Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

Abstract

Cropland soil organic matter (SOM) is recognized as a significant carbon reservoir in terrestrial ecosystems. Digital mapping of SOM in croplands is essential for comprehending the global carbon cycle. Accurately mapping cropland SOM using multi-source remote sensing data has been effectively incorporated into prediction models across various scales. However, the impact of multi-source remote sensing data on cropland SOM mapping outcomes in hilly and mountainous regions remains insufficiently understood. In this study, Jiangyou City, located in Sichuan Province, China, was chosen as a representative example of hilly and mountainous regions. Fifteen distinct feature combinations were devised using three remote sensing variables (Sentinel-1, Sentinel-2, and Landsat-8) along with DEM data. Feature selection was conducted using the Boruta algorithm. Subsequently, the RF, SVR, Cubist, and INLA-SPDE models were adopted to create spatially detailed distribution maps of cropland SOM for the region. Additionally, an uncertainty analysis was performed on the cropland SOM mapping results. The results indicate the following: (1) The INLA-SPDE model, which integrates both data information and spatial structure, achieves the highest accuracy and the less uncertainty in cropland SOM mapping, with an R² of 0.647 and an RMSE of 4.227 g/kg. (2) Optical imagery is more important than SAR images, but their combination enhances model accuracy. Specifically, Sentinel-2 data has a significant impact cropland SOM prediction in hilly and mountainous areas, followed by Landsat-8 data. (3) The predicted spatial distribution patterns of cropland SOM by the four models show consistency, indicating lower SOM content in the southwest and higher SOM content in the central and northeast regions. This study provides valuable references for future large-scale and high-spatial cropland SOM prediction, highlighting the importance of spatial resolution for precise SOM prediction accuracy in hilly and mountainous regions.