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

作者：Bolin Fu, Zhinan Lao, Yiyin Liang, Jun Sun, Xu He, Tengfang Deng, Wen He, Donglin Fan, Ertao Gao, Qinglin Hou · 发表于：Ecological Indicators · 年份：2022 · DOI：10.1016/j.ecolind.2022.109675 · 被引用次数：41 · 研究领域：Marine and coastal ecosystems、Water Quality Monitoring Technologies、Aquatic Ecosystems and Phytoplankton Dynamics

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 estim...