Observations of water transparency in China’s lakes from space

Liu, Dong; Duan, Hongtao; Loiselle, Steven; Hu, Chuanmin; Zhang, Guoqing; Li, Junli; Yang, Hong; Thompson, J. R.; Cao, Zhigang; Shen, Ming; Ma, Ronghua; Zhang, Min; Han, Wei

doi:10.1016/j.jag.2020.102187

Cited by 52 publications

(31 citation statements)

References 49 publications

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“…Several past studies have examined the spatiotemporal variation of SDD in lakes across China (or parts of China), but these investigations were mainly based on MODIS images to estimate SDD in large lakes (>10 km 2 ) and primarily focused on the period after 2000 (Feng et al, 2019a;Liu et al, 2020a;Pi et al, 2020;Wang et al, 2020a). Therefore, it becomes a challenge to compare these past results with the results of the present study due to difference in the period of interest, resolution of the satellite images and lake size (>1 ha in our study).…”

Section: Comparison With Past Studiesmentioning

confidence: 99%

“…In recent years, a few studies have examined the spatiotemporal dynamics of SDD in lakes across China, but they mainly focused on the large lakes and reservoirs (area >10 km 2 ) (Liu et al 2020a;Wang et al 2020a;Zhang et al 2021). Smaller lakes (area < 10 km 2 ) are widely distributed across the country, but our understanding of their ecological status remains limited.…”

Section: Introductionmentioning

confidence: 99%

“…Smaller lakes (area < 10 km 2 ) are widely distributed across the country, but our understanding of their ecological status remains limited. For example, Liu et al (2020a) used an empirical model and the MODIS red and green bands (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) within GEE to study SDD variation in 412 large lakes (area > 20 km 2 ) across China. Wang et al (2020a) applied water color parameters (Forel-Ule Index and hue angle) to MODIS data (2000MODIS data ( -2017 and obtained SDD data for 153 lakes (> 25 km 2 ) across China.…”

Section: Introductionmentioning

confidence: 99%

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Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

Tao

Song

Liu

et al. 2021

Preprint

Self Cite

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Abstract. Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets 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 (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed 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 that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0–0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984–2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results 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 lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01–1 km2, whereas that 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 can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571.

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Section: Comparison With Past Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

Tao

Song

Liu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In previous studies, the water transparency estimation using remote sensing approaches were mainly applied to inland water in China [21][22][23] with few reports on the coastal and marine regions of China seas. For example, the Bohai Sea (BHS) was found to have the lowest water transparency among the East China Seas from in situ collected data over a 21-year period (1959-1979) [24].…”

Section: Introductionmentioning

confidence: 99%

Variations of Water Transparency and Impact Factors in the Bohai and Yellow Seas from Satellite Observations

Zhou

Yang

et al. 2021

Remote Sensing

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Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y−1, and highest in the SYS at 0.015 m y−1. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.

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“…There have been several researches on water transparency in larger lakes across Inner Mongolia. The water transparency in several lakes with the area >20 km 2 during 2000-2018 were retrieved by Liu et al [52]. Song et al [53] mapped water transparency in the lakes with area >8 ha with OLI images mainly acquired in 2016.…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing to Understand the Total Suspended Matter Dynamics in Lakes Across Inner Mongolia

Song

Wang

Liu

et al. 2021

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

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An exponential function model was developed based on the red band from Landsat and in situ data. The model performed well, the calibration determination coefficient (R 2 ) was 0.82 and validation metrics root-mean-square error, mean absolute percent error, and bias was 8.23 mg/L, 30% and −2.35 mg/L, respectively. Then, the time series of total suspended matter (TSM) concentrations for the lakes across Inner Mongolia were presented using the Landsat data from 1984-2019. The results showed that the number of the lakes with TSM decrease was slightly more than that with TSM increase (57% versus 43%). A total of 70.72% of those lakes had a changing rate less than 1 mg/L/yr in TSM, while the other 29.28% had a changing rate more than 1 mg/L/yr. In some lakes (24.2%), the coefficient of variation was greater than 102% indicating significant spatial variation. The TSM less than 20 mg/L mainly appeared in the reservoirs. Based on different environmental backgrounds, this study showed comprehensive estimates (increasing and decreasing regions) of TSM concentrations. Finally, the relative roles of several factors to the TSM changes were quantified and examined at different scales. The responses of TSM changes to NDVI representing vegetation coverage indicated that vegetation played an important role for most lakes. Whilst wind speed and precipitation significantly affected a few lakes. For most lakes, the TSM were affected by multiple factors, where a single factor could not be highlighted. Nevertheless, a comprehensive analysis of the factors surrounding a lake remains relevant for determining the possible trend (better or worse) of its TSM variation.

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Observations of water transparency in China’s lakes from space

Cited by 52 publications

References 49 publications

Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

Variations of Water Transparency and Impact Factors in the Bohai and Yellow Seas from Satellite Observations

Using Remote Sensing to Understand the Total Suspended Matter Dynamics in Lakes Across Inner Mongolia

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