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 (SSIM _day = 0.96, PSNR _day = 23.97; SSIM _night = 0.95, PSNR _night = 24.99). 2) LSWT had an overall upward trend (CR _day = 0.22 °C/10a, CR _night = 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.

中文翻译：

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基于深度学习的湖泊表面水温降尺度研究


湖泊表面水温（LSWT）是水生态环境的重要影响因素。随着全球变暖，LSWT也呈上升趋势。研究LSWT上升的主要原因是控制和改善区域生态环境的基础。然而，现有的遥感图像很难兼顾时间和空间分辨率。低分辨率图像严重影响数据准确性，甚至产生不正确的结果。因此，通过降尺度获得高时空分辨率的图像对于更准确地分析LSWT的时空特性具有重要意义。本文选择滇池为研究区域，以高空间分辨率影像（Landsat）和高时间分辨率影像（MODIS）为数据。基于统计和学习的降尺度算法，提出DisTrad-超分辨率卷积神经网络（SRCNN）降尺度模型，构建了50 m分辨率的LSWT月平均数据集。结果表明：1）DisTrad-SRCNN最能反映LSWT的分布特征（SSIM_日=0.96，PSNR_日=23.97；SSIM_夜间=0.95，PSNR_夜间=24.99）。 2）LSWT总体呈上升趋势（CR_白天=0.22℃/10a，CR_夜间=0.21℃/10a），呈现约4年的冷-暖-冷循环变化。 3）北部和湖滨地区基本处于高温状态，全湖呈现4~5年的暖-冷-暖周期变化；距离城市和居民区较近的LSWT且变化率较高，间接反映了人类活动对LSWT的严重影响。