Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties

Shen, Ming1; Luo, Juhua1; Cao, Zhigang1; Xue, Kun1; Qi, Tianci1,2; Ma, Jinge1,2; Liu, Dong1; Song, Kaishan3; Feng, Lian4; Duan, Hongtao1

2022-12-01

10.1016/j.jhydrol.2022.128685

JOURNAL OF HYDROLOGY   影响因子和分区

0022-1694

1879-2707

A robust and reliable chlorophyll-a (Chla) concentration algorithm is still lacking for optically complex waters due to the lack of understanding of the bio-optical process. Machine learning approaches, which excel at detecting potential complex nonlinear relationships, provide opportunities to estimate Chla accurately for optically complex waters. However, the uncertainties in atmospheric correction (AC) may be amplified in different Chla algorithms. Here, we aim to select one state-of-the-art algorithm or establish a new algorithm based on machine learning approaches that less sensitive to AC uncertainties. Firstly, nine state-of-the-art empirical, semianalytical, and optical water types (OWT) classification-based Chla algorithms were imple-mented. These existing algorithms showed good performance by using in situ database, however, failed in actual OLCI applications due to their sensitivity to AC uncertainties. Thus, four popular machine learning approaches (random forest regression (RFR), extreme gradient boosting (XGBoost), deep neural network (DNN), and support vector regression (SVR)) were then employed. Among them, the "RFR-Chla" model performed the best and showed less sensitivity to AC uncertainties. Finally, the Chla spatiotemporal variations in 163 major lakes across eastern China were mapped from OLCI between May 2016 and April 2020 using the proposed RFR-Chla model. Generally, the lakes in eastern China are severely eutrophic, with an average Chla concentration of 33.39 +/- 6.95 mu g/L. Spatially, Chla in the south of eastern China was significantly higher than those in northern lakes. Seasonally, Chla was high in the summer and autumn and low in the spring and winter. This study provides a reference for water quality monitoring in turbid inland waters suffering certain AC uncertainties and supports aquatic management and SDG 6 reporting.

Chlorophyll-a Machine learning Eutrophication Turbid waters

SCI

英语

其他

National Natural Science Foundation of China["42201403","U2243205","42271377","41971309","41901299"] ; Natural Science Foundation of Jiangsu Province[BK20221159]

Engineering ; Geology ; Water Resources

Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources

WOS:000895770900002

ELSEVIER

ENGINEERING

Web of Science

1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, 73 East Beijing Rd, Nanjing 210008, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China
4.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China

Shen, Ming,Luo, Juhua,Cao, Zhigang,et al. Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties[J]. JOURNAL OF HYDROLOGY,2022,615.

Shen, Ming.,Luo, Juhua.,Cao, Zhigang.,Xue, Kun.,Qi, Tianci.,...&Duan, Hongtao.(2022).Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties.JOURNAL OF HYDROLOGY,615.

Shen, Ming,et al."Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties".JOURNAL OF HYDROLOGY 615(2022).