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Satellite and Machine Learning Monitoring of Optically Inactive Water Quality Variability in a Tropical River

文献类型： 外文期刊

作者： Li, Ning 1 ; Ning, Ziyu 1 ; Chen, Miao 1 ; Wu, Dongming 1 ; Hao, Chengzhi 6 ; Zhang, Donghui 7 ; Bai, Rui 9 ; Liu, Huiran 1 ; Chen, Xin 1 ; Li, Wei 1 ; Zhang, Wen 1 ; Chen, Yicheng 1 ; Li, Qinfen 1 ; Zhang, Lifu 7 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Environm & Plant Protect Inst, Haikou 571101, Hainan, Peoples R China

2.Hainan Danzhou Trop Agroecosyst Natl Observat & R, Danzhou 571737, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Low Carbon Green Agr Trop Reg China, Haikou 571101, Hainan, Peoples R China

4.Hainan Key Lab Trop Ecocircular Agr, Haikou 571101, Hainan, Peoples R China

5.Natl Long Term Expt Stn Agr Green Dev, Natl Agr Expt Stn Agr Environm, Danzhou 571737, Peoples R China

6.Hainan Prov Ecol & Environm Monitoring Ctr, Haikou 571126, Hainan, Peoples R China

7.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China

8.Tianjin Progoo Informat Technol Co Ltd, Progoo Res Inst, Tianjin 300380, Peoples R China

9.China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China

10.Hainan Climate Ctr, Haikou 570203, Hainan, Peoples R China

关键词： machine learning; multispectral remote sensing; optically inactive water quality monitoring; total nitrogen (TN); ammoniacal nitrogen (AN); total phosphorus (TP); tropical river

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 21 期

页码：

收录情况： SCI

摘要： Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R-2 value ranged between 0.44 and 0.67, and the RMSE was 0.03-0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013-2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.

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