Prediction of soil organic carbon and nitrogen: a WMTR-CatBoost model based on multi-feature fusion

To overcome the limitations of limited feature diversity and insufficient exploitation of inter-variable correlations in hyperspectral prediction of soil organic carbon (SOC) and nitrogen (N), we propose a novel framework that integrates multi-feature fusion (MFF) with multi-target regression (MTR). The framework combines spectral features (SF) from the LUCAS 2015 dataset with land classification features (LCF) and remote sensing features (RSF), and further enriches the feature space using outputs from single-target regression (STR) models. A weighted MTR CatBoost model (WMTR-CatBoost) is then developed to systematically evaluate the contributions of MFF and MTR. Experimental results show that WMTR-CatBoost achieves coefficient of determination (R2) values of 0.945 for SOC and 0.923 for N, representing relative improvements of 2.5% and 4.6% over the SF-based baseline, and gains of 1%–3.3% for SOC and 1%–8.1% for N compared with STR-based models. These findings confirm the effectiveness of the proposed MFF–MTR framework and highlight its potential for advancing large-scale soil nutrient monitoring and precision agricultural management.