Prediction of Vegetation Indices Series Based on SWAT-ML: A Case Study in the Jinsha River Basin

作者：Chong Li, Qianzuo Zhao, Junyuan Fei, Lei Cui, Xiu Zhang, Guodong Yin · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17060958 · 被引用次数：5 · 研究领域：Remote Sensing in Agriculture、Environmental and Agricultural Sciences、Remote Sensing and Land Use

Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation dynamics data for ecohydrological simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) are widely used in monitoring vegetation dynamics and ecohydrological research. Accurately predicting long-term SIF and NDVI dynamics can support the monitoring of vegetation anomalies and trends. This study proposed a SWAT-ML framework, combining the Soil and Water Assessment Tool (SWAT) and machine learning (ML), in the Jinsha River Basin (JRB). The lag effects that vegetation responds to using hydrometeorological elements were considered while using SWAT-ML. Based on SWAT-ML, SIF and NDVI series from 1982 to 2014 were reconstructed. Finally, the spatial and temporal characteristics of vegetation dynamics in the JRB were analyzed. The results showed the following: (1) the SWAT-ML framework can simulate ecohydrological processes in the JRB with satisfactory results (NS > 0.68, R2 > 0.79 for the SWAT; NS > 0.77, MSE < 0.004 for the ML); (2) the vegetation index’s mean value increases (the Z value, the significance indicator in the Mann–Kendall method, is 1.29 for the SIF and 0.11 for the NDVI), whereas the maximum value decreases (Z value = −0.20 for SIF and −0.42 for the NDVI); and (3) the greenness of the vegetation decreases (Z value...