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, 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, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
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.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Xiaojin Qian, Liangyun Liu, Holly Croft, and Jingming Chen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-228, https://doi.org/10.5194/bg-2019-228, 2019
Preprint withdrawn
Short summary
Short summary
The leaf maximum carboxylation rate (Vcmax) is a key photosynthesis parameter. We attempt to investigate whether a universal and stable relationship exists between leaf Vcmax25 and chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. The results confirm that leaf chlorophyll can be a reliable proxy for estimating Vcmax25, providing an operational approach for the global mapping of Vcmax25 across different plant types.
M. Gao and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 359–362, https://doi.org/10.5194/isprs-archives-XLII-3-359-2018, https://doi.org/10.5194/isprs-archives-XLII-3-359-2018, 2018
Related subject area
Subject: Rivers and Lakes | Techniques and Approaches: Remote Sensing and GIS
High-resolution automated detection of headwater streambeds for large watersheds
Remote quantification of the trophic status of Chinese lakes
Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data
Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning
Changes in glacial lakes in the Poiqu River basin in the central Himalayas
Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept
A simple cloud-filling approach for remote sensing water cover assessments
Evaluation of historic and operational satellite radar altimetry missions for constructing consistent long-term lake water level records
Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B
Assessing the capabilities of the Surface Water and Ocean Topography (SWOT) mission for large lake water surface elevation monitoring under different wind conditions
Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters
Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery
River-ice and water velocities using the Planet optical cubesat constellation
Exposure of tourism development to salt karst hazards along the Jordanian Dead Sea shore
A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry
Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series
Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle
Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model
Observing river stages using unmanned aerial vehicles
Quantification of the contribution of the Beauce groundwater aquifer to the discharge of the Loire River using thermal infrared satellite imaging
Swath-altimetry measurements of the main stem Amazon River: measurement errors and hydraulic implications
Satellite radar altimetry for monitoring small rivers and lakes in Indonesia
Quantifying river form variations in the Mississippi Basin using remotely sensed imagery
River ice flux and water velocities along a 600 km-long reach of Lena River, Siberia, from satellite stereo
Geometric dependency of Tibetan lakes on glacial runoff
Assessing the potential hydrological impact of the Gibe III Dam on Lake Turkana water level using multi-source satellite data
River monitoring from satellite radar altimetry in the Zambezi River basin
Flood occurrence mapping of the middle Mahakam lowland area using satellite radar
Satellite remote sensing of water turbidity in Alqueva reservoir and implications on lake modelling
Hydro-physical processes at the plunge point: an analysis using satellite and in situ data
Regional scale analysis of landform configuration with base-level (isobase) maps
Reconstructing the Tropical Storm Ketsana flood event in Marikina River, Philippines
Reading the bed morphology of a mountain stream: a geomorphometric study on high-resolution topographic data
Francis Lessard, Naïm Perreault, and Sylvain Jutras
Hydrol. Earth Syst. Sci., 28, 1027–1040, https://doi.org/10.5194/hess-28-1027-2024, https://doi.org/10.5194/hess-28-1027-2024, 2024
Short summary
Short summary
Headwaters streams, which are small streams at the top of a watershed, represent two-thirds of the total length of streams, yet their exact locations are still unknown. This article compares different techniques in order to remotely detect the position of these streams. Thus, a database of more than 464 km of headwaters was used to explain what drives their presence. A technique developed in this article makes it possible to detect headwater streams with more accuracy, despite the land uses.
Sijia Li, Shiqi Xu, Kaishan Song, Tiit Kutser, Zhidan Wen, Ge Liu, Yingxin Shang, Lili Lyu, Hui Tao, Xiang Wang, Lele Zhang, and Fangfang Chen
Hydrol. Earth Syst. Sci., 27, 3581–3599, https://doi.org/10.5194/hess-27-3581-2023, https://doi.org/10.5194/hess-27-3581-2023, 2023
Short summary
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.
