Quantification of lake clarity in China using Landsat OLI imagery data

Song, Kaishan; Liu, Ge; Wang, Qiang; Wen, Zhidan; Lyu, Lili; Du, Yu‐Cang; Sha, Linwei; Fang, Chong

doi:10.1016/j.rse.2020.111800

Cited by 87 publications

(56 citation statements)

References 54 publications

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“…In addition, the Chinese lake inventories for 2005 [49] and 91 2010 [50], the Global Lakes and Wetland Database (GLWD) 92 [51], and the existing Chinese Reservoirs and Dams vector 93 database [52] were used to assist in the extraction of lakes.…”

Section: B Auxiliary Inland Water Products 72mentioning

confidence: 99%

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An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel–Ule Index and the Google Earth Engine Platform

Chen

Liu

Zhang

et al. 2021

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

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Water color is an important parameter in water quality assessment. However, the existing water color investigations have mostly focused on the lakes with areas greater 1 km 2 . In order to improve the understanding of the color of water bodies in China, a cloud-free composite image of China for the summer of 2015 was generated using time-series of Landsat-8 imagery and the best-available-pixel (BAP) compositing algorithm.Then, the first Forel-Ule index (FUI) water color product with a resolution of 30 m was produced for China using the generated BAP composite and the Google Earth Engine computing platform.Finally, the first national-scale assessment of the FUI of natural lakes with an area > 0.01 km 2 (N = 60026) was conducted based on the generated FUI product. The generated FUI product was shown to have a high degree of consistency with in-situ water surface reflectance-derived FUI (R 2 = 0.90, P < 0.001). Also, it had a high degree of consistency with the in-situ Secchi depth (SD) (R 2 = 0.90, P < 0.001) and Trophic Level Index (TLI) (R 2 = 0.62, P < 0.001) datasets. In addition, we found that the most prevalent lake colors in China were yellow (about 49%) and green (about 41%). Besides, the proportion of small lakes (areas < 1 km 2 ) found to be yellow was much larger than for large lakes (area ≥ 1 km 2 ) (50% against 28%). Our results will provide important information that can be used for preserving and restoring inland water resources. Index Terms-Inland water color; Forel-Ule index (FUI); Landsat 8; Google Earth Engine; China; lake I. INTRODUCTION nland water bodies have a direct interface with human society and are important to the food supply, commerce, transportation, and human health [1]. However, with the growth of the population and the rapid development of the economy, China has been facing increasingly severe water scarcity and security issues in recent years [2-5]. Because of the close relationship between water bodies and humans, water bodies in China are under great pressure due to human activities Manuscript received January 14, 2021.

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Section: B Auxiliary Inland Water Products 72mentioning

confidence: 99%

“…After this, the lake objects with an area > 71 0.01 km 2 (more than ten pixels) were extracted. Next, by 72 referring to the Chinese lake inventories of 2005 [49] and 2010 73 [50], the lake vectors from the GLWD [51] Moreover, the method introduced by Li, et al…”

mentioning

confidence: 99%

An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel–Ule Index and the Google Earth Engine Platform

Chen

Liu

Zhang

et al. 2021

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

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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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“…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. The spatial characteristics of water transparency between 2016 and 2018 were detected by Zhang et al [54] using Landsat 8 images for lakes with area ≥10 km 2 .…”

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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Quantification of lake clarity in China using Landsat OLI imagery data

Cited by 87 publications

References 54 publications

An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel–Ule Index and the Google Earth Engine Platform

An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel–Ule Index and the Google Earth Engine Platform

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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