棉花学报2026，Vol.38Issue(2)：158-172,15.DOI:10.11963/hgjcs20260008
基于Sentinel-2影像和TPE优化堆叠集成模型估算棉花各生育时期叶片SPAD值估算
Estimation of SPAD values for cotton leaves at various growth stages based on Sentinel-2 imagery and TPE-optimized stacking algorithm
摘要
Abstract
[Objective]This study aimed to achieve rapid,large-area monitoring of cotton leaf SPAD values using remote sensing technology and to provide technical support for evaluating cotton growth conditions in the Aral reclamation area.[Methods]A total of 22 vegetation indices and color indices were extracted using the Google Earth Engine platform.The mutual information method,recursive feature elimination,and extreme gradient boosting(XGBoost)were separately applied to select spectral features at the seedling,squaring,and flowering and boll-setting stages.The feature-selection performance of the three methods was compared to construct an optimal feature subset for each growth stage and reduce feature redundancy.The tree-structured Parzen estimator(TPE)was used to determine the hyperparameters of K nearest neighbors(KNN),random forest(RF),and categorical boosting(CatBoost).An optimized stacking algorithm(OSA)model was subsequently constructed using the TPE-optimized KNN,RF,and CatBoost models as base learners to improve the accuracy and stability of SPAD value estimation at different cotton growth stages.[Results]When vegetation indices and color indices were jointly used as input variables,the feature combinations selected by XGBoost showed better overall performance.Using the feature subsets selected by XGBoost,the OSA models constructed with TPE-optimized base learners achieved the validation-set coefficients of determination(R2)of 0.805,0.850,and 0.881 at the seedling,squaring,and flowering and boll-setting stages,respectively.And the corresponding mean absolute percentage error(MAPE)values were 0.026,0.022,and 0.019,respectively.The model performed best at the flowering and boll-setting stage,with training-set R2 and MAPE values of 0.917 and 0.008,respectively.[Conclusion]For estimating cotton SPAD values in the Aral Reclamation Area,GRNDVI,GLI,RESAVI,NDVI,EXR,and TVI are recommended as input features at the seedling stage;GNDVI,VARI,GRNDVI,EXR,NDVI,and SI1 at the squaring stage;and OSAVI,LCI,NDI,GBNDVI,NDVI,and SAVI at the flowering and boll-setting stage.At all three growth stages,the OSA model constructed using TPE-optimized KNN,RF,and CatBoost models as base learners is recommended for SPAD value estimation.关键词
棉花/SPAD值/极限梯度提升算法/树结构Parzen估计器/优化堆叠集成模型Key words
cotton/SPAD value/extreme gradient boosting/tree-structured Parzen estimator/optimized stacking algorithm引用本文复制引用
洪国军,张灵,高日升,徐衡,张西梦,范振岐..基于Sentinel-2影像和TPE优化堆叠集成模型估算棉花各生育时期叶片SPAD值估算[J].棉花学报,2026,38(2):158-172,15.基金项目
国家自然科学基金地区科学基金(32560409 （）
61662064) （）
