Rainforest Monitoring Using Deep Learning and Short Time Series of Sentinel-1 IW Data

The latest advances in remote sensing play a central role in providing Earth observation (EO) data for numerous applications in the scope of reaching environmentally sustainable goals. However, over tropical rainforests, optical imaging is often hindered by extensive cloud coverage, which means that analysis-ready images are mostly restricted to the dry season. In this study, we propose combining radar features extracted from short time series of Sentinel-1 Interferometric Wide Swath (IW) data with a deep learning-based classification scheme to continuously monitor the state of forests. The proposed methodology is based on the joint use of SAR backscatter and interferometric coherences at different temporal baselines to perform pixel-wise classification of land cover classes of interest. However, we show that for a sequence of Sentinel-1 time series, different land cover classes exhibit particular seasonal-dependent variations. Another challenge in performing short-term predictions stems from the fact that ground truths are usually available only on a yearly basis. To address these challenges, we propose a seasonal sampling of the training data, masked by potential deforestation, along with a classification based on a modified U-Net model. The classification results show that overall accuracies above 90% can be achieved throughout the whole year with the proposed method, emerging as a potential tool for mapping rainforests with unprecedented temporal resolution.