Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3517-2022
© Author(s) 2022. 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-26-3517-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2022
Research article |
| 08 Jul 2022
| 08 Jul 2022
Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020
Xidong Chen
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Xiao Zhang
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Junsheng Li
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Shenglei Wang
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yuan Gao
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China
Jun Mi
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data, 17, 4039–4062, https://doi.org/10.5194/essd-17-4039-2025, https://doi.org/10.5194/essd-17-4039-2025, 2025
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with a fine classification system, which contains 30 land-cover subcategories and achieves sufficient performance on a global scale.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data, 17, 4039–4062, https://doi.org/10.5194/essd-17-4039-2025, https://doi.org/10.5194/essd-17-4039-2025, 2025
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with a fine classification system, which contains 30 land-cover subcategories and achieves sufficient performance on a global scale.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
Atmos. Meas. Tech., 18, 3647–3667, https://doi.org/10.5194/amt-18-3647-2025, https://doi.org/10.5194/amt-18-3647-2025, 2025
Short summary
Short summary
TanSat-2 is designed for global carbon monitoring, offering high-resolution dual-band observations of solar-induced chlorophyll fluorescence – a key indicator of photosynthesis. Simulations show its data processing can retrieve fluorescence with high accuracy. These results suggest TanSat-2 will enhance global tracking of the carbon cycle and vegetation health, providing valuable insights for climate change research.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-213, https://doi.org/10.5194/essd-2025-213, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-94, https://doi.org/10.5194/essd-2025-94, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Understanding plant sunlight absorption is crucial for tracking global ecosystem health. We developed a 1995–2023 dataset that enhances satellite-based plant activity measurements by resolving data inconsistencies and improving resolution. Using advanced modeling, we harmonized signals from multiple satellites, cutting errors by 45 %. This offers clearer global photosynthesis trends, aiding climate research and vegetation monitoring.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3672, https://doi.org/10.5194/egusphere-2024-3672, 2025
Short summary
Short summary
The Russia-Ukraine war in 2022 severely disrupted Ukraine’s economy, with significant reductions in industrial, transportation, and residential activities. Our research used satellite data to track changes in nitrogen oxide emissions, a key indicator of human activity, during the war. We found a 28 % decline in emissions, which was twice of the decrease caused by the COVID-19 pandemic. This study highlights how modern warfare can deeply impact both the environment and economic stability.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
Short summary
Short summary
To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Linan Guo, Hongxing Zheng, Yanhong Wu, Lanxin Fan, Mengxuan Wen, Junsheng Li, Fangfang Zhang, Liping Zhu, and Bing Zhang
Earth Syst. Sci. Data, 14, 3411–3422, https://doi.org/10.5194/essd-14-3411-2022, https://doi.org/10.5194/essd-14-3411-2022, 2022
Short summary
Short summary
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an indicator of climate change. By combining the strengths of satellites and models, we produced an integrated dataset on daily LSWT of 160 large lakes across the Tibetan Plateau (TP) for the period 1978–2017. LSWT increased significantly at a rate of 0.01–0.47° per 10 years. The dataset can contribute to research on water and heat balance changes and their ecological effects in the TP.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Cited articles
Barnes, A.: Muddy Waters: The Public Health Risks and Sustainability of Bottled Water in China, Vermont Law Review, 38, 971–1024, 2014.
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and Enrich-Prast, A.:
Freshwater Methane Emissions Offset the Continental Carbon Sink, Science, 331, 50, https://doi.org/10.1126/science.1196808, 2011.
Biggs, J., von Fumetti, S., and Kelly-Quinn, M.:
The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers, Hydrobiologia, 793, 3–39, https://doi.org/10.1007/s10750-016-3007-0, 2017.
Brönmark, C. and Hansson, L.:
Environmental issues in lakes and ponds: current state and perspectives, Environ. Conserv., 29, 290–306, 2002.
Cao, Z., Duan, H., Feng, L., Ma, R., and Xue, K.: Climate-and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales, Remote Sens. Environ., 192, 98–113, https://doi.org/10.1016/j.rse.2017.02.007, 2017.
Carlson, R. E.: A trophic state index for lakes, Limnol. Oceanogr., 22, 361–369, https://doi.org/10.4319/lo.1977.22.2.0361, 1977.
Chen, X. and Liu, L.: The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020), Zenodo [data set], https://doi.org/10.5281/zenodo.5734071, 2021a.
Chen, X. and Liu, L.: Natural Lake Dataset in China (NLD_China), Zenodo [data set], https://doi.org/10.5281/zenodo.5734166, 2021b.