Mathilde de Fleury, Laurent Kergoat, and Manuela Grippa
Hydrol. Earth Syst. Sci., 27, 2189–2204, https://doi.org/10.5194/hess-27-2189-2023, https://doi.org/10.5194/hess-27-2189-2023, 2023
Short summary
Short summary
This study surveys small lakes and reservoirs, which are vital resources in the Sahel, through a multi-sensor satellite approach. Water height changes compared to evaporation losses in dry seasons highlight anthropogenic withdrawals and water supplies due to river and groundwater connections. Some reservoirs display weak withdrawals, suggesting low usage may be due to security issues. The
satellite-derived water balance thus proved effective in estimating water resources in semi-arid areas.
Antoine Di Ciacca, Scott Wilson, Jasmine Kang, and Thomas Wöhling
Hydrol. Earth Syst. Sci., 27, 703–722, https://doi.org/10.5194/hess-27-703-2023, https://doi.org/10.5194/hess-27-703-2023, 2023
Short summary
Short summary
We present a novel framework to estimate how much water is lost by ephemeral rivers using satellite imagery and machine learning. This framework proved to be an efficient approach, requiring less fieldwork and generating more data than traditional methods, at a similar accuracy. Furthermore, applying this framework improved our understanding of the water transfer at our study site. Our framework is easily transferable to other ephemeral rivers and could be applied to long time series.
Pengcheng Su, Jingjing Liu, Yong Li, Wei Liu, Yang Wang, Chun Ma, and Qimin Li
Hydrol. Earth Syst. Sci., 25, 5879–5903, https://doi.org/10.5194/hess-25-5879-2021, https://doi.org/10.5194/hess-25-5879-2021, 2021
Short summary
Short summary
We identified ± 150 glacial lakes in the Poiqu River basin (central Himalayas), and we explore the changes in five lakes over the last few decades based on remote sensing images, field surveys, and satellite photos. We reconstruct the lake basin topography, calculate the water capacity, and propose a water balance equation (WBE) to explain glacial lake evolution in response to local weather conditions. The WBE also provides a framework for the water balance in rivers from glacierized sources.
Concetta Di Mauro, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, https://doi.org/10.5194/hess-25-4081-2021, 2021
Short summary
Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
Connor Mullen, Gopal Penny, and Marc F. Müller
Hydrol. Earth Syst. Sci., 25, 2373–2386, https://doi.org/10.5194/hess-25-2373-2021, https://doi.org/10.5194/hess-25-2373-2021, 2021
Short summary
Short summary
The level of lake water is rapidly changing globally, and long-term, consistent observations of lake water extents are essential for ascertaining and attributing these changes. These data are rarely collected and challenging to obtain from satellite imagery. The proposed method addresses these challenges without any local data, and it was successfully validated against lakes with and without ground data. The algorithm is a valuable tool for the reliable historical water extent of changing lakes.
Song Shu, Hongxing Liu, Richard A. Beck, Frédéric Frappart, Johanna Korhonen, Minxuan Lan, Min Xu, Bo Yang, and Yan Huang
Hydrol. Earth Syst. Sci., 25, 1643–1670, https://doi.org/10.5194/hess-25-1643-2021, https://doi.org/10.5194/hess-25-1643-2021, 2021
Short summary
Short summary
This study comprehensively evaluated 11 satellite radar altimetry missions (including their official retrackers) for lake water level retrieval and developed a strategy for constructing consistent long-term water level records for inland lakes. It is a two-step bias correction and normalization procedure. First, we use Jason-2 as the initial reference to form a consistent TOPEX/Poseidon–Jason series. Then, we use this as the reference to remove the biases with other radar altimetry missions.
Cecile M. M. Kittel, Liguang Jiang, Christian Tøttrup, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 333–357, https://doi.org/10.5194/hess-25-333-2021, https://doi.org/10.5194/hess-25-333-2021, 2021
Short summary
Short summary
In poorly instrumented catchments, satellite altimetry offers a unique possibility to obtain water level observations. Improvements in instrument design have increased the capabilities of altimeters to observe inland water bodies, including rivers. In this study, we demonstrate how a dense Sentinel-3 water surface elevation monitoring network can be established at catchment scale using publicly accessible processing platforms. The network can serve as a useful supplement to ground observations.
