Spatiotemporal Fusion of Multi-Temporal MODIS and Landsat-8/9 Imagery for Enhanced Daily 30 m NDVI Reconstruction: A Case Study of the Shiyang River Basin Cropland (2022)

Drought poses a severe threat to crop health and food security, particularly in arid regions like the Shiyang River Basin (SRB), highlighting the need for timely monitoring to support sustainable agriculture. The normalized difference vegetation index (NDVI) is a critical tool for evaluating crop conditions. However, existing NDVI datasets often lack the spatial-temporal resolution required for effective crop monitoring. This study introduces an NDVI reconstruction method combining Savitzky–Golay filtering with the variation-based spatiotemporal data fusion model to produce a high-resolution daily NDVI dataset for SRB cropland in 2022, with a 30 m spatial resolution. The dataset achieves a cropland integrity rate of 98.50%, a 42.35% improvement over the initial MOD09GA NDVI. It also demonstrates high accuracy, with an average r-mean of 0.7511—49.88% higher than MOD09GA NDVI. Validation metrics, including abs-AD (0.0064), RMSE (0.0466), abs-EDGE (0.0373), and abs-LBP (0.0317), fall within acceptable ranges. This enhanced NDVI dataset facilitates detailed monitoring of crop conditions across diverse growth stages and planting structures, offering valuable insights for precision agriculture in the region.