Chen, X., Liu, L., Zhang, X., Li, J., and Song, K.: An assessment of water color for inland water in China using a Landsat 8-derived Forel-Ule index and the Google Earth Engine platform, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 99, 5773–5785, https://doi.org/10.1109/JSTARS.2021.3085411, 2021.
CIE: Commission Internationale de l'Éclairage Proceedings, Cambridge University Press, Cambridge, UK, 1932.
Cuffney, T. F., Meador, M. R., Porter, S. D., and Gurtz, M. E.:
Responses of Physical, Chemical, and Biological Indicators of Water Quality to a Gradient of Agricultural Land Use in the Yakima River Basin, Washington, Environ. Monit. Assess., 64, 259–270, https://doi.org/10.1023/A:1006473106407, 2000.
Dai, Y., Feng, L., Hou, X., and Tang, J.: An automatic classification algorithm for submerged aquatic vegetation in shallow lakes using Landsat imagery, Remote Sens. Environ., 260, 112459, https://doi.org/10.1016/j.rse.2021.112459, 2021.
Dona, C., Sanchez, J. M., Caselles, V., Dominguez, J. A., and Camacho, A.:
Empirical Relationships for Monitoring Water Quality of Lakes and Reservoirs Through Multispectral Images, IEEE J. Sel. Top. Appl., 7, 1632–1641, https://doi.org/10.1109/JSTARS.2014.2301295, 2014.
Downing, J. A., Cole, J. J., Duarte, C. M., Middelburg, J. J., Melack, J. M., Prairie, Y. T., Kortelainen, P., Striegl, R. G., McDowell, W. H., and Tranvik, L. J.:
Global abundance and size distribution of streams and rivers, Inland Waters, 2, 229–236, https://doi.org/10.5268/iw-2.4.502, 2012.
Feng, L., Hou, X., and Zheng, Y.:
Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations, Remote Sens. Environ., 221, 675–686, https://doi.org/10.1016/j.rse.2018.12.007, 2019.
Garaba, S., Friedrichs, A., Voß, D., and Zielinski, O.:
Classifying Natural Waters with the Forel-Ule Colour Index System: Results, Applications, Correlations and Crowdsourcing, Int. J. Env. Res. Pub. He., 12, 16096–16109, https://doi.org/10.3390/ijerph121215044, 2015.
Garnesson, P., Mangin, A., Fanton d'Andon, O., Demaria, J., and Bretagnon, M.:
The CMEMS GlobColour chlorophyll a product based on satellite observation: multi-sensor merging and flagging strategies, Ocean Sci., 15, 819–830, https://doi.org/10.5194/os-15-819-2019, 2019.
Gomez, C., White, J. C., and Wulder, M. A.:
Optical remotely sensed time series data for land cover classification: A review, ISPRS J. Photogramm., 116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.:
Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Griffiths, P., van der Linden, S., Kuemmerle, T., and Hostert, P.:
Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping, IEEE J. Sel. Top. Appl., 6, 2088–2101, https://doi.org/10.1109/jstars.2012.2228167, 2013.
Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V. C., Abrudan, I. V., Lieskovsky, J., Munteanu, C., Ostapowicz, K., and Hostert, P.:
Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites, Remote Sens. Environ., 151, 72–88, https://doi.org/10.1016/j.rse.2013.04.022, 2014.
Guo, L.:
Ecology – Doing battle with the green monster of Taihu Lake, Science, 317, 1166–1166, https://doi.org/10.1126/science.317.5842.1166, 2007.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., and Hobart, G. W.:
An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites, Remote Sens. Environ., 158, 220–234, https://doi.org/10.1016/j.rse.2014.11.005, 2015.
Hu, C.:
A novel ocean color index to detect floating algae in the global oceans, Remote Sens. Environ., 113, 2118–2129, https://doi.org/10.1016/j.rse.2009.05.012, 2009.
Hu, H.:
Population distribution, regionalization, and prospects in China, Acta Geographica Sinica, 45, 139–145, 1990.
Islam, M. M.:
Analyses of ASTER and Spectroradiometer Data with in Situ Measurements for Turbidity and Transparency Study of Lake Abashiri, International Journal of Geoinformatics, 87, 76–77, 2006.
Kendall, M. G.:
Rank Correlation Methods, Brit. J. Psychol., 25, 86–91, https://doi.org/10.1111/j.2044-8295.1934.tb00727.x, 1990.
