,0 -1,2 -4,6 -0,1 -31,6 17250,7 8,6 13999,9 -20,3 3251,1 29,1 11959,1 50,7 4316,1 -43,8 2751,9 -30,5 2354,9 21,2 -0,1 1,7 0,0 1,7 -0,1 1,6 0,9 4,5 0,4 3,4 -0,1 5,1 -1,1 -3,7 0,9 -79,6 172313 -10,8 13995,7 -24,4 3235,7 13,7 11930,5 22,1 4312,8 -47,1 2760,5 -21,9 2298,7 -35,1 % 2,9 -0,2 1,6 -0,1 1,7 -0,2 1,1 0,4 4,3 0,2 3,3 -1,1 5,5 -0,8 '6,0 -1,5 % 1997 1996 1000 ha % 3,07 38071,22 36938,86 1,65 17242,08 16961,96 1,88 14020,13 13761,77 0,67 3221,95 3200,49 4,07 11908,40 11442,99 4,46 4359,90 4173,80 6,29 2782,37 2617,83 -4,54 2333,74 2444,61 193,49 0,51 98,01 0,57 91,08 0,65 16,56 0,51 193,52 1,63 7136 1,64 141,99 5,10 202,48 8,68 estim. = areas (x 1000 ha) and % change in relation to previous year estimated by Activity B deviat. = difference between the Activity B estimate and the Eurostat official figures % = change in area in relation to previous year E U R O S T AT final figures in May 1998 (x 1000 ha) ANNEX 5 Operational activity contractors Activity A Contractor Instituto de Estruturas agrarias e desenvolvimento rural (Portugal) Ministerio de Agricultura, Pesca y Alimentaciôn (Spain): Activity B Image analysis Main contractor SOTEMA Group Image suppliers SPOT IMAGE, S. A. EIRIMAGE, SCRL. G. A. F Subcontractor SOTEMA GEOSPACE CISI AID Bureau Veritas Faculté de Gembloux Information technology and software CISI INGENIERIE Ground data: Instituto de Estructuras Agrarias e Desenvolvimento Rural (IEADR) Remote Sensing Data Engineering, Sri Danish Institute of Plant and Soil Science Katholieke Universiteit Leuven Da Vinci Consulting Ôsterreichisches Statistisches Zentralamt Desarrollo Agrario y Pesquero de Andalucia Era-Maptec Eftas Fernerkundung Technologietransfer, GmbH Information Centre of Ministry of Agriculture and Forestry Irco s. p. r. l National Remote Sensing Center Limited Trabajos Catastrales Ministère de l'Agriculture et de la Pêche Hunting Technical Services Ltd Spazio Verde Strabo, s. a. Statistics Sweden ANNEX 6 LIST OF MARS PUBLICATIONS 1994-1998 Monographs BOUMAN, B. A. M. , SCHAPENDONK, A. H. C. M. , STOL, W. , VAN KRAALINGEN, D. , 1996. Description of the grass growth model LINGRA as implemented in CGMS. Joint publication of the JRC Space Applications Institute and the DLO Research Institute for Agrobiology and Soil Fertility. Wageningen (NL); 83pp. D'SOUZA G. , BEL WARD A. S. , MALINGREAU J. P. (Eds. ) - Advances in the use of NOAA-AVHRR data for land applications. Kluwer Academic Publishers, Dordrecht, Boston, London (1995). JONES, P. D. , SALMON, M. , 1996. Development and integration of a stochastic weather generator into a crop growth model for European agriculture. Contract Report N° 5631 - 93-12 ED ISP GB. University of East Anglia, Norwich (UK), 32 pp. PAQUET, F. , J. V. VOGT & A. VIAU, 1996. Régionalisation de la température de l'air à partir des données physiographiques et des images NOAA AVHRR. Cas de l'Andalousie. - JRC-SAI-AIS Technical Report, Ispra/Italy, 92 S. VAN DER VOET, P. , KRAMER, K. , VAN DIEPEN, C. A. , 1996. Parametrization of the Richardson weather generator within the European Union. Joint publication of the JRC Space Applications Institute and the DLO Research Institute for Integrated Land, Soil and Water Research. Wageningen (NL); 73pp. VOGT, J. , ASTRAND P. , BORIES, L. , NIEMEYER, S. , 1996. Monitoring the Quality and Quantity of Water Resources in Sicily: Development of a Geographic Information System and Integration of Remote Sensing Derived Parameters. Joint Research Centre of the EC. Space Applications Institute. Ispra (I), 61 pp + annexes. WOODING M. G. , DE GROOF H. et al. - Satellite radar in agriculture - experience with ERS-1. European Space Agency, ISBN 90-9092-339-3, Noordwijk (NL), October 1995 Contributions to specialist publications BURRILL A. , VOSSEN P. , VAN DIEPEN C. A. - A G. I. S. database for crop modelling. In: European Land Information Systems for Agro-Environmental Monitoring. King D. , Jones R. J. A. , Thomasson