Jean Bergeron, Gabriela Siles, Robert Leconte, Mélanie Trudel, Damien Desroches, and Daniel L. Peters
Hydrol. Earth Syst. Sci., 24, 5985–6000, https://doi.org/10.5194/hess-24-5985-2020, https://doi.org/10.5194/hess-24-5985-2020, 2020
Short summary
Short summary
We want to assess how well the Surface Water and Ocean Topography (SWOT) satellite mission will be able to provide information on lake surface water elevation and how much of an impact wind conditions (speed and direction) can have on these retrievals.
Charlotte Marie Emery, Sylvain Biancamaria, Aaron Boone, Sophie Ricci, Mélanie C. Rochoux, Vanessa Pedinotti, and Cédric H. David
Hydrol. Earth Syst. Sci., 24, 2207–2233, https://doi.org/10.5194/hess-24-2207-2020, https://doi.org/10.5194/hess-24-2207-2020, 2020
Short summary
Short summary
The flow of freshwater in rivers is commonly studied with computer programs known as hydrological models. An important component of those programs lies in the description of the river environment, such as the channel resistance to the flow, that is critical to accurately predict the river flow but is still not well known. Satellite data can be combined with models to enrich our knowledge of these features. Here, we show that the coming SWOT mission can help better know this channel resistance.
Anette Eltner, Hannes Sardemann, and Jens Grundmann
Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, https://doi.org/10.5194/hess-24-1429-2020, 2020
Short summary
Short summary
An automatic workflow is introduced to measure surface flow velocities in rivers. The provided tool enables the measurement of spatially distributed surface flow velocities independently of the image acquisition perspective. Furthermore, the study illustrates how river discharge in previously ungauged and unmeasured regions can be retrieved, considering the image-based flow velocities and digital elevation models of the studied river reach reconstructed with UAV photogrammetry.
Andreas Kääb, Bas Altena, and Joseph Mascaro
Hydrol. Earth Syst. Sci., 23, 4233–4247, https://doi.org/10.5194/hess-23-4233-2019, https://doi.org/10.5194/hess-23-4233-2019, 2019
Short summary
Short summary
Knowledge of water surface velocities in rivers is useful for understanding a wide range of processes and systems, but is difficult to measure over large reaches. Here, we present a novel method to exploit near-simultaneous imagery produced by the Planet cubesat constellation to track river ice floes and estimate water surface velocities. We demonstrate the method for a 60 km long reach of the Amur River and a 200 km long reach of the Yukon River.
Najib Abou Karaki, Simone Fiaschi, Killian Paenen, Mohammad Al-Awabdeh, and Damien Closson
Hydrol. Earth Syst. Sci., 23, 2111–2127, https://doi.org/10.5194/hess-23-2111-2019, https://doi.org/10.5194/hess-23-2111-2019, 2019
Short summary
Short summary
The Dead Sea shore is a unique salt karst system. Development began in the 1960s, when the water resources that used to feed the Dead Sea were diverted. The water level is falling at more than 1 m yr−1, causing a hydrostatic disequilibrium between the underground fresh water and the base level. Despite these conditions, tourism development projects have flourished. Here, we show that a 10 km long strip of coast that encompasses several resorts is exposed to subsidence, sinkholes and landslides.
Tim Busker, Ad de Roo, Emiliano Gelati, Christian Schwatke, Marko Adamovic, Berny Bisselink, Jean-Francois Pekel, and Andrew Cottam
Hydrol. Earth Syst. Sci., 23, 669–690, https://doi.org/10.5194/hess-23-669-2019, https://doi.org/10.5194/hess-23-669-2019, 2019
Short summary
Short summary
This paper estimates lake and reservoir volume variations over all continents from 1984 to 2015 using remote sensing alone. This study improves on previous methodologies by using the Global Surface Water dataset developed by the Joint Research Centre, which allowed for volume calculations on a global scale, a high resolution (30 m) and back to 1984 using very detailed lake area dynamics. Using 18 in situ volume time series as validation, our volume estimates showed a high accuracy.