Kloiber, S. N., Brezonik, P. L., Olmanson, L. G., and Bauer, M. E.:
A procedure for regional lake water clarity assessment using Landsat multispectral data, Remote Sens. Environ., 82, 38–47, https://doi.org/10.1016/s0034-4257(02)00022-6, 2002.
Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., and Lafaye, M.:
Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, Remote Sens. Environ., 106, 66–74, https://doi.org/10.1016/j.rse.2006.07.012, 2007.
Lee, Z., Arnone, R., Boyce, D., Franz, B., and Wilson, C.: Global Water Clarity: Continuing a Century-Long Monitoring, Marine Sci. Facult. Publ., 99, 1984, https://doi.org/10.1029/2018EO097251, 2018.
Li, J., Wang, S., Wu, Y., Zhang, B., Chen, X., Zhang, F., Shen, Q., Peng, D., and Tian, L.:
MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012, Int. J. Digit. Earth, 9, 788–805, https://doi.org/10.1080/17538947.2016.1139637, 2016.
Li, N., Shi, K., Zhang, Y., Gong, Z., Peng, K., Zhang, Y., and Zha, Y.:
Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance, Remote Sens.-Basel, 11, 177, https://doi.org/10.3390/rs11020177, 2019.
Li, Y., Shi, K., Zhang, Y., Zhu, G., Zhang, Y., Wu, Z., Liu, M., Guo, Y., and Li, N.: Analysis of water clarity decrease in Xin’anjiang Reservoir, China, from 30-Year Landsat TM, ETM plus, and OLI observations, J. Hydrol., 590, 125476, https://doi.org/10.1016/j.jhydrol.2020.125476, 2020.
Li, Z., Cao, Y., Tang, J., Wang, Y., Duan, Y., Jiang, Z., and Qu, Y.:
Relationships between Temporal and Spatial Changes in Lakes and Climate Change in the Saline-Alkali Concentrated Distribution Area in the Southwest of Songnen Plain, Northeast China, from 1985 to 2015, Water, 12, 3557, https://doi.org/10.3390/w12123557, 2020.
Liu, C., Zhu, L., Li, J., Wang, J., Ju, J., Qiao, B., Ma, Q., and Wang, S.:
The increasing water clarity of Tibetan lakes over last 20 years according to MODIS data, Remote Sens. Environ., 253, 112199, https://doi.org/10.1016/j.rse.2020.112199, 2021.
Liu, D., Duan, H., Loiselle, S., Hu, C., Zhang, G., Li, J., Yang, H., Thompson, J. R., Cao, Z., Shen, M., Ma, R., Zhang, M., and Han, W.: Observations of water transparency in China's lakes from space, International Journal of Applied Earth Observations and Geoinformation, 92, 102187, https://doi.org/10.1016/j.rse.2020.111950 2020.
Liu, L., Zhang, X., Gao, Y., Chen, X., Xie, S., and Mi, J.: Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects, J. Remote Sens., 2021, 38, https://doi.org/10.34133/2021/5289697, 2021.
Liu, X., Lee, Z., Zhang, Y., Lin, J., and Sun, Z.: Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data, Remote Sens.-Basel, 11, 2226, https://doi.org/10.3390/rs11192226, 2019.
Liu, Y., Chen, C., and Li, Y.:
Differentiation regularity of urban-rural equalized development at prefecture-level city in China, J. Geogr. Sci., 25, 1075–1088, https://doi.org/10.1007/s11442-015-1220-9, 2015.
Ma, H., Xu, J., and Wang, P.: Water Resource Utilization and China's Urbanization, Resources Science, 36, 334–341, 2014.
Ma, R., Yang, G., Duan, H., Jiang, J., Wang, S., Feng, X., Li, A., Kong, F., Xue, B., Wu, J., and Li, S.:
China's lakes at present: Number, area and spatial distribution, Sci. China-Earth Sci., 54, 283–289, https://doi.org/10.1007/s11430-010-4052-6, 2011.
Mann, H. B.:
Non-parametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
McCullough, I. M., Loftin, C. S., and Sader, S. A.:
Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity, Remote Sens. Environ., 123, 109–115, https://doi.org/10.1016/j.rse.2012.03.006, 2012.
Ministry of Ecology and Environment of the People's Republic of China: Report on the State of the Ecology and Environment in China, Ministry of Ecology and Environment of the People's Republic of China, Beijing, http://english.mee.gov.cn/Resources/Reports/, last access: 30 December 2020.
