Abstract
Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011–2018), this article developed a retrieval model suitable for turbidity. The determination coefficient ( R 2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984–2018). 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 R = 0.56, P R P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.
Highlights
L AKES are one of the most important components of the earth’s surface system [1]–[3]
Based on extensive field measurements and Landsat image data, this article developed an empirical model for estimating water turbidity; and mapped the spatiotemporal patterns of water turbidity of the Daqing Lakes in Northeast China in order to explore the reasons of the spatial and temporal differences of water turbidity
Through the comparison of multiband combined operation results, we confirmed that the linear model (Blue+NIR)∗NIR is more suitable for the assessment of water turbidity in Daqing lakes
Summary
L AKES are one of the most important components of the earth’s surface system [1]–[3]. Manuscript received April 12, 2021; revised June 12, 2021 and July 19, 2021; accepted July 27, 2021. Date of publication August 4, 2021; date of current version September 15, 2021.
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