Prediction of the flood distribution caused by returning cropland to forest based on Generative Adversarial Network and multi-source remote sensing data

Variations in Land Use and Land Cover (LULC) significantly influence flooding patterns, particularly through alterations in forest and cropland. However, existing flood prediction studies rarely utilize LULC maps as direct training inputs, typically generating probabilistic rather than spatially explicit flood distribution maps. This study addresses this gap by integrating Generative Adversarial Networks (GANs) with multi-source remote sensing data to predict flood distribution from LULC maps. We focus specifically on “Returning Cropland to Forest” initiative of China using 2020 remote sensing data from the Poyang Lake region. Flood impact ranges were extracted through Synthetic Aperture Radar (SAR) and optical data fusion, producing 3,972 LULC-flood map pairs for model training and validation. The multi-source fusion approach achieved substantial improvements in flood extraction accuracy. User accuracy increased from 60% (SAR-only) to 90% (SAR-optical fusion), while overall classification accuracy reached 89.76% with an F1 score of 94.58%. Model performance validation through Fréchet Inception Distance (FID) scores demonstrated high-quality flood map generation, with FID decreasing from 81.5 to 53 at epoch 45. Analysis revealed that reforestation ratios exceeding 50% significantly reduce flood occurrence. Optimal effectiveness was observed when forests are strategically positioned along rivers and lake edges rather than randomly distributed. These findings provide actionable guidance for planners to prioritize riparian zones for initial reforestation, then systematically achieve 50% reforestation ratios in flood-prone watersheds, integrating these targets within existing regulatory frameworks. This research contributes a robust framework for integrating LULC data into predictive flood models, advancing sustainable flood management and enhancing water resource security strategies. © 2025 The Authors.