Investigating the Optimal Time Window and Composition Strategy for Soil Salinity Content Retrieval in the Yellow River Delta, China

Soil salinization is a primary bottleneck for the sustainable development of agriculture in the Yellow River Delta (YRD). Conventional remote sensing monitoring predominantly relies on single-phase instantaneous spectral responses, which are highly vulnerable to transient environmental interferences, such as surface moisture fluctuations. This study proposes a novel predictive framework based on legacy vegetation signals. By integrating multi-temporal Sentinel-2 imagery from the 2024 growing season, we quantified the cumulative physiological feedback of crops from the preceding year and developed a spring soil salinity content (SSC) inversion model for 2025 using the LightGBM algorithm. The results demonstrate that the median compositing technique significantly enhances model robustness against outliers. Furthermore, the optimal time window for capturing these legacy signals for spring salinity monitoring was identified as July to September. Compared with traditional immediate monitoring models, the LightGBM model based on previous-season legacy signals achieved superior predictive accuracy (R2 = 0.84), effectively mitigating the impact of stochastic noise. This research validates the critical role of long-term vegetation memory in salinity early warning and provides a robust scientific foundation for the precision management of coastal saline-alkali land.