Gaofen-1/Gaofen-6 (GF1/GF6) wide-field-view (WFV) imagery provides repeated wide-area observations for regional surface-water monitoring, but its use is complicated by fragmented water patterns, vegetation–water mixtures, terrain and cloud shadows, and cross-scene radiometric variability. This study presents a seven-channel nested U-Net (UNet++) framework with a residual logit refinement branch (RLRB) for binary surface-water extraction. The input combines Blue, Green, Red, near-infrared, normalized difference water index, normalized difference vegetation index, and digital-elevation-model-derived slope channels. Five complete GF1/GF6 scenes were locked before dataset partitioning, and an 8192 m exclusion buffer was used to exclude spatially overlapping or adjacent non-test tiles from model development, thereby reducing the risk of spatial leakage. The resulting quantitative test set contained 2783 valid 512 by 512 pixel tiles derived from the five scenes withheld at scene level. The UNet++ model with RLRB and edge-consistency supervision achieved an mIoU of 0.941, Water IoU of 0.889, and Water F1 of 0.941. A controlled $2\times 2$ experiment separated the effects of residual refinement and edge-consistency supervision, while feature, loss-component, random-seed, rule-based-baseline, and complete-scene analyses further characterized the framework. Complete-scene sliding-window inference required approximately 3.7 min per scene on a single NVIDIA GeForce RTX 5060 Laptop GPU, demonstrating the feasibility of georeferenced water-mask generation under the specified computational setting.

Drought events exacerbated by global climate change occur frequently in China. Currently, high-spatiotemporal-resolution gridded meteorological drought index datasets are generally available for single time scales (e.g., 30, 60, 90, and 150 days) and do not fully account for seasonal differences in the impact of drought on vegetation, thus limiting their accuracy when monitoring drought in different regions of China. To compensate for the limitations of existing drought index datasets, a Chinese regional daily meteorological drought comprehensive index (MCI) dataset covering 1951–2022 with a spatial resolution of 0.1 degrees was developed, and standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) datasets at 30- and 90-day scales were constructed based on ERA5-Land datasets. Compared with the existing SPI and SPEI datasets, the generated dataset exhibits a high degree of consistency with those in eastern part of China (R² > 0.5; the average biases were close to 0 and significantly smaller than RMSEs of the fitting). Additionally, the MCI dataset can more accurately reflect the changes in shallow soil moisture in the eastern part of China in a timely manner (R² > 0.7 for the 0–7 cm depth), thus providing notable empirical support for research on drought development in different ecosystems.