AI-Based Spatiotemporal Crop Monitoring by Cloud Removal in Satellite Images

Efficient crop monitoring and crop dynamics forecasting leveraging diverse satellite and point data are described. UnCRtainTS neural network architecture is utilized for cloud removal in satellite imagery which overcomes an issue in crop monitoring. Combining optical (Sentinel-2) and radar (Sentinel1) satellite data improves the robustness and accuracy of the model in terms of satellite image reconstruction and vegetation index estimation. However, available soil-type geographical data and land surface analysis products, do not improve prediction accuracy significantly.