Cropland inundation mapping in a mountain dominated region based on multi-resolution remotely sensed imagery and active learning for semantic segmentation

Abstract

Accurate and timely mapping of cropland inundated area is critical to loss assessment and recovery plan development. However, existing methods for cropland inundation mapping using Synthetic Aperture Radar (SAR) imagery are primarily designed for plain regions and may not be suitable for mountainous regions. The complex rugged terrain at large scale present challenges such as significant noise from radar shadows, smooth surfaces, floodplains, and permanent water bodies. To remove these noise for effectively mapping of cropland inundation, this study proposes a robust two-stage framework integrating the multi-resolution remotely sensed imageries and semantic segmentation method. First, active learning for cropland extraction is performed with Google Very-High-Resolution (VHR) imagery and Segformer model. Second, the cropland inundation area is obtained using marker-controlled watershed segmentation (MCW) on Sentinel-1 SAR imagery with area-of-interest (AOI) filtering. Results show that the extracted cropland mask are superior to six public cropland data products including ESA, ESRI, FCS30, FROM, GL30, and SLOPE. In addition, with AOI filtering, the F-score of the potential flood identification using MCW was increased from 0.685 to 0.828. At last, the monthly cropland inundation caused by intense rainfall varied approximately from 6.6 km2 to 436.1 km2 during monsoon season of 2017–2022 in Guangdong. Our approach offers a fast and feasible solution for mapping cropland inundation in large and complex mountainous regions, without requiring extensive parameter tuning or additional data.