Articles | Volume 27, issue 19
https://doi.org/10.5194/hess-27-3581-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3581-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Oct 2023
Research article |
| 09 Oct 2023
| 09 Oct 2023
Remote quantification of the trophic status of Chinese lakes
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Key Laboratory of Space Ocean Remote Sensing and Application,
Ministry of Natural Resources, National Satellite Ocean Application Service,
Beijing 100081, China
Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618
Tallinn, Estonia
Shiqi Xu
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Kaishan Song
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Tiit Kutser
Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618
Tallinn, Estonia
Zhidan Wen
CORRESPONDING AUTHOR
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Ge Liu
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Yingxin Shang
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Lili Lyu
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Hui Tao
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Xiang Wang
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Lele Zhang
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
Fangfang Chen
Northeast Institute of Geography and Agroecology, Chinese Academy of
Sciences, Changchun 130102, China
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Cited
13 citations as recorded by crossref.
- Diagnóstico funcional de cuerpos lóticos y lénticos usando el Índice de Valoración Trófica-Ecológica (IVT-Ecológica): caso río Orinoco y laguna de Castillero, Venezuela A. Márquez & J. Acosta Núñez
- Hydrodynamic and water quality simulation of Yangzonghai Lake, Southwest China, using the two-dimensional CE-QUAL-W2 model C. Tang et al.
- Small lakes, big improvements: a multi-decadal assessment of policy and climate impacts on chlorophyll-a in China S. Li et al.
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review T. Miller et al.
- Calcium‒phosphorus coprecipitation induced by Hydrilla verticillata photosynthesis: response to elevated phosphorus concentration and regulation by light intensity, inorganic carbon and calcium levels Z. Li et al.
- Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing X. Zhou et al.
- A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality M. Diganta et al.
- Regional heterogeneity in water pollution risk-economic development dynamics: a grid-scale analysis and impact factor exploration in China Y. Ding et al.
- Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions L. Lai et al.
- Spatial dynamics of pCO2 and CO2 emissions from eutrophic lakes X. Wang et al.
- Remote sensing inversion of lake water quality and its response to human activities in multi-scale buffer zones M. Wang et al.
- A data-driven methodology for calculating key eutrophication parameters in water systems M. Elenes Alderete et al.
- Ensemble-based mapping and trophic characterization of lentic water bodies in the Middle Ganga Basin under climate and land use change P. Singh & B. Yadav
13 citations as recorded by crossref.
- Diagnóstico funcional de cuerpos lóticos y lénticos usando el Índice de Valoración Trófica-Ecológica (IVT-Ecológica): caso río Orinoco y laguna de Castillero, Venezuela A. Márquez & J. Acosta Núñez
- Hydrodynamic and water quality simulation of Yangzonghai Lake, Southwest China, using the two-dimensional CE-QUAL-W2 model C. Tang et al.
- Small lakes, big improvements: a multi-decadal assessment of policy and climate impacts on chlorophyll-a in China S. Li et al.
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review T. Miller et al.
- Calcium‒phosphorus coprecipitation induced by Hydrilla verticillata photosynthesis: response to elevated phosphorus concentration and regulation by light intensity, inorganic carbon and calcium levels Z. Li et al.
- Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing X. Zhou et al.
- A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality M. Diganta et al.
- Regional heterogeneity in water pollution risk-economic development dynamics: a grid-scale analysis and impact factor exploration in China Y. Ding et al.
- Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions L. Lai et al.
- Spatial dynamics of pCO2 and CO2 emissions from eutrophic lakes X. Wang et al.
- Remote sensing inversion of lake water quality and its response to human activities in multi-scale buffer zones M. Wang et al.
- A data-driven methodology for calculating key eutrophication parameters in water systems M. Elenes Alderete et al.
- Ensemble-based mapping and trophic characterization of lentic water bodies in the Middle Ganga Basin under climate and land use change P. Singh & B. Yadav
Saved (final revised paper)
Latest update: 04 May 2026
Short summary
1. Blue/red and green/red Rrs(λ) are sensitive to lake TSI. 2. Machine learning algorithms reveal optimum performance of TSI retrieval. 3. An accurate TSI model was achieved by MSI imagery data and XGBoost. 4. Trophic status in five limnetic regions was qualified. 5. The 10m TSI products were first produced in 555 typical lakes in China.
1. Blue/red and green/red Rrs(λ) are sensitive to lake TSI. 2. Machine learning algorithms...
