## 1 Introduction  
Land Surface Temperature (LST) is a key parameter for understanding the physical and chemical interactions between a land surface and atmosphere, and plays a crucial role in several priority research areas of the International Geosphere-Biosphere Programme (IGBP), such as drought monitoring, evapotranspiration assessment, soil moisture determination, urban heat island effect detection, and global climate change analysis [1, 2, 3, 4]. In these fields, accurate, continuous, and comprehensive LST data are essential for research. However, the traditional methods of obtaining surface temperature, especially those relying on thermal infrared sensors, are often limited by cloud cover [5, 6, 7], making it difficult to provide continuous all-weather monitoring. This limitation has driven the exploration and development of reliable all-weather LST retrieval methods, highlighting the urgent need for new research directions and technological innovations. Microwave (PMW) radiation can penetrate cloud cover, but it has a relatively lower spatial resolution [8]. Therefore, it is evident that TIR and PMW have complementary spatial layouts and data accuracy. As a result, the fusion of TIR and PMW data has become a promising approach for obtaining high-quality, complete LST data [9; 10]. To date, traditional fusion methods for TIR and PMW include the Bayesian Maximum Entropy (BME) method by [PERSON], [PERSON], and [PERSON] [11], temperature spatial interpolation used by [PERSON] and [PERSON] [12; 13], cloud proportion weighting [14], and time component decomposition by [PERSON] [15]. Most traditional methods yield high-precision LST fusion results; however, they all have limitations. For instance, the cloud proportion weighting method may suffer from reduced accuracy and instability when cloud cover is extensive. The temperature spatiotemporal interpolation method can result in significant interpolation errors when data distribution is uneven and has high computational complexity. The time component decomposition method is heavily dependent on historical data, with a complex model that is difficult to adjust. The BME method is computationally demanding and requires stringent probability assumptions. The limitations of these traditional methods make them less applicable in large-scale and high-precision scenarios. In contrast, machine learning methods [16; 17] are more capable of handling complex data processing and analysis tasks due to their superior performance and versatility. Machine learning and deep learning techniques, with their ability to model complex data relationships, provide promising new approaches for all-weather LST prediction. Although these methods require more computational resources, they excel at identifying underlying patterns, capturing nonlinear relationships, and delivering precise predictions. To address the technical challenges in this field, this study proposes an innovative machine learning-based method for generating all-weather LST, which fully leverages the potential of domestic Fengyun-3C satellite data [18]. This method is the first to combine Fengyun satellite data, including vegetation index, infrared temperature, and microwave temperature, for surface temperature reconstruction. Unlike previous studies that typically used high-resolution satellite data such as MODIS and Sentinel, this experiment relies entirely on Fengyun satellite data, aiming to explore the potential of this dataset. This represents a novel approach in terms of the data used. Additionally, the SSA method was applied to fill orbital gaps in the microwave and infrared data, further enhancing the novelty of the experimental methodology. Moreover, a stacked model, rather than a single model, was employed for training, highlighting another innovative aspect of the approach. The novel approach integrates the Ridge Regression and Random Forest ensemble algorithm, with the two methods complementing each other.