Andrew Ogilvie, Gilles Belaud, Sylvain Massuel, Mark Mulligan, Patrick Le Goulven, and Roger Calvez
Hydrol. Earth Syst. Sci., 22, 4349–4380, https://doi.org/10.5194/hess-22-4349-2018, https://doi.org/10.5194/hess-22-4349-2018, 2018
Short summary
Short summary
Accurate monitoring of surface water extent is essential for hydrological investigation of small lakes (1–10 ha), which supports millions of smallholder farmers. Landsat monitoring of long-term surface water dynamics is shown to be suited to lakes over 3 ha based on extensive hydrometric data from seven field sites over 15 years. MNDWI water classification optimized here for the specificities of small water bodies reduced mean surface area errors by 57 % compared to published global datasets.
Filippo Bandini, Daniel Olesen, Jakob Jakobsen, Cecile Marie Margaretha Kittel, Sheng Wang, Monica Garcia, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 22, 4165–4181, https://doi.org/10.5194/hess-22-4165-2018, https://doi.org/10.5194/hess-22-4165-2018, 2018
Short summary
Short summary
Water depth observations are essential data to forecast flood hazard, predict sediment transport, or monitor in-stream habitats. We retrieved bathymetry with a sonar wired to a drone. This system can improve the speed and spatial scale at which water depth observations are retrieved. Observations can be retrieved also in unnavigable or inaccessible rivers. Water depth observations showed an accuracy of ca. 2.1 % of actual depth, without being affected by water turbidity or bed material.
Kiana Zolfaghari, Claude R. Duguay, and Homa Kheyrollah Pour
Hydrol. Earth Syst. Sci., 21, 377–391, https://doi.org/10.5194/hess-21-377-2017, https://doi.org/10.5194/hess-21-377-2017, 2017
Short summary
Short summary
A remotely-sensed water clarity value (Kd) was applied to improve FLake model simulations of Lake Erie thermal structure using a time-invariant (constant) annual value as well as monthly values of Kd. The sensitivity of FLake model to Kd values was studied. It was shown that the model is very sensitive to variations in Kd when the value is less than 0.5 m-1.
Tomasz Niedzielski, Matylda Witek, and Waldemar Spallek
Hydrol. Earth Syst. Sci., 20, 3193–3205, https://doi.org/10.5194/hess-20-3193-2016, https://doi.org/10.5194/hess-20-3193-2016, 2016
Short summary
Short summary
We study detectability of changes in water surface areas on orthophotomaps. We use unmanned aerial vehicles to acquire visible light photographs. We offer a new method for detecting changes in water surface areas and river stages. The approach is based on the application of the Student's t test, in asymptotic and bootstrapped versions. We test our approach on aerial photos taken during 3-year observational campaign. We detect transitions between all characteristic river stages using drone data.
E. Lalot, F. Curie, V. Wawrzyniak, F. Baratelli, S. Schomburgk, N. Flipo, H. Piegay, and F. Moatar
Hydrol. Earth Syst. Sci., 19, 4479–4492, https://doi.org/10.5194/hess-19-4479-2015, https://doi.org/10.5194/hess-19-4479-2015, 2015
Short summary
Short summary
This work shows that satellite thermal infrared images (LANDSAT) can be used to locate and quantify groundwater discharge into a large river (Loire River, France - 100 to 300 m wide). Groundwater discharge rate is found to be highly variable with time and space and maximum during flow recession periods and in winter. The main identified groundwater discharge area into the Loire River corresponds to a known discharge area of the Beauce aquifer.
M. D. Wilson, M. Durand, H. C. Jung, and D. Alsdorf
Hydrol. Earth Syst. Sci., 19, 1943–1959, https://doi.org/10.5194/hess-19-1943-2015, https://doi.org/10.5194/hess-19-1943-2015, 2015
Short summary
Short summary
We use a virtual mission analysis on a ca. 260km reach of the central Amazon River to assess the hydraulic implications of potential measurement errors in swath-altimetry imagery from the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission. We estimated water surface slope from imagery of water heights and then derived channel discharge. Errors in estimated discharge were lowest when using longer reach lengths and channel cross-sectional averaging to estimate water slopes.
