## 1 Introduction  
Water temperature is a vital parameter that reflects the state of aquatic systems. Understanding sea surface temperature (SST) is crucial for investigating meteorological variations, fish growth dynamics, underwater acoustic propagation, and infrared detection of maritime targets [1]. SST is a fundamental physical parameter at the air-sea interface, frequently utilized as a boundary condition to estimate heat exchange between the ocean and atmosphere, as well as freshwater exchange driven by precipitation and evaporation. Consequently, high-precision and high spatial-resolution SST data are indispensable for accurate weather forecasting and effective climate change monitoring [2]. Furthermore, the sea surface acts as a significant background for military targets, with its infrared radiation characteristics forming the foundation for infrared guidance systems in maritime operations. The infrared radiation signatures of ship wakes, generated through the interaction between ship wakes and sea surface temperature, represent a critical target source for infrared detection systems [3, 4]. Since the 20 th century, many maritime nations have conducted extensive research on sea surface and subsurface seawater temperatures. The development of satellite and aerial remote sensing, along with deep-water exploration technologies, has significantly advanced the analysis and forecasting of seawater temperatures in offshore and oceanic areas, which has fostered substantial scientific progress [5; 6]. In the study of sea surface temperature, researchers primarily utilize regression-based statistical models, deterministic models, machine learning models, and hybrid models for modeling. These approaches, each with distinct advantages, contribute to a comprehensive understanding of sea surface temperature dynamics, enriching the theoretical foundation of the field [7]. [PERSON] [8] had developed a physics-based simplified model to estimate surface water temperature from air temperature data, deriving a simplified differential equation. [PERSON] [9] and [PERSON] [10] introduced multiple parameterization approaches to estimate the cool skin temperature difference, formulated as a function of the wind speed U\({}_{10}\) at 10 m above the surface, thereby building the relationship between wind speed and the cool skin temperature difference. [PERSON] [11] introduced an optimal estimation and probabilistic cloud-screening approach for estimating lake surface water temperature. The study found that the daytime satellite in situ difference was \(-\)0.2 \(\pm\) 0.7 K, while the nighttime difference was \(-\)0.1 \(\pm\) 0.5 K. [PERSON] [12] proposed a machine learning algorithm trained using in situ and multispectral satellite data for extracting sea surface temperature (SST). The method employs Shapley values to assess feature importance and utilizes spectral bands and metadata for prediction, significantly enhancing the accuracy and reliability of SST estimation. [PERSON] [13] utilized Landsat 8 satellite data and applied the radiative transfer equation, single-channel algorithm, and split-window algorithm to retrieve lake surface temperature in the study area. The distribution of water temperature is influenced by numerous factors. [PERSON] [14] et al. investigated the surface temperature of levitating water droplets using infrared spectroscopy. In the water absorption band around 3 \(\upmu\)m, they established an inverse problem for determining the upper surface temperature by considering the directional spectral emissivity of the droplet surface and the spectral sensitivity of the detector. [PERSON] [15] assessed the simulation algorithms for four critical lake thermal processes--turbulent heat flux, wind-driven mixing, light extinction, and snow density--and analyzed the simulation results. This work offered practical parameterization strategies to enhance the one-dimensional lake thermal model, advancing the understanding of lake thermal dynamics.