## 1 Introduction
[PERSON] [16] explored the impact of salinity on water temperature fields, noting that when water evaporates, a thin layer with increased salt concentration forms on its surface, preventing further evaporation of the water. [PERSON] [17] pointed out that water constituents (such as phytoplankton) modulate the penetration process of visible solar radiation in the upper ocean and influence the mixing processes and heat transfer within the water column. Water surface temperature measurement primarily relies on in situ monitoring and satellite observation. In situ monitoring provides high-precision, real-time data through sensor deployment, whereas satellite observation enables continuous, large-scale monitoring using remote sensing technology. Together, these methods offer a robust framework for accurate water surface temperature assessment [18]. With the rise of spatial distribution monitoring, automated vessels and unmanned aerial vehicles (UAVs) are increasingly being used to measure surface water temperature [19]. In situ measurement employs mercury thermometers [20], multi-parameter probes [21], or thermistor-equipped mooring stations [22] to collect water temperature data at discrete depths, either automatically or manually. In situ measurement data are often challenging to obtain and limited in spatial scale and regional coverage. Satellite observations, however, provide a global data source that corrects latitude biases in in situ surface water temperature monitoring. By using satellite-mounted radiometers to measure surface water temperature, this approach resolves the standardization issues of long-term ground-based data and enhances the comprehensiveness and reliability of spatial surface water temperature characterization, opening new avenues for water temperature research [23]. [PERSON] [24] created a climate data record of sea surface temperature under rain-free ocean conditions based on over two decades of data from three satellite instruments. Compared to in situ radiosonde and buoy observations, the satellite-derived SST exhibited a bias of 0.2 K and a root-mean-square error of 1.6 K, demonstrating the precision and reliability of satellite data for ocean temperature monitoring and advancing global SST climate studies. [PERSON] (2015) created a global lake temperature database based on satellite observations of summer lake surface temperatures from 291 lakes, investigating spatial and temporal patterns of water temperature variations globally and regionally. [PERSON] (2016) proposed an innovative method for retrieving plateau lake surface temperature (p-LWST) using unmanned aerial vehicle (UAV) thermal infrared (TIR) data. The results demonstrated that the method successfully retrieved LWST in the Erhai region of the plateau, with temperatures ranging from 288 K to 295 K and a final accuracy of 0.89 K. Infrared measurement technology is now extensively applied in oceanography to detect breaking waves, microscale breaking waves, fronts, and internal wave structures ([PERSON] and [PERSON], 2016), and to study the thermal boundary layer and its connection to ocean heat flux. In the context of remote sensing and satellite sea surface temperature measurements, the measurement of the so-called \"cool skin\" phenomenon has garnered significant attention from researchers ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). [PERSON] (2016) utilized Landsat 5 and Landsat 7 archived images to create the skin surface temperature dataset, which includes surface water temperature data from 442 French inland water bodies. By comparing satellite temperature measurements with in situ measurements and accounting for the cool skin effect and warm layer effect, the study estimated that the cool skin effect typically ranges between \(-0.3\)\({}^{\circ}\)C and \(-0.6\)\({}^{\circ}\)C, with the cool skin layer being more frequently observed at night.