Abstract: This study aims at investigating variations in soil moisture and vegetation net primary productivity （NPP） in the Qingling River Irrigation Area，Yunnan （elevation 1 515~1 876 m），a typical subtropical alpine climate region. To this end，initially，this study recognized land surface temperature （LST） and normalized difference vegetation index （NDVI） as explanatory variables，leveraging remote sensing technology for rapid and long-term sequential monitoring. Subsequently，the SMAP L4 soil moisture product was downscaled to a 30 m spatial resolution using the random forest adaptive window regression algorithm. Then，the water stress parameter of the CASA model was modified using the land surface water index （LSWI），which integrated multi-source remote sensing data，such as surface reflectance，to estimate NPP. Following spatial resampling，a 30 m resolution NPP spatial distribution was achieved. Finally，multiple land cover scenarios，including forest land，paddy fields，and irrigated farmland，were established. The Pearson correlation coefficient was introduced for the quantitative evaluation of the spatial relationship between soil moisture and NPP in the study area. In terms of the spatial distribution of soil moisture，the study area exhibited higher values in the north and lower values in the south during summer，while lower values in the northwest and higher values in the southeast and south during winter. Compared to field measurements，the inverted NPP results showed a R²>0.7 and a RMSE<0.3. Both summer，winter，and annual average NPP values at the pixel level showed an increasing trend over time. Spatially，scenarios such as paddy fields and forested land presented correlation coefficients exceeding 0.5. Among these，forest land was least sensitive to water stress，while paddy fields and irrigated farmland were most affected. This study establishes a monitoring and feedback mechanism for the soil moisture-NPP balance from seasonal and spatial perspectives in the study area.