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

作者：Ning Li, Ziyu Ning, Miao Chen, Dongming Wu, Chengzhi Hao, Donghui Zhang, Rui Bai, Huiran Liu, Xin Chen, Wei Li, Wen Zhang, Yi-Cheng Chen, Qinfen Li, Lifu Zhang · 发表于：Remote Sensing · 年份：2022 · DOI：10.3390/rs14215466 · 被引用次数：40 · 研究领域：Water Quality and Pollution Assessment、Marine and coastal ecosystems、Fish Ecology and Management Studies

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 R2 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 po...