Towards a Large-Scale Rainforest Mapping System with Sentinel-1 Short Time Series

In this work, we investigate the challenges of implementing a large-scale rainforest mapping with Sentinel-1 short time series. A frequent and accurate monitoring of these ecosystems is of utmost importance in the context of environmental policy-making. In particular, we propose to combine additional descriptive features with deep learning to mitigate the effect of seasonal components on SAR backscatter and interferometric coherences over a year of acquisitions in the Amazon forest. Preliminary analyses suggest that precipitation patterns might play a key role in how discernible land cover classes are with respect to the radar-based input data. Moreover, our findings show that such effects may vary in different regions of the rainforest, so that different configurations of ancillary features might be necessary to achieve a large-scale model able to generalize in both seasonal and regional dimensions.