A. J. (Eds. ). EUR Publication N° 16232/EN (1995), 143-154 CARFAGNA E. , GALLEGO F. J. - Sulla dimensione ottimale del segmento per rilievi a terra e fotointerpretazione. Statistica e Telerilevamento. AIT. Quaderno N° 3 (1995) CNR Pisa, 41-61 GENOVESE G. - The methodology, the results and the evaluation of the MARS crop yield forecasting "system". In: Agrometeorological Applications for Regional Crop 40 Monitoring and Production Assessment. EUR Publication 17735 EN, 1998, Ispra, Italy, 100p. JONES R. J. A. , BUCKLEY B. - European Soil Database Information Access and Data Distribution Procedures. EUR Publication N° 17266/EN (1996), 35 pp. KING D. & THOMASSON A. J. , 1996 - European Soil Information Policy for Land Management and Soil Monitoring. EUR 16393 EN, Ispra, Italy, 22p. LE BAS C, 1996 - Base de Données Géographique des Sols d'Europe, EUR 16380 FR, Ispra, Italy, 39p. MONTANARELLA L. - The European Soil Bureau. European Society for Soil Conservation (ESSC) Newsletter nr. 2, 1996, 2-5. NEZRY E. , RÉMONDIÈRE S. , SOLAAS G. , GENOVESE G. - Mapping of next season's crops during the previous winter using ERS SAR", Earth Observation Quarterly, N° 50 (1995), 5 pp. TERRES J. M. , DELINCÉ J. , VAN DE STEENE M. - The use of remote sensing and GIS capabilities to support the Common Agricultural Policy of the European Community. Remote Sensing Review, Vol. 12 (1995), 53-60 VOGT J. V. (1996): Land Surface Temperature Retrieval from NOAA AVHRR Data in: G. D'Souza, A. Belward & J. -P. Malingreau (eds. ), Advances in the Use of AVHRR Data for Land Applications. - (Kluwer Academic Publishers) Dordrecht, Boston, London. , pp. 125-151. VOGT J. V. , VIAU A. , PAQUET F. (1996): Mapping Regional Air Temperature Fields Using Satellite Derived Surface Skin Temperatures. - submitted to Int. J. of Climatology. VOSSEN P. - Land information with remote sensing: the MARS Project. GIM International Journal for Geomatics, Volume 9(11) (1995), 51-55 VOSSEN P. - Crop production assessment for the European Union: the MARS-STAT Project, including the use of NOAA-AVHRR data. In: Advances in the Use of NOAA- AVHRR Data for Land Applications. D'Souza G. , Belward A. S. , Malingreau J. P. (Eds. ), Kluwer Academic Publishers, Dordrecht, Boston, London (1995), 336-355. VOSSEN P. , GENOVESE G. - MARS: previsioni della produzione agricola. AER Meteorologia, Ciimatologia, Agrometeorologia, Ambiente, Volume 8 (1995), 12-16 VOSSEN P. , MEYER-ROUX J. - Crop monitoring and yield forecasting activities of the MARS Project. In: European Land Information Systems for Agro-Environmental Monitoring. King D. , Jones R. J. A. , Thomasson A. J. (Eds. ), EUR Publication N° 16232/EN (1995), 11-30 41 EUR Technical reports BERNARD, A. C. , MEYER-ROUX, J. , 1994 (third print 1996). Proceedings of the Conference on the MARS Project: Overview and Perspectives. EUR Publication N° 15599 of the Office for Official Publications of the EC. Luxembourg, 168 pp. BESSELAT, B. , COUR, P. , MONTANARELLA (Eds. ), 1996. Guide pratique: Elaboration d'une prévision de récolte à partir du dosage pollinique de l'atmosphère. Rapport EUR 16422 FR. Institut des Applications Spatiales, Centre Commun de Recherche. Ispra, 130 pp. BINDI, M. , FIBBI, L. , MARACCHI, G. , 1996. A comparative study of radiation estimation methodologies. EUR Publication N° 16429 EN of the Office for Officiai Publications of the EC. Luxembourg, 182 pp. DALLEMAND, J. F. , VOSSEN, P. , 1995 (second print 1996). Agrometeorological models: theory and applications in the MARS Project. EUR Publication N° 16008 of the Office for Official Publications of the EC. Luxembourg, 246 pp. GALLEGO J. - Sampling frames of square segments. EUR Publication N° 16317/EN (1995), 72 pp. GALLEGO F. J. , (1996), Sampling satellite images for area estimates in a large region. Proceedings. Conference on Spatial Accuracy Assessment. Fort Collins, May 21-23, USD A Technical Report RM-GTR-277, pp. 509-516. KING D. , JONES