Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation

Shen, Ming; Duan, Hongtao; Cao, Zhigang; Xue, Kun; Qi, Tianci; Ma, Jinge; Liu, Dong; Song, Kaishan; Huang, Chunlin; Song, Xiaoyu

doi:10.1016/j.rse.2020.111950

Cited by 106 publications

(43 citation statements)

References 82 publications

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“…The first two methods are difficult to apply to large-scale studies (provincial and national scales) due to the complex theoretical models and parameterization processes and expensive equipment required (Cao et al, 2017;Giardino et al, 2007). The last group of methods is widely used to retrieve SDD at multiple scales due to its simplicity and operability (Duan et al, 2009;McCullough et al, 2012;Olmanson et al, 2011;Shen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018

Tao

Song

Liu

et al. 2022

Earth Syst. Sci. Data

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Abstract. Water clarity serves as a sensitive tool for understanding the spatial pattern and historical trend in lakes' trophic status. Despite the wide availability of remotely sensed data, this metric has not been fully explored for long-term environmental monitoring. To this end, we utilized Landsat top-of-atmosphere reflectance products within Google Earth Engine in the period 1984–2018 to retrieve the average Secchi disk depth (SDD) for each lake in each year. Three SDD datasets were used for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red / blue band ratio algorithm was applied to map SDD for lakes (>0.01 km2) based on the first SDD dataset, where R2=0.79 and relative RMSE (rRMSE) =61.9 %. The other two datasets were used to validate the temporal transferability of the SDD estimation model, which confirmed the stable performance of the model. The spatiotemporal dynamics of SDD were analyzed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found the number of lakes with SDD <2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total areas of lakes with SDD of <0.5 and >4 m were the largest, both accounting for about 24.00 % of the total lakes. During 1984–2018, lakes in the Tibetan–Qinghai Plateau region (TQR) had the clearest water with an average value of 3.32±0.38 m, while that in the northeastern region (NLR) exhibited the lowest SDD (mean 0.60±0.09 m). Among the 10 814 lakes with SDD results for more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia–Xinjiang region (MXR), more than half of the total lakes in every other region exhibited significant increasing trends. In the eastern region (ELR), NLR and Yungui Plateau region (YGR), almost more than 50 % of the lakes that displayed increase or decrease in SDD were mainly distributed in the area range of 0.01–1 km2, whereas those in the TQR and MXR were primarily concentrated in large lakes (>10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset is freely available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.271571, Tao et al., 2021).

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Section: Introductionmentioning

confidence: 99%

A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018

Tao

Song

Liu

et al. 2022

Earth Syst. Sci. Data

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“…These important effects are especially evident in Daqing Lakes, the largest lake gathering place in Northeast China [6], [7]. The water quality of lakes can directly influence the survival of human beings and sustainable development of society [8]. However, many lakes are encountering water quality problems because of rapid urbanization, industrialization, agricultural intensification, anthropogenic interference and climate change [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Wang

Song

Wen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011-2018) ，this study developed a retrieval model suitable for turbidity. The determination coefficient (R 2 ) of the model was 0.946 and the root mean square error (RMSE) was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China . The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P< 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onwards). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R=0.56, P< 0.001), followed by water temperature and wind speed (0.02

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“…The water clarity of lakes is generally quantified as the Secchi disk depth (SDD, in meter), which is the depth at which a black-white Secchi disk can no longer be seen when deployed in water. Due to the simplicity and low cost of SDD measurements and their important function in assessing the underwater light climate in lakes and the consequent productivity of planktonic and benthic algae and aquatic macrophytes (Lee et al 2015), numerous studies conducted over the past five decades have focused on monitoring the changes in SDD of ocean and lake ecosystems (Binding et al 2007;Liu et al 2020;Olmanson et al 2008;Shen et al 2020). The water quality of Lake Hongze, Lake Taihu, and Lake Liangzi has been reported to have deteriorated dramatically over the past halfcentury, with decrease rates of 0.9, 0.5, and 5.0 cm/year (Li et al 2019;Shi et al 2018;Xu et al 2018), respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Feng et al (2019) reported that many Yangtze lakes in China, showed increasing SDD during 2003−2016. Liu et al (2020) and Shen et al (2020) reported that the water SDD of Chinese lakes has increased over the past two decades. In addition, the average SDD of three of America's great lakes, Ontario, Huron, and Michigan increased by 58%, 49% and 62% from 1978 to 2014 (Binding et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

Zhang

Shi

Zhang

et al. 2021

Science of The Total Environment

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Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation

Cited by 106 publications

References 82 publications

A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018

A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

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