Soil monitoring is fundamental for maintaining global soil health, ensuring food security, and achieving sustainable development. While satellite platforms provide invaluable tools for this purpose, the accuracy of soil monitoring heavily relies on the appropriate design of their remote sensing payload parameters. This study focuses on enhancing the accuracy of satellite-based global soil monitoring. Key physicochemical soil parameters—including total nitrogen (TN), soil organic matter (SOM), total salt content (TSS), moisture content (MC), and clay fraction (Clay)—were analyzed. A full-chain analytical validation model integrating “instrument–radiative transfer–soil parameter inversion” was developed. Using spectral measurements and soil sample analyses from the black soil region of Northeast China, the spectral response characteristics of core soil parameters were simulated and cross-validated under varying spectral resolutions and integration times. Results indicate that, under specific parameter configurations, the ‘Linshi-1’ satellite achieved robust TN inversion accuracy with R2 > 0.65. SOM consistently exhibited good inversion performance, with RMSE ranging between 5.04 and 5.76 g/kg across various spectral treatments (all 263). MC inversion accuracy was sensitive to both spectral resolution and regional variations, requiring a resampled resolution below 10 nm for consistently high accuracy. Clay inversion required the highest resolution, achieving an RMSE of less than 6 g/kg only at resampled resolutions of 1 nm or 2 nm (SNR approximately 150–210). These findings guided the design of the ‘Linshi-1’ black soil monitoring satellite system and its hyperspectral payload prototype. This effort establishes a solid theoretical and methodological foundation for future deployment, providing crucial space-based support for China’s black soil resource management and sustainable utilization.
Hu et al. (Sun,) studied this question.