R. J. A. , THOMASSON A. J. (Eds. ) - European land information systems for agro-environmental monitoring. EUR Publication N° 16232/EN (1995), 286 pp. KING, D. , THOMASSON, A. J. , 1996. European Soil Information Policy for Land Management and Soil Monitoring. EUR Publication N° 16393 EN of the Office for Official Publications of the EC. Ispra, 22 pp. LE BAS, C, JAMAGNE, M. (Eds. ), 1996. Soil databases to support sustainable development. EUR Publication N° 16371 of the Office for Official Publications of the EC. Jointly Published by the Joint Research Centre of the EC and the Institut National de la Recherche Agronomique (F). Orléans (F), 149 pp. MONTANARELLA L. (Ed. ) - Guide Pratique: Elaboration d'une prévision de récolte à partir du dosage pollinique de l'atmosphère. EUR 16422 FR, 1996, 151p. MONTANARELLA, L. (Editor), 1996. Soils Information for Europe. EUR Publication N° 16423 Office for Officiai Publications of the EC. Ispra (I), 21 pp. RIJKS D. , TERRES J. M. , VOSSEN P. (editors) - Agrometeorological Applications for Regional Crop Monitoring and Production Assessment. EUR Publication 17735 EN. 1998, Ispra 500 pp. 42 RUSSEL G. , WILSON G. W. - An Agro-pedo-climatological Knowledge-base of Wheat in Europe. EUR publication 15789 EN, 1994. S AI, Ispra. RUSSEL G. , MUETZELFELDT R. , TAYLOR K. - Crop Knowledge Base. EUR publication 17697 EN, 1997. SAI, Ispra. SCHOENMAKERS R. P. H. M. - Integrated methodology for segmentation of large optical satellite images in land applications of remote sensing. EUR Publication N° 16292/EN (1995), 178 pp. SUPITI. - Global Radiation. EUR Publication 15745 EN, 1994, SAI, Ispra. SUPIT, L, HOOIJER, A. A. , VAN DIEPEN, C. A. , Edts, 1994 (reprint 1996). System description of the WOFOST 6. 0 crop simulation model implemented in CGMS. Volume 1: Theory and Algorithms. EUR Publication N° 15959 EN of the Office for Official Publications of the EC. Luxembourg, 146 pp. TYCHON B. , TONNARD V. (editors) - Estimation de la production agricole à une échelle régionale. Proceedings of the Arlon Conference 16-20 September 1996. EUR Publication 17663 FR, 1997, SAI, Ispra. VOSSEN, P. RIJKS, D. , 1995 (third print, 1996). Early crop yield assessment of the EU countries: the system implemented by the Joint Research Cenre. EUR Publication N° 16318 of the Office for Official Publications of the EC. Luxembourg, 182 pp. Conferences CARFAGNA E. , GALLEGO F. J. - Yield estimates from area frame at European level. In: Proceedings of the Seminar on Yield Forecasting, 24-27 October 1994, Villefranche- sur-Mer (France), Published by EUROSTAT-CESD, 237-240. CARFAGNA E. , GALLEGO F. J. - Extrapolating intra-cluster correlation to optimize the size of segments in an area frame. In: Proceedings of the VI Conference on Applied Statistics to Agriculture, 1995, Kansas State Univ. Manhattan (USA), 261-270 CARFAGNA E. , GALLEGO F. J. - Three stage area frame sampling for a farm survey in the European Union. In: Proceedings of the 50th Session of the International Statistical Institute, August 1995, Beijing (P. R. China), Book 1, 141-142. CHIUDERI, A. , CAPPELLINI, V. , 1996. A Kohonen's Self-Organizing Map for in Landcover Classification. Proc. of Environmental Research and Applications Basel (CH) September 4-6, 1995. the EARSeL Symposium on Progress CHIUDERI, A. , 1996. Neural Networks for multi-temporal and multi-sensor data fusion in landcover classification. International Workshop on Time-varying Image Processing and Moving Object Recognition, Florence (Italy), Sept. 5-6 1996. 43 CHIUDERI, A. , 1996. Integrating LANDSAT and SPOT images to improve land cover classification accuracy. 