Comment on hess-2022-91 Article Swipe
Sijia Li
,
Shiqi Xu
,
Kaishan Song
,
Tiit Kutser
,
Zhidan Wen
,
Ge Liu
,
Yingxin Shang
,
Lili Lyu
,
Hui Tao
,
Wang Xiang
,
Lele Zhang
,
Fangfang Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/hess-2022-91-rc1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/hess-2022-91-rc1
Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as 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 (R2 = 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 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 the XGBoost to map TSI in 2019–2020 with good quality Sentinel-2 Level-1C images embedded in ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10-m TSI products from investigated 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate, and anthropogenic activities. The methodological framework proposed herein could serve as a useful resource toward a continuous, long-term, and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.
Related Topics
Concepts
Eutrophication
Environmental science
Water quality
Lake ecosystem
Trophic level
Ecosystem
Trophic state index
Multispectral image
Cartography
Environmental resource management
Hydrology (agriculture)
Remote sensing
Water resource management
Physical geography
Ecology
Geography
Nutrient
Engineering
Biology
Geotechnical engineering
Metadata
- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.5194/hess-2022-91-rc1
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283262413
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283262413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/hess-2022-91-rc1Digital Object Identifier
- Title
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Comment on hess-2022-91Work title
- Type
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peer-reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-06-21Full publication date if available
- Authors
-
Sijia Li, Shiqi Xu, Kaishan Song, Tiit Kutser, Zhidan Wen, Ge Liu, Yingxin Shang, Lili Lyu, Hui Tao, Wang Xiang, Lele Zhang, Fangfang ChenList of authors in order
- Landing page
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https://doi.org/10.5194/hess-2022-91-rc1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/hess-2022-91-rc1Direct OA link when available
- Concepts
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Eutrophication, Environmental science, Water quality, Lake ecosystem, Trophic level, Ecosystem, Trophic state index, Multispectral image, Cartography, Environmental resource management, Hydrology (agriculture), Remote sensing, Water resource management, Physical geography, Ecology, Geography, Nutrient, Engineering, Biology, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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