Dynamic Monitoring of Soil Salinization in Oasis Regions Using Spatiotemporal Fusion Algorithms

Accurate dynamic monitoring of soil salinization in arid oasis regions is crucial for sustainable regional development. Remote sensing is widely used for large-scale, long-term monitoring, but its effectiveness is often limited by image quality and spatiotemporal resolution. Spatiotemporal fusion algorithms, due to their low cost and accessibility, are frequently applied to generate missing images. However, the applicability of these fused images for soil salinization inversion, the impact of different fusion strategies on image quality, and the potential for using multiple fused images to improve model accuracy remain unclear. This study evaluates the performance of three typical spatiotemporal fusion algorithms on raw spectral bands and compares two fusion strategies: fusion-then-index (FI) and index-then-fusion (IF), for two vegetation indices (NDVI and EVI) and two salinity indices (SI and SI2) related to soil salinization. Additionally, the inclusion of multiple fused images during the sampling period is examined for its effect on model accuracy. The results show that (1) spatiotemporal fusion images are suitable for soil salinization inversion, with accuracy depending on image quality; (2) for vegetation indices (NDVI and EVI), the IF strategy yields better results, while for salinity indices (SI and SI2), the FI strategy is more effective; and (3) combining multi-year and multiple fused images significantly improves model accuracy, though using fused images as auxiliary datasets or variables does not further enhance accuracy. These findings provide valuable insights for large-scale, long-term monitoring of soil salinization in arid regions.