SDG6: Clean Water and Sanitation

Spatiotemporal patterns of water transparency in China's lakes(2020)

 Target: 6.3 By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally.
During 2000-2019, the water transparency of China's lakes showed a spatial pattern of "high in the west and low in the east". Overall, water clarity was good and showed a positive trend. The proportion of Types I, II, and III water bodies with good clarity increased from 84.11% in 2000 to 92.46% in 2019.


Water transparency (Secchi Disk Depth, SDD) refers to the depth at which a black/white Secchi disk becomes invisible when it sinks vertically into the water and can be used to describe water clarity. In general, the higher the transparency is, the clearer the water. Many studies have shown that satellite-derived water transparency has close relationships with water quality indicators (Chang et al., 2020; Lee and Lee, 2015). To reveal the water clarity in China's lakes at a macro level, this study developed a novel remote sensing algorithm to retrieve the water transparency of large lakes in China (>20 km2) during 2000-2019, as a useful exploration for the monitoring and evaluation of SDG 6.3.2.

Data used

①Land reflectance, land surface temperature, and Normalized Difference Vegetation Index (NDVI) of Moderate Resolution Imaging Spectroradiometer (MODIS) during 2000-2019.

Precipitation data of the Tropical Rainfall Measuring Mission (TRMM) during 2000-2019.

③China's Digital Elevation Model (DEM).

④Reanalysis products of wind speed during 2000-2019.

⑤Chinese population density per square kilometer in 2010.


This case developed a novel remote sensing algorithm of SDD applicable to the land reflectance of MODIS (MOD09GA):

where R555 and R645 denote reflectance at two bands of MODIS with central wavelengths of 555 nm and 645 nm respectively; R is an intermediate variable. By applying the algorithm to reflectance during 2000-2019, the daily SDD was remotely retrieved. Then, the annual and climatological mean SDD values for different lakes were further calculated through the arithmetic mean method. For the in situ SDD of China's lakes (N = 2236), 75% of the synchronous cloudless match-ups were selected randomly for algorithm calibration, and the remaining 25% were used for validation. The results of the new algorithm were comparable to those of reported regional algorithms, indicating the wide applicability of the new algorithm at regional and national scales.

Results and analysis

1) Spatiotemporal patterns of SDD in China's lakes

Overall, the SDD of China's lakes showed a geographical pattern of "high in the west and low in the east." The mean SDD of lakes in the three western mountainous lake zones (180.28 ± 171.29 cm) was more than twice that of the two eastern plain lake zones (78.01 ± 40.54 cm) (Fig. 3-1a). The mean SDD values in the Yunnan-Guizhou Plateau Lake (YGPL), Tibetan Plateau Lake (TPL), Inner Mongolia-Xinjiang Lake (IMXL), Eastern Plain Lake (EPL), and Northeast Plain and Mountain Lake (NPML) zones were 404.63 ± 363.98 cm, 182.41 ± 184.29 cm, 139.70 ± 193.96 cm, 92.90 ± 90.09 cm, and 55.05 ± 33.46 cm, respectively (Fig. 1). The results showed that the spatial changes in lake SDD were mainly influenced by water depth, which explained 88.81% of the spatial variations. In situ showed that water eutrophication also reduced SDD and there was a significant negative power correlation between the measured chlorophyll a and SDD (N = 1827, r = 0.36, p < 0.001).

During 2000-2019, the water clarity of China's lakes improved to a certain extent. For the 412 studied lakes, which accounted for 87.02% of China's total lake areas, 70.15% showed increases in SDD, and 42.72% showed significant increases. Vegetation restoration in the catchment played a major role in increasing lake SDD. Improvement in NDVI contributed 44.95%, 37.87%, 75.66%, 58.12% and 36.34% of the increases in SDD in the IMXL, TPL, YGPL, NPML and EPL zones, respectively. Climate change also showed significant effects on increasing SDD, especially for lakes in the TPL zone. The rising air temperature led to the melting of glaciers and rising lake water levels, which explained 24.98% of the increase in SDD in the TPL zone.

2) Proportions of lakes at different water clarity levels

According to the published standard (Chang et al., 2020; Lee and Lee, 2015), this case set thresholds for different water clarity levels: lakes with annual mean SDD values of ≤ 25, (25, 65], (65, 100] and > 100 (unit: cm) were classified as Types IV, III, II and I, respectively. Most lakes of Type I were located in western China, especially in the TPL zone. For lakes in the two eastern lake zones, most were Type III, and the degradation of water clarity was observed in some of these lakes. From 2000 to 2019, Type I lakes increased significantly from 39.12% to 54.01%; Type II lakes remained stable at approximately 14.21%; Type III lakes decreased significantly from 32.52% to 23.84%; and Type IV lakes also decreased significantly from 15.89% to 7.54%. The combined proportion of lakes with good water clarity (Type I, II or III) increased from 84.11% in 2000 to 92.46% in 2019 (Fig. 1), representing an increase of 8.35 percentage points. In general, China's large lakes exhibit good water clarity and are still improving.

Figure 1. The SDD values of China's large lakes during 2000-2019

(a) The mean SDD values; (b) Proportions of lakes with different water clarity levels

The change rate in NDVI was the linear fitting slope during 2000-2019. IMXL: Inner Mongolia-Xinjiang Lake; TPL: Tibetan Plateau Lake; YGPL: Yunnan-Guizhou Plateau Lake; NPML: Northeast Plain and Mountain Lake; EPL: Eastern Plain Lake


This case developed a remote sensing algorithm to rapidly map SDD in China's lakes and quantitatively calculated the contributions of different impact factors to the spatiotemporal variations in SDD. Vegetation restoration in the catchment had positive effects on increasing lake SDD during 2000-2019. Overall, the water clarity of large lakes in China is fairly good and still improving, with the proportions of lakes in Types I, II or III up from 84.11% in 2000 to 92.46% in 2019. Based on correlation analyses, this case proposed three measures to improve water clarity of China's lakes: ecological water replenishment, eutrophication control, and vegetation restoration in the catchment.


Big Earth Data Science Engineering Project (CASEarth) SDG Working Group

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