农业机械学报2026，Vol.57Issue(20)：94-107,14.DOI:10.6041/j.issn.1000-1298.2026.20.009
基于CWT-Stacking集成学习模型的灌区土壤水盐含量和pH值估算
Estimation of Soil Water-Salt Content and pH Value in Irrigation Districts Based on CWT-Stacking Ensemble Learning Model
摘要
Abstract
Accurate estimation of soil moisture,salinity and pH value is essential for soil and water environment monitoring and sustainable land use in arid and semi-arid agricultural irrigation districts.Taking farmland and other vegetation-covered areas in the Hetao Irrigation District as the research objects,the first-order derivative,second-order derivative,continuum removal,standard normal variate and continuous wavelet transform(CWT)were adopted to preprocess raw spectra and enhance features.Five base learners,including PLSR,SVM,RF,XGBoost and LightGBM were selected,and three ensemble strategies of Stacking,Voting and Bagging were combined to construct comprehensive estimation models for soil moisture,salinity and pH value.The results showed that continuous wavelet transform performed the best in removing high-frequency noise and highlighting local features,with an applicability score of 4.7,which greatly improved the sensitivity of spectral data to soil moisture,salinity and pH value.Compared with single models,ensemble learning strategies effectively reduced the risk of overfitting and enhanced the generalization ability of models.Among all strategies,Stacking achieved the best overall performance,and the coefficients of determination R2 of the training sets for soil moisture,salinity and pH value were increased by 4.9%,4.7%and 5.1%,respectively.The optimal model constructed by CWT-Stacking realized high-precision estimation of soil moisture,salinity and pH value.The R2 of the validation sets reached 0.85,0.89 and 0.82,and the corresponding root mean square errors(RMSE)were2.26%,0.61g/kg and 0.30,respectively.The combination of advanced spectral transformation technology and complex ensemble learning algorithms can provide a high-precision and feasible technical means for the dynamic monitoring of large-scale soil health in arid and semi-arid regions.关键词
土壤水盐含量和pH值估计/地物光谱/集成学习/光谱变换/河套灌区Key words
soil water,salt content,and pH value estimation/ground object spectral/ensemble learning/spectral transformation/Hetao Irrigation District分类
农业科技引用本文复制引用
李瑞平,孙伟琦,元志辉,马小茗,赵建伟,阿莫尔,陈忠冉,宝路,王思楠..基于CWT-Stacking集成学习模型的灌区土壤水盐含量和pH值估算[J].农业机械学报,2026,57(20):94-107,14.基金项目
国家自然科学基金黄河水科学研究联合基金重点支持项目(U2443210)、内蒙古自治区科技计划项目(2025YFHH0166)和内蒙古自治区自然科学基金项目(2026MS0804) （U2443210）
