Cloud gap-filling with deep learning for improved grassland monitoring
Iason Tsardanidis Alkiviadis Koukos Vasileios Sitokonstantinou Thanassis Drivas Charalampos Kontoes
Uninterrupted optical image time series are crucial for the timely monitoring
of agricultural land changes. However, the continuity of such time series is
often disrupted by clouds. In response to this challenge, we propose a deep
learning method that integrates cloud-free optical (Sentinel-2) observations
and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data, using
a combined Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN)
architecture to generate continuous Normalized Difference Vegetation Index
(NDVI) time series. We emphasize the significance of observation continuity by
assessing the impact of the generated time series on the detection of grassland
mowing events. We focus on Lithuania, a country characterized by extensive
cloud coverage, and compare our approach with alternative interpolation
techniques (i.e., linear, Akima, quadratic). Our method surpasses these
techniques, with an average MAE of 0.024 and R^2 of 0.92. It not only improves
the accuracy of event detection tasks by employing a continuous time series,
but also effectively filters out sudden shifts and noise originating from
cloudy observations that cloud masks often fail to detect.