Estimating NDVI from Sentinel-1 Sar Data Using Deep Learning

Monitoring vegetation is of great importance for many applications, for example agriculture or forestry. Commonly, the normalized difference vegetation index (NDVI) of spaceborn sensor is utilized for this task. However, as the NDVI is derived from multispectral optical data, cloud coverage prevents the acquisition of useful values. This results in data and monitoring gaps. Generally, this can be avoided using cloud penetrating radar sensors but the different sensing method and different image characteristics hamper the easy usage of the data. Therefore, in this paper a method is presented to allow global cloud-independent vegetation monitoring by estimating the NDVI from radar data using a deep learning model. The used U-Net architecture is trained on a newly created dataset called SEN12TP of globally distributed radar and optical imagery with a small difference in acquisition time. The resulting performance is evaluated and different input modalities are compared. Additionally, the ability of this approach to densify NDVI time series is demonstrated.