Evaluating optically and non-optically active water quality and its response relationship to hydro-meteorology using multi-source data in Poyang Lake, China

Bolin Fu; Zhinan Lao; Yiyin Liang; Jun Sun; Xu He; Tengfang Deng; Wen He; Donglin Fan; Ertao Gao; Qinglin Hou

Ecological Indicators (Dec 2022)

Evaluating optically and non-optically active water quality and its response relationship to hydro-meteorology using multi-source data in Poyang Lake, China

Bolin Fu,
Zhinan Lao,
Yiyin Liang,
Jun Sun,
Xu He,
Tengfang Deng,
Wen He,
Donglin Fan,
Ertao Gao,
Qinglin Hou

Affiliations

Bolin Fu: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China; Corresponding authors at: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China (B. Fu) and Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China (W. He).
Zhinan Lao: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Yiyin Liang: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Jun Sun: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Xu He: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Tengfang Deng: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Wen He: Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China; Corresponding authors at: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China (B. Fu) and Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China (W. He).
Donglin Fan: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Ertao Gao: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China
Qinglin Hou: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; University Key Laboratory of Ecological Spatiotemporal Big Data Perception, Guangxi, Guilin 541006, China

Journal volume & issue: Vol. 145
p. 109675

Abstract

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Lakes potentially provide sensitive indicators of global climate change, but water pollution seriously affects the lake ecosystem. Therefore, mapping and monitoring water quality of lake has attracted extensive attention from scholars. However, estimating optically active (OAPs) and non-optically water quality parameters (NOAPs) and clarifying their response relationship with hydro-meteorology still face great challenges. To resolve these issues, this paper presents an approach for assessing OAPs (Chlorophyll a (Chla), Total suspended matter (TSM)) and NOAPs (total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH4-N), five-day biochemical oxygen demand (BOD5)) in Poyang Lake, the largest freshwater lake in China, by using ensemble learning (SEL), combing leave-one-out validation and machine learning (LOOCV-ML) algorithms with multi-source remote sensing images. We further quantitatively evaluated retrieval accuracy differences between OAPs and NOAPs using multispectral and hyperspectral images. Meanwhile, a novel automatic extraction method was proposed in this paper for mapping water bodies. We systematically explored the response relationship between water quality (OAP and NOAP) and hydro-meteorology. We found that NOAPs achieved higher estimation accuracy than OAPs. The LOOCV-XGBoost and LOOCV-GradientBoost provided higher inversion accuracy of OAP and NOAP than SEL, demonstrating the great potential of combining OHS-2A hyperspectral images with LOOCV-ML to estimate the water quality in inland lakes. We revealed that the estimation accuracy (MRE) of TN and TP was 1.09 %-18.03 % higher than NH4-N and BOD5. Finally, we clarified that water temperature had a significantly negative effect on NH4-N and BOD5 (Projected importance of variables (VIP) > 1, Coefficient 1, Coefficient > 1), further revealing that water temperature, water body area, wind speed, rainfall, mean relative humidity and sunshine hours were important divers of OAPs and NOAPs changes. These results provide a scientific basis for restoration and protection of water quality in lake ecosystems.

Published in Ecological Indicators

ISSN: 1470-160X (Print); 1872-7034 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-indicators

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