Quantifying how soil erosion shapes crop yield at landscape scale, and how this relationship evolves in a changing climate, is critical for safeguarding food security in erosion-prone agricultural regions. Yet such assessments remain constrained by the lack of spatially continuous, physically meaningful erosion observations. Here, we developed a spectra-based modelling framework based on the principle that erosion-induced lateral redistribution of soil materials leaves distinct optical fingerprints, detectable by multispectral imagery. A cross-validated random forest model integrating multitemporal Sentinel-2 bare soil mosaics, spectral indices, and terrain attributes predicted 137 Cs-derived soil redistribution rates, enabling high-resolution erosion mapping (R 2 =0.66, RMSE = 1.51 mm yr −1 ) in an intensively managed Mollisol catchment (46.20 km 2 ) in Northeast China. Coupling the predicted erosion map with a phenologically consistent NDVI time series (1985–2023), we show that while crop yields improved over recent decades, severe erosion has persistently suppressed crop performance beyond a threshold of −2 mm yr −1 . The erosion-yield relationship exhibited distinct functional forms modulated by hydroclimatic conditions: a concave response in dry years, with yield rising sharply only beyond the −2 mm yr⁻¹ threshold as water deficits amplified the constraints of truncated profiles; and a convex response in wet years, with yield rising rapidly as erosion intensity lessened, before plateauing at high values once limitations on moisture and nutrients were alleviated. Crucially, the explanatory power of soil redistribution for productivity patterns varied tenfold across the precipitation gradient, demonstrating that erosion impacts were amplified under drought stress. This integrated framework establishes a foundation for monitoring erosion-yield relationships in a changing climate, with critical implications for developing climate-resilient soil management strategies in erosion-prone agricultural regions.
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