Murshed, M. F., Aslam, Z., Lewis, R., Chow, C., Wang, D., Drikas, M., and Leeuwen, J. V.:
Changes in the quality of river water before, during and after a major flood event associated with a La Nia cycle and treatment for drinking purposes, J. Environ. Sci., 26, 1985–1993, https://doi.org/10.1016/j.jes.2014.08.001, 2014.
Novoa, S., Wernand, M. R., and van der Woerd, H. J.: The Forel-Ule scale revisited spectrally: preparation protocol, transmission measurements and chromaticity, J. Eur. Opt. Soc.-Rapid, 8, 13057, https://doi.org/10.2971/jeos.2013.13057, 2013.
Odermatt, D., Gitelson, A., Brando, V. E., and Schaepman, M.:
Review of constituent retrieval in optically deep and complex waters from satellite imagery, Remote Sens. Environ., 118, 0–126, https://doi.org/10.1016/j.rse.2011.11.013, 2012.
Olmanson, L., Bauer, M., and Brezonik, P.:
A 20-year Landsat water clarity census of Minnesota's 10,000 lakes, Remote Sens. Environ., 112, 4086–4097, https://doi.org/10.1016/j.rse.2007.12.013, 2008.
Olmanson, L. G., Brezonik, P. L., Finlay, J. C., and Bauer, M. E.:
Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes, Remote Sens. Environ., 185, 119–128, https://doi.org/10.1016/j.rse.2016.01.007, 2016.
Page, B. P., Olmanson, L. G., and Mishra, D. R.:
A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems, Remote Sens. Environ., 231, 111284, https://doi.org/10.1016/j.rse.2019.111284, 2019.
Palmer, S. C. J., Kutser, T., and Hunter, P. D.:
Remote sensing of inland waters: Challenges, progress and future directions, Remote Sens. Environ., 157, 1–8, https://doi.org/10.1016/j.rse.2014.09.021, 2015.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.:
High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Pitarch, J., van der Woerd, H. J., Brewin, R. J. W., and Zielinski, O.:
Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations, Remote Sens. Environ., 231, https://doi.org/10.1016/j.rse.2019.111249, 2019.
Racetin, I., Krtalic, A., Srzic, V., and Zovko, M.: Characterization of short-term salinity fluctuations in the Neretva River Delta situated in the southern Adriatic Croatia using Landsat-5 TM, Ecol. Indic., 110, 105924, https://doi.org/10.1016/j.ecolind.2019.105924, 2020.
Shen, M., Duan, H., Cao, Z., Xue, K., Qi, T., Ma, J., Liu, D., Song, K., Huang, C., and Song, X.: Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation, Remote Sens. Environ., 247, 111950, https://doi.org/10.1016/j.rse.2020.111950, 2020.
Shi, K., Zhang, Y., Zhu, G., Qin, B., and Pan, D.:
Deteriorating water clarity in shallow waters: Evidence from long term MODIS and in-situ observations, Int. J. Appl. Earth Obs., 68, 287–297, https://doi.org/10.1016/j.jag.2017.12.015, 2018.
Singh, S. P. and Singh, P.:
Effect of temperature and light on the growth of algae species: A review, Renew. Sust. Energ. Rev., 50, 431–444, https://doi.org/10.1016/j.rser.2015.05.024, 2015.
Song, K., Wen, Z., Xu, Y., Yang, H., Lyu, L., Zhao, Y., Fang, C., Shang, Y., and Du, J.:
Dissolved carbon in a large variety of lakes across five limnetic regions in China, J. Hydrol., 563, 143–154, https://doi.org/10.1016/j.jhydrol.2018.05.072, 2018.
Song, K., Liu, G., Wang, Q., Wen, Z., Lyu, L., Du, Y., Sha, L., and Fang, C.:
Quantification of lake clarity in China using Landsat OLI imagery data, Remote Sens. Environ., 243, 111800, https://doi.org/10.1016/j.rse.2020.111800, 2020.
Steyerberg, E. W.:
Regression Modeling Strategies: With Applications, to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, Biometrics, 72, 1006–1007, https://doi.org/10.1007/978-3-319-19425-7, 2016.
USGS: Preliminary Assessment of the Value of Landsat 7 ETM+ Data Following Scan Line Corrector Malfunction, US Geological Survey, Sioux Falls, SD, https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/SLC_off_Scientific_Usability.pdf (last access: 20 December 2018), 2003.
van der Woerd, H. J. and Wernand, M. R.:
True Colour Classification of Natural Waters with Medium-Spectral Resolution Satellites: SeaWiFS, MODIS, MERIS and OLCI, Sensors, 15, 25663–25680, https://doi.org/10.3390/s151025663, 2015.
van der Woerd, H. J. and Wernand, M. R.: Hue-Angle Product for Low to Medium Spatial Resolution Optical Satellite Sensors, Remote Sens.-Basel, 10, 180, https://doi.org/10.3390/rs10020180, 2018.
