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A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images

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Abstract

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI \(Rrs(560)\), \(Rrs(620)\), \(Rrs(681)\), and \(Rrs(709)\) was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI \(Rrs(490)\), \(Rrs(665)\), \(Rrs(673)\), and \(Rrs(754)\) were used for water type 1; \(Rrs(620)\), \(Rrs(865)\), and \(Rrs(885)\) were adopted for water type 2; and \(Rrs(620)\), \(Rrs(665)\), and \(Rrs(709)\) were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

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Acknowledgements

Thanks to the graduate student of remote sensing application of Nanjing Normal University, China, for their help in laboratory analysis.

Funding

This research is supported by the National Natural Science Foundation of China (grant numbers 42271341 and U2102207).

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Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Huaiqing Liu, Wenyu Liu, Heng Lyu, Yunmei Li, Fangfang Chen, Ying Zhao, Jiafeng Xu & Honglei Guo

  2. Co-Innovation Center for Sustainable Forestry in Southern China of Jiangsu Province, Key Laboratory of Soil and Water Conservation and Ecological Restoration of Jiangsu Province, Nanjing Forestry University, Nanjing, 210037, People’s Republic of China

    Jie Lin

  3. Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, People’s Republic of China

    Heng Lyu & Yunmei Li

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  1. Huaiqing Liu
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Contributions

Huaiqing Liu: writing – original draft, methodology, writing – review and editing. Wenyu Liu: data curation, investigation, writing – review and editing, formal analysis. Jie Lin: conceptualization, methodology, resources. Heng Lyu: conceptualization, writing – review and editing, funding acquisition. Yunmei Li: writing – review and editing, validation. Fangfang Chen: investigation, writing – review and editing. Ying Zhao: formal analysis. Jiafeng Xu: supervision, formal analysis, investigation. Honglei Guo: resources, investigation.

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Liu, H., Liu, W., Lin, J. et al. A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images. Environ Sci Pollut Res 30, 64203–64220 (2023). https://doi.org/10.1007/s11356-023-26876-8

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