3rd International Workshop on Signal/Image Processing, Manchester (UK) November 4-7,1996, pg. 365-368. FUENTES M. , GALLEGO F. J. - Stratification and cluster estimator on an area frame by square segments with an aligned sample. In: Proceedings of the VI Conference on Applied Statistics to Agriculture, 1995, Kansas State Univ. Manhattan (USA), 112-121 GALLEGO F. J. - Agricultural surveys on area frame based on a square grid. In: Proceedings of the 50th Session of the International Statistical Institute, August 1995, Beijing (P. R, China), Book 1, 378-379 GENOVESE G. - Yield forecasting and operational approaches using remote sensing: overview of approaches and operational applications in 1994 in EU. In: Proceedings of the Seminar on Yield Forecasting, 24-27 October 1994, Villefranche-sur-Mer (France), Published by EUROSTAT-CESD, 91-99 GENOVESE, G. - The MARS Bulletin, an integrated assessment of agricultural status. In: Proceedings of the Eurocourse on Agrometeorological Models, Theory and Applications in the MARS Project, 21-25 November 1994, Ispra (Italy), EUR Publication N° 16008/EN (1995), 221-240 GENOVESE G. , NEZRY E. , ARCANGELI N. - Stime precoci di superfici agricole utilizzando i dati radar dei satelliti ERS. In: Problemi Statistici nel Telerilevamento. Atti delle giornate di studio dell' AIT e della SIS, Roma 10-11 Giugno 1996. Rubettino éd. , 1998, Roma. GENOVESE G. , TERPESSI C. - The use of remote sensing for the derivation of crop yield and areas statistics at different scales. In: Proceedings of the 50th Session of the International Statistical Institute, August 1995, Beijing (P. R. China), Book 1, 396-397 KING D. & MONTANARELLA L. - European Soil Information: Present status and future perspectives. In: Proceedings of the National Congress of the Spanish Soil Science Society, Lleida, 1996. 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Volume 2 (1994) N°: 1,2,3,4,5,6,7,8 Volume 2 (1995) N°: 1,2,3,4,5,6,7,8 Volume 2 (1996) N°: 1,2,3,4,5,6,7,8 Volume 2 (1997) N°: 1,2,3,4,5,6,7 Volume 2 (1998) N°: 1,2,3,4,5,6 Joint Research Centre of the EC, Space Applications Institute, Ispra (Italy). 46 MARS Project, 1996. Agrometeorological monitoring of the whole continent of Europe and the Maghreb region. Situation at the end of May, July, August, September and November 1996. Space Application Institute, Joint Research Centre of the EC. Ispra (Italy), variable number of pages. Technical documents ACTIVITY B DOCUMENTATION - Documentation on the first 5 years of operation of Action 4. Volumes 1-8. Ispra, 1994, Scot Conseil and EC editors. BORIES L. , P. VOSSEN - Bilan provisoire du programme 1988-1995 "Méthode pollinique pour la prévision précoce du potentiel de production viticole en Europe". Technical Report with Limited Circulation. 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In: Project Document for DG I - External Affairs, in the context of the PHARE Regional Programme (1995), 55 pp. 48 ANNEX 7 Abbreviations used Project Management Programme specially used by Activity B European Community European Economic Community Crop Growth Monitoring System European unit of account Earth Remote Sensing Estimation European Union Food and Agricultural Organization of the United Nations Geometric and Radiometric Image Processing System Indian Remote Sensing Satellites CAMEL EC EEC CGMS ECU ERS estim EU FAO GRIPS 1RS LANDSAT System of Satellites for Earth Resources Observation LISS MARS NDVI NOAA- AVHRR OCT. OLIWIN ORCA CAP PC PHARE October Agro-meteorological Model for the Estimation of Olive and Grapevine yields Orbital Remote Sensing of Crop Areas Common Agricultural Policy Personal Computer Programme of economic restructuring aid for Central and East European countries. Satellite September Experimental Earth Observation system Technical Assistance to the Commonwealth of New Independent States European Union Multispectral Sensors on Board of 1RS Satellites Monitoring Agriculture by Remote Sensing Normalised Difference Vegetation Index Constellation of Satellites with Advanced Very High Resolution Radiometer SAR SEPT. SPOT TACIS EU M^ ISSN 0254-1475 COM(98) 613 final DOCUMENTS EN 03 15 17 Catalogue number : CB-CO-98-603-EN-C Office for Official Publications of the European Communities L-2985 Luxembourg fo