Wang, S.:
Large-scale and Long-time Water Quality Remote Sensing Monitoring over Lakes Based on Water Color Index, University of Chinese Academy of Sciences, Beijing, 2018.
Wang, S., Li, J., Zhang, B., Spyrakos, E., Tyler, A. N., Shen, Q., Zhang, F., Kutser, T., Lehmann, M. K., Wu, Y., and Peng, D.:
Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index, Remote Sens. Environ., 217, 444–460, https://doi.org/10.1016/j.rse.2018.08.026, 2018.
Wang, S., Li, J., Zhang, B., Lee, Z., Spyrakos, E., Feng, L., Liu, C., Zhao, H., Wu, Y., Zhu, L., Jia, L., Wan, W., Zhang, F., Shen, Q., Tyler, A. N., and Zhang, X.: Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS, Remote Sens. Environ., 247, 111949, https://doi.org/10.1016/j.rse.2020.111949, 2020.
Wang, S., Li, J., Zhang, W., Cao, C., and Zhang, B.: A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000–2018, Scientific Data, 8, 26, https://doi.org/10.1038/s41597-021-00807-z, 2021.
Wang, X. and Yang, W.:
Water quality monitoring and evaluation using remote-sensing techniques in China: A systematic review, Ecosystem Health and Sustainability, 5, 47–56, https://doi.org/10.1080/20964129.2019.1571443, 2019.
White, J. C., Wulder, M. A., Hobart, G. W., Luther, J. E., Hermosilla, T., Griffiths, P., Coops, N. C., Hall, R. J., Hostert, P., Dyk, A., and Guindon, L.:
Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science, Can. J. Remote Sens., 40, 192–212, https://doi.org/10.1080/07038992.2014.945827, 2014.
Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y.: Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine, Remote Sens.-Basel, 11, 3023, https://doi.org/10.3390/rs11243023, 2019.
Xue, K., Ma, R., Duan, H., Shen, M., Boss, E., and Cao, Z.:
Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China's three largest freshwater lakes, Remote Sens. Environ., 225, 328–346, https://doi.org/10.1016/j.rse.2019.03.006, 2019.
Yin, Z., Li, J., Liu, Y., Xie, Y., and Zhang, B.:
Water clarity changes in Lake Taihu over 36 years based on Landsat TM and OLI observations, Int. J. Appl. Earth Obs., 102, 102457, https://doi.org/10.1016/j.jag.2021.102457, 2021.
Yu, R. and Zhai, P.: More frequent and widespread persistent compound drought and heat event observed in China, Scient. Rep., 10, 14576, https://doi.org/10.1038/s41598-020-71312-3, 2020.
Yuan, Z., Liang, C., and Li, D.:
Urban stormwater management based on an analysis of climate change: A case study of the Hebei and Guangdong provinces, Landscape Urban Plan., 177, 217–226, https://doi.org/10.1016/j.landurbplan.2018.04.003, 2018.
Zhang, G., Yao, T., Chen, W., Zheng, G., Shum, C. K., Yang, K., Piao, S., Sheng, Y., Yi, S., Li, J., O'Reilly, C. M., Qi, S., Shen, S. S. P., Zhang, H., and Jia, Y.: Regional differences of lake evolution across China during 1960s-2015 and its natural and anthropogenic causes, Remote Sens. Environ., 221, 386–404, https://doi.org/10.1016/j.rse.2018.11.038, 2019.
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., and Zhang, B.:
Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform, Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, 2020.
Zhang, X., Liu, L., Chen, X., Gao, Y., and Jiang, M.: Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta, Journal of Remote Sensing, 2021, 9873816, https://doi.org/10.34133/2021/9873816, 2021.
Zhang, Y., Zhang, Y., Shi, K., Zhou, Y., and Li, N.: Remote sensing estimation of water clarity for various lakes in China, Water Res., 192, 116844, https://doi.org/10.1016/j.watres.2021.116844, 2021.
Zhou, Q. C., Wang, W. L., Huang, L. C., Zhang, Y. L., Qin, J., Li, K. D., and Chen, L.: Spatial and temporal variability in water transparency in Yunnan Plateau lakes, China, Aquat. Sci., 81, 36, https://doi.org/10.1007/s00027-019-0632-5, 2019.
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and...
