Remote quantification of the trophic status of Chinese lakes
Abstract blue/red, green/red and red/red, which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (R 2 = 0.87, slope = = = 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Remote quantification of the trophic status of Chinese lakes ; volume:27 ; number:19 ; year:2023 ; pages:3581-3599 ; extent:19
Hydrology and earth system sciences ; 27, Heft 19 (2023), 3581-3599 (gesamt 19)
- Creator
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Li, Sijia
Xu, Shiqi
Song, Kaishan
Kutser, Tiit
Wen, Zhidan
Liu, Ge
Shang, Yingxin
Lyu, Lili
Tao, Hui
Wang, Xiang
Zhang, Lele
Chen, Fangfang
- DOI
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10.5194/hess-27-3581-2023
- URN
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urn:nbn:de:101:1-2023101204293298678351
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:56 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Li, Sijia
- Xu, Shiqi
- Song, Kaishan
- Kutser, Tiit
- Wen, Zhidan
- Liu, Ge
- Shang, Yingxin
- Lyu, Lili
- Tao, Hui
- Wang, Xiang
- Zhang, Lele
- Chen, Fangfang
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