Abstract
Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.
Highlights
IntroductionThe lake ecological environment can reflect the environmental conditions of the region [4]–[6]
The results showed that DisTrad–super-resolution convolutional neural network (SRCNN) has high efficiency and low error, can reflect the Lake surface water temperature (LSWT)’s distribution characteristics, and provided a new method for lake water quality
1) Spatial Distribution Characteristics: Based on the data of MOD11A2 and MOD13Q1, the spatial downscaling of 1 km–> 250 m was achieved by the DisTrad method, and the results showed the spatial distribution characteristics basically consistent with MOD11A1, and the distribution characteristics of decomposed pixels were closer to MOD13Q1
Summary
The lake ecological environment can reflect the environmental conditions of the region [4]–[6]. In the past 30 years, the surface water temperature of most lakes in the world has been rising rapidly at a rate of 0.34 °C/10a [5]. This phenomenon will cause ecological problems such as extension of the suitable growth period of lake cyanobacteria and increased eutrophication, as well as environmental problems such as the extension of lake thermal stratification period, the increase of thermocline depth and intensity, and the increase of stability, which cause hypoxia at the lake bottom [5]. This article shows that the temporal and spatial change process analysis of lake environment is the basis for the management and improvement of the regional ecological environment
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