National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images

Abstract

Precise monitoring of soil organic carbon (SOC) is urgently needed in agricultural regions to tackle global challenges like food security, water regulation, land degradation, and climate change. Remote sensing technology has emerged as a powerful method for detecting variations in SOC at localized scales. However, its application on a broader, national scale faces limitations, especially in countries like China, where soil landscapes exhibit significant diversity. This study aimed to couple bare soil reflectance and conventional environmental covariates to map Chinese cropland SOC content at a 10-m spatial resolution. First, a new time-series bare soil extraction method, the Two-Dimensional Bare Soil Separation Algorithm, was applied, utilizing Sentinel-2 images from 2018 to 2022 to generate a continuous spectral reflectance composite. Then, nine indices with the strongest correlation to SOC were selected. Additionally, a list of environmental covariates was prepared based on SCORPAN model. Finally, bootstrapping random forest models were fitted using the covariates selected through forward recursive feature selection (FRFS), and the spatial prediction SOC map was created. The results indicated that the framework was suitable for mapping SOC in croplands of China, with the best model using remote sensing indices and environmental covariates selected through FRFS achieving an R² of 0.62, an RMSE of 4.84 g kg⁻¹, and an uncertainty depicted by a 90 % prediction interval range of 17.88 g kg⁻¹. The final map showed that the Northeast China had the highest SOC content in cropland. Climatic conditions, position, and remote sensing indices are key covariates in national-scale SOC estimation. This study can be easily implemented across broad areas for the prediction of SOC with computational efficiency. The 10-m spatial resolution SOC map of China contributes to land management and the development of policies for precision agriculture.