An Improved Remote-Sensing Mapping Framework for Soil Total Nitrogen in Fog-Prone Regions: Integrating SST Covariates with the XGBoost Model

Monitoring soil total nitrogen (TN) status and spatial variation is essential for preventing land degradation and supporting precision agriculture. Remote sensing (RS) bands and their derived spectral indices are important covariates for soil TN mapping. However, optical RS-based soil mapping may be ineffective in fog-prone regions because water vapor interferes with spectral information. Here, multi-source earth observation data were acquired, including synthetic aperture radar (SAR), multispectral, and topographic data. Spectral indices, SAR-derived indices, and topographic indices related to soil TN were systematically developed. Subsequently, the PCC-VIF method was used to assess the importance of RS-based indices and select the optimal covariates, referred to as SST covariates. Finally, SST covariates and measured soil TN values were used as the independent and dependent variables, respectively, to construct an XGBoost-based soil TN mapping model for regions with persistent foggy cover. The results showed that: (1) SST covariates included Ela, TWI, SOS, VHplusVV, VHdiffVV, B2, and NDGI; (2) the proposed RS-based soil TN mapping model achieved satisfactory accuracy, with an R2 of 0.82 and an RPIQ of 3.03. Compared with models developed using traditional approaches, the R2 increased by approximately 0.16–0.52; and (3) areas with high soil TN content were mainly located in the southern part of the study area, whereas lower TN values occurred in the northern part. Soil TN content and its spatial distribution were mainly influenced by agricultural planting activities and microtopographic conditions. This study provides new insights into RS-based soil TN mapping in fog-prone regions. The robust soil TN mapping framework based on SST covariates and the XGBoost algorithm can support food security and help prevent environmental degradation.