Cross-Regional Hyperspectral Estimation of Soil Organic Carbon in Eurasian Black Soils Using an Optimal Spectral Feature Set

Soil organic carbon (SOC) plays a critical role in the global carbon cycle and agroecosystem productivity. However, existing hyperspectral inversion models often exhibit significant predictive biases when applied across large geographic scales, primarily due to the spatial heterogeneity of pedogenic environments and background mineralogy. This study proposes a cross-regional SOC prediction method based on an optimal spectral feature set (SOC-OSFS). Leveraging laboratory hyperspectral and SOC data from 17,730 samples collected across the black soil regions of Northeast China and Europe, a core spectral feature set comprising 31 diagnostic bands was extracted using the competitive adaptive reweighted sampling (CARS) algorithm combined with the successive projections algorithm (SPA). Although this SOC-OSFS accounts for merely 1.55% of the original full-spectrum dimensionality (31 out of 2000 bands), it demonstrated robust analytical capability in local modeling across all study regions, yielding coefficients of determination (R2 = 0.6714–0.8854). When transferring the prediction model calibrated in the core source domain (n = 10,000) to the other seven independent typical black soil target domains, the direct cross-regional prediction consistently reduced the root mean square error (RMSE) by over 15% compared to that of the full-spectrum models. By further incorporating 20% of the local background samples for intercept correction, the cross-regional predictive accuracy was substantially improved; the goodness-of-fit for the Northeast China target domains increased sharply (maximum R2 = 0.8567), and the European target domains, which feature substantially different pedogenic environments, were successfully corrected from negative to positive linear fits. This study validates the efficacy of extracting physiochemically meaningful spectral bands in mitigating the interference caused by spatial heterogeneity, thereby providing a mechanistically grounded and practically viable framework for large-scale SOC estimation via remote sensing.