Y. B. Sulistioadi, K.-H. Tseng, C. K. Shum, H. Hidayat, M. Sumaryono, A. Suhardiman, F. Setiawan, and S. Sunarso
Hydrol. Earth Syst. Sci., 19, 341–359, https://doi.org/10.5194/hess-19-341-2015, https://doi.org/10.5194/hess-19-341-2015, 2015
Short summary
Short summary
This paper investigates the possibility of monitoring small water bodies through Envisat altimetry observation. A novel approach is introduced to identify qualified and non-qualified altimetry measurements by assessing the waveform shapes for each returned radar signal. This research indicates that small lakes (extent < 100 km2) and medium-sized rivers (e.g., 200--800 m in width) can be successfully monitored by satellite altimetry.
Z. F. Miller, T. M. Pavelsky, and G. H. Allen
Hydrol. Earth Syst. Sci., 18, 4883–4895, https://doi.org/10.5194/hess-18-4883-2014, https://doi.org/10.5194/hess-18-4883-2014, 2014
Short summary
Short summary
Many previous studies have used stream gauge data to estimate patterns of river width and depth based on variations in river discharge. However, these relationships may not capture all of the actual variability in width and depth. We have instead mapped the widths of all of the rivers wider than 100 m (and many narrower) in the Mississippi Basin and then used them to also improve estimates of depth as well. Our results show width and depth variations not captured by power-law relationships.
A. Kääb, M. Lamare, and M. Abrams
Hydrol. Earth Syst. Sci., 17, 4671–4683, https://doi.org/10.5194/hess-17-4671-2013, https://doi.org/10.5194/hess-17-4671-2013, 2013
V. H. Phan, R. C. Lindenbergh, and M. Menenti
Hydrol. Earth Syst. Sci., 17, 4061–4077, https://doi.org/10.5194/hess-17-4061-2013, https://doi.org/10.5194/hess-17-4061-2013, 2013
N. M. Velpuri and G. B. Senay
Hydrol. Earth Syst. Sci., 16, 3561–3578, https://doi.org/10.5194/hess-16-3561-2012, https://doi.org/10.5194/hess-16-3561-2012, 2012
C. I. Michailovsky, S. McEnnis, P. A. M. Berry, R. Smith, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 16, 2181–2192, https://doi.org/10.5194/hess-16-2181-2012, https://doi.org/10.5194/hess-16-2181-2012, 2012
H. Hidayat, D. H. Hoekman, M. A. M. Vissers, and A. J. F. Hoitink
Hydrol. Earth Syst. Sci., 16, 1805–1816, https://doi.org/10.5194/hess-16-1805-2012, https://doi.org/10.5194/hess-16-1805-2012, 2012
M. Potes, M. J. Costa, and R. Salgado
Hydrol. Earth Syst. Sci., 16, 1623–1633, https://doi.org/10.5194/hess-16-1623-2012, https://doi.org/10.5194/hess-16-1623-2012, 2012
A. T. Assireu, E. Alcântara, E. M. L. M. Novo, F. Roland, F. S. Pacheco, J. L. Stech, and J. A. Lorenzzetti
Hydrol. Earth Syst. Sci., 15, 3689–3700, https://doi.org/10.5194/hess-15-3689-2011, https://doi.org/10.5194/hess-15-3689-2011, 2011
C. H. Grohmann, C. Riccomini, and M. A. C. Chamani
Hydrol. Earth Syst. Sci., 15, 1493–1504, https://doi.org/10.5194/hess-15-1493-2011, https://doi.org/10.5194/hess-15-1493-2011, 2011
C. C. Abon, C. P. C. David, and N. E. B. Pellejera
Hydrol. Earth Syst. Sci., 15, 1283–1289, https://doi.org/10.5194/hess-15-1283-2011, https://doi.org/10.5194/hess-15-1283-2011, 2011
S. Trevisani, M. Cavalli, and L. Marchi
Hydrol. Earth Syst. Sci., 14, 393–405, https://doi.org/10.5194/hess-14-393-2010, https://doi.org/10.5194/hess-14-393-2010, 2010
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...
