[Ground-based Hyperspectral Coupled Interpretable Integrated Machine Learning for Salinity and pH Inversion in Agricultural Soils].
Abstract
， RMSE， and RPD of 0.908， 0.151， and 3.31， respectively. ④ SHAP analysis showed that the prediction models for SSC and pH reflected multifactorial synergies. Waveband and climate factors were the dominant factors in SSC modeling with a cumulative contribution of 80.8%. Soil attributes （24.88%） had the highest contribution to pH modeling， waveband data had the smallest contribution of 15.13%， microwave remote sensing data had limited contribution to salinity and alkalinity modeling， and the combination of multi-source data provided a strong support for the accurate monitoring of soil salinization and alkalization. The study conclusions help to promote sustainable land management and efficient agricultural production.
Fields of Study & Subjects
Soil Geostatistics and Mapping
Spectroscopy and Chemometric Analyses
Engineering and Agricultural Innovations
Publication details
- Journal / source
- PubMed
- Volume
- 47
- Issue
- 2
- Pages
- 1080–1091
- Publication date
- 8 February 2026
- Language
- ZH