Abstract：
Lake water surface temperature (LWST) is a critical component in understanding the response of freshwater ecosystems to climate change. Traditional estimation of LWST estimation considers water surface bodies to be static. Our work proposes a novel open-source web application, IMPART, designed for estimating dynamic LWST using Landsat reflectance and MODIS temperature datasets from 2004 to 2022. Results presented globally for over 342 lakes reveal a root mean square deviation of 0.86 °C between static and dynamic LWST. Additionally, our results demonstrate that 57% of the lakes exhibit a statistically significant difference between the static and dynamic LWST values. Improved LWST will ultimately enhance our ability to comprehensively monitor and respond to the impacts of climate change on freshwater ecosystems worldwide. Furthermore, based on the Koppen-Geiger climate classification, our zonal analysis demonstrates the deviation between static and dynamic LWST. It identifies specific zones where considering waterbodies as dynamic entities is essential.
摘要：
湖水表面温度（LWST）是了解淡水生态系统对气候变化响应的关键组成部分。LWST估计的传统估计认为水面体是静态的。我们的工作提出了一种新颖的开源Web应用程序，IMPART,设计用于使用2004年至2022年的Landsat反射率和MODIS温度数据集估计动态LWST。全球342多个湖泊的结果显示，静态和动态LWST之间的均方根偏差为0.86°C。此外,我们的结果表明，57％的湖泊在静态和动态LWST值之间表现出统计学上的显着差异。改进的LWST最终将提高我们全面监测和应对气候变化对全球淡水生态系统影响的能力。此外，根据Koppen-Geiger气候分类，我们的区域分析表明静态和动态LWST之间存在偏差。它确定了将水体视为动态实体至关重要的特定区域。
