Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

被引:22
|
作者
Xu, Shiqi [1 ]
Li, Sijia [1 ]
Tao, Zui [2 ]
Song, Kaishan [1 ]
Wen, Zhidan [1 ]
Li, Yong [1 ]
Chen, Fangfang [1 ]
机构
[1] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China
[2] Chinese Acad Sci, Inst Air & Space Informat Innovat, Beijing 100094, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
chlorophyll-a; GF-6; machine learning; Xingkai Lake; SEMIANALYTICAL MODEL; INLAND WATERS; ALGAL BLOOMS; ALGORITHMS; INDEX; TRENDS; COLOR; RED;
D O I
10.3390/rs14205136
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 mu g/L, MAE = 0.57 mu g/L, slope = 0.94, and R-2 = 0.98; Validation: N = 40, RMSE = 2.12 mu g/L, MAE = 1.58 mu g/L, slope = 0.91, R-2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.
引用
收藏
页数:17
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