[Ground-based Hyperspectral Coupled Interpretable Integrated Machine Learning for Salinity and pH Inversion in...

Soil salinity and alkalinity are key factors limiting sustainable agricultural development. Timely acquisition of salinity and alkalinity information is crucial for soil improvement and long-term fertility enhancement. After orthogonal signal correction （OSC） transformation of the hyperspectral reflectance， competitive adaptive reweighted sampling （CARS） was used to screen the characteristic bands of salinity and alkalinity information using the ground hyperspectral and measured soil salinity （SSC） and pH values of the Hetao Plain as data sources. Then， environmental variables and microwave remote sensing data were introduced to build the inversion models based on six integrated machine learning algorithms， including extreme gradient boosting （XGBoost）， adaptive boosting （AdaBoost）， and random forest （RF）， and six integrated machine learning algorithms were used to build inversion models of SSC and pH. The models were visualized and analyzed using Shapley additive explanations （SHAP）. The results showed that： ① The salinity and alkalinity grades of farmland soils in the Hetao Plain were generally mild to moderate， with strong spatial heterogeneity in salinity and alkalinity. ② The OSC transform optimized the structure of the spectral data， which greatly improved the resolution ability under the complex background. CARS effectively screened out the characteristic bands related to salinity and alkalinity information， and the SSC characteristic bands included 13 bands such as 450， 470， and 600 nm. The pH characteristic bands included 15 bands such as 680， 730， and 740 nm. ③ The AdaBoost algorithm performed optimally for SSC inversion with validation set R_p²， root mean square error （RMSE）， and relative analysis error （RPD） of 0.852， 1.352， and 2.88， respectively， whereas pH was best with the XGBoost model， which had an R_p²， 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.