MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

• Published in: ISPRS journal of photogrammetry and remote sensing 2023-11, Vol.205, p.176-190
• Main Authors: Li, Zhouyayan, Xiang, Zhongrun, Demiray, Bekir Z., Sit, Muhammed, Demir, Ibrahim
• Format: Article
• Language: English
• Subjects: Deep learning; Floods; Image synthesis with physical forces; One-shot nowcasting; Remote sensing; SAR image prediction
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2023.10.002

Remote sensing imagery is one of the most widely used data sources for large-scale Earth observations with consistent spatial and temporal quality. However, the current usage scenarios of Earth’s surface remote sensing images, such as those generated from Landsat, Sentinel 2, and SAR, are largely limited to retrospective tasks, as they are often used to reconstruct existing phenomena, such as land use change, flood inundation, and wildfire. This study proposes MA-SARNet, a one-shot nowcasting framework built with a modified MA-Net structure and ResNet50 as the backbone, to predict the backscatter values of Synthetic-Aperture Radar (SAR) images using the previous SAR observations, precipitation, soil moisture, and geomorphic data layers as input. The model was trained, validated, and tested with SAR images collected during the catastrophic 2019 Midwest U.S. Floods that affected several states on the Missouri and Mississippi River tributaries. Compared to the benchmark performance, model predictions show an increase of 31.9% and 17.8% for the mean and median AAI (Assemble Accuracy Index) and an increase of 37.9% and 15.1% for the mean and median NSE (Nash-Sutcliffe Efficiency) on the test set. Results showed that the flood extent derived from backscatter predictions has less misclassifications caused by pixel-level noise compared to the flood map derived using the real backscatters. Results from spatial and temporal robustness tests demonstrate that the model has sufficient generalization potential and does not require further fine-tuning to work with new data, and therefore proves its usefulness in real-time Earth surface prediction tasks that are aimed at fast response to and mitigation for upcoming floods on a tight time schedule.