Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-79-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-79-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 Jan 2022
Data description paper |
| 13 Jan 2022
| 13 Jan 2022
A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018
Hui Tao
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
School of Resources and Environment, University of the Chinese Academy of Sciences, Beijing, 100049, China
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
School of Environment and Planning, College of Urban Research and Planning, Liaocheng University, Liaocheng, 252000, China
Ge Liu
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Qiang Wang
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Zhidan Wen
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Pierre-Andre Jacinthe
Department of Earth Sciences, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA
Xiaofeng Xu
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Jia Du
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Yingxin Shang
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Sijia Li
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Zongming Wang
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Lili Lyu
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Junbin Hou
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Xiang Wang
Center of Remote Sensing and Geographic Information, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Dong Liu
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
Kun Shi
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
Baohua Zhang
School of Environment and Planning, College of Urban Research and Planning, Liaocheng University, Liaocheng, 252000, China
Hongtao Duan
CORRESPONDING AUTHOR
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
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Cited
14 citations as recorded by crossref.
- Retrieval of lake water surface albedo from Sentinel-2 remote sensing imagery J. Du et al. 10.1016/j.jhydrol.2022.128904
- Spatiotemporal Dynamics of Remote-Sensed Forel–Ule Index for Inland Waters Across China During the COVID-19 Pandemic L. Xu et al. 10.1109/JSTARS.2023.3298108
- Comparison of Machine Learning Algorithms for Estimating Global Lake Clarity With Landsat TOA Data Q. Wang et al. 10.1109/TGRS.2024.3400221
- Analysis of Spatio-Temporal Dynamics of Chinese Inland Water Clarity at Multiple Spatial Scales between 1984 and 2018 H. Tao et al. 10.3390/rs14205091
- Mapping water clarity in North American lakes and reservoirs using Landsat images on the GEE platform with the RGRB model Q. Wang et al. 10.1016/j.isprsjprs.2022.09.014
- Improving remote sensing retrieval of global ocean transparency with optical water classification T. Jia et al. 10.1016/j.ecolind.2022.109359
- Spatiotemporal variability at seasonal and interannual scales of terrestrial water variation over Tibetan Plateau from geodetic observations Y. Pan et al. 10.1080/10095020.2024.2331553
- Water surface albedo and its driving factors on the turbid lakes of Northeast China J. Du et al. 10.1016/j.ecolind.2023.109905
- Monitoring inland water via Sentinel satellite constellation: A review and perspective F. Zeng et al. 10.1016/j.isprsjprs.2023.09.011
- Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing G. Yulong et al. 10.1016/j.rse.2022.113045
- Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework Z. Zhao et al. 10.1016/j.jclepro.2024.142496
- The imprint of urbanization on vegetation in the ecologically fragile area: A case study from China’s Loess Plateau A. Zhao et al. 10.1016/j.ecolind.2023.110791
- Response of total suspended matter to natural and anthropogenic factors since 1990 in China's large lakes H. Tao et al. 10.1016/j.scitotenv.2023.164474
- Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China H. Tao et al. 10.1016/j.scitotenv.2022.157328
14 citations as recorded by crossref.
- Retrieval of lake water surface albedo from Sentinel-2 remote sensing imagery J. Du et al. 10.1016/j.jhydrol.2022.128904
- Spatiotemporal Dynamics of Remote-Sensed Forel–Ule Index for Inland Waters Across China During the COVID-19 Pandemic L. Xu et al. 10.1109/JSTARS.2023.3298108
- Comparison of Machine Learning Algorithms for Estimating Global Lake Clarity With Landsat TOA Data Q. Wang et al. 10.1109/TGRS.2024.3400221
- Analysis of Spatio-Temporal Dynamics of Chinese Inland Water Clarity at Multiple Spatial Scales between 1984 and 2018 H. Tao et al. 10.3390/rs14205091
- Mapping water clarity in North American lakes and reservoirs using Landsat images on the GEE platform with the RGRB model Q. Wang et al. 10.1016/j.isprsjprs.2022.09.014
- Improving remote sensing retrieval of global ocean transparency with optical water classification T. Jia et al. 10.1016/j.ecolind.2022.109359
- Spatiotemporal variability at seasonal and interannual scales of terrestrial water variation over Tibetan Plateau from geodetic observations Y. Pan et al. 10.1080/10095020.2024.2331553
- Water surface albedo and its driving factors on the turbid lakes of Northeast China J. Du et al. 10.1016/j.ecolind.2023.109905
- Monitoring inland water via Sentinel satellite constellation: A review and perspective F. Zeng et al. 10.1016/j.isprsjprs.2023.09.011
- Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing G. Yulong et al. 10.1016/j.rse.2022.113045
- Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework Z. Zhao et al. 10.1016/j.jclepro.2024.142496
- The imprint of urbanization on vegetation in the ecologically fragile area: A case study from China’s Loess Plateau A. Zhao et al. 10.1016/j.ecolind.2023.110791
- Response of total suspended matter to natural and anthropogenic factors since 1990 in China's large lakes H. Tao et al. 10.1016/j.scitotenv.2023.164474
- Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China H. Tao et al. 10.1016/j.scitotenv.2022.157328
Latest update: 16 Jul 2024
Short summary
During 1984–2018, lakes in the Tibetan-Qinghai Plateau had the clearest water (mean 3.32 ± 0.38 m), while those in the northeastern region had the lowest Secchi disk depth (SDD) (mean 0.60 ± 0.09 m). Among the 10 814 lakes with > 10 years of SDD results, 55.4 % and 3.5 % experienced significantly increasing and decreasing trends of SDD, respectively. With the exception of Inner Mongolia–Xinjiang, more than half of lakes in all the other regions exhibited a significant trend of increasing SDD.
During 1984–2018, lakes in the Tibetan-Qinghai Plateau had the clearest water (mean...
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