基于梯度提升机的喀斯特高原湖泊叶绿素a浓度遥感反演研究OA

[Objective]For karst plateau lakes,traditional remote sensing models face challenges of spectral signal mixing and insufficient fitting of nonlinear relationships due to high pH values(average>8.2),high suspended particulate matter(SPM>50 mg/L),and seasonal hydrological fluctuations.The Pingzhai Reservoir,a typical karst plateau lake,is taken as the study area,and the aim is to achieve high-precision remote sensing inversion of chlorophyll-a(Chla)concentration in such water bodies.[Methods]Sentinel-2 MSI Level 2A imagery(with a spatial resolution of 10～60 m)and data from 40 field sampling points were used.A"shortwave-sensitive single-band+cross-band linear combination"feature engineering strategy was proposed to screen highly sensitive spectral features(including single bands B3,B1,B2,B5,B4 and linear combinations such as B1+B3,B2+B3,B3+B5).Additionally,a remote sensing inversion framework for Chla concentration was constructed by leveraging the efficient fitting capability of the Gradient Boosting Machine(GBM)model for nonlinear relationships.The fitting capability of the model for the nonlinear relationship between spectral features and Chla concentration was enhanced through data preprocessing and hyperparameter optimization.[Results]The result showed that the constructed GBM model achieved an inversion accuracy with a coefficient of determination(R2)of 0.908,root mean square error(RMSE)of 0.731 μg/L,and mean absolute error(MAE)of 0.529 μg/L,representing a 62%improvement in accuracy compared to the traditional single-band linear model(B3 band,R2=0.560 7).The Chla concentration in Pingzhai Reservoir showed significant seasonal characteristics,with average values of 10.22 μg/L in summer,2.46 μg/L in winter,6.01 μg/L in spring,and 5.88 μg/L in autumn.Its variation was primarily driven by water temperature(correlation coefficient r=0.730)and total organic carbon(TOC,correlation coefficient r=0.783),and the negative feedback mechanism of total nitrogen bioavailability in high pH environments reflected the distinctive characteristics of karst water bodies.[Conclusion]The findings provide a technical solution of"sensitive band combination+machine learning"for high-precision remote sensing monitoring of Chla concentration in karst plateau lakes,while also offering scientific support for reservoir water quality management and ecological protection.