## 1 Introduction  
Due to the close association with photosynthesis, chlorophyll fluorescence has become an important indicator for characterizing terrestrial photosynthesis and is widely used in fields of ecosystem carbon cycling, vegetation drought stress monitoring, and pest and disease detection [1, 2, 3]. In recent years, several studies have evaluated satellite-derived SIF signals, mainly from satellites that cover the SIF emission bands, such as GOSAT, OCO-2, TanSat, and TROPOMI [4, 5, 6]. Satellite SIF signals have shown strong correlations with the gross primary productivity (GPP) of terrestrial ecosystems at both global and regional scales [7, 8]. However, the early launched instruments, such as GOSAT and GOME-2, had relatively low spatial and temporal resolutions, which were insufficient for ecosystem monitoring requirements. Although the new instruments on satellites, such as OCO-2 and TROPOMI, provide high spatial resolution (covering an area of several kilometers), they still have some limitations in terms of spatial sampling and archival time. Therefore, in order to provide SIF data products with high resolution, spatial continuity, and high temporal frequency, it is necessary to reconfigure them. Reconstruction studies of different satellite products primarily focus on two aspects. The first is spatial reconstruction, which targets satellite products with very coarse spatial resolution, such as SCIAMACHY and GOME-2, whose spatial resolution is on the order of tens of kilometers. Spatial downscaling enhances the spatial detail of these products, allowing for better alignment with ecosystem flux observations [9; 10]. In recent years, with the continuous improvement of satellite spatial resolution, spatial reconstruction products for high-resolution datasets have also emerged, such as the TroDSIF product developed by [PERSON]'s team, which is a new SIF product with a spatial resolution of 500 m generated from the original 0.05\({}^{\circ}\) TROPOMI SIF [11]. The second is temporal reconstruction. Recently launched satellites like TROPOMI offer high spatial resolution and wide coverage but have relatively short data records, limiting the application. By developing SIF prediction models, it is possible to estimate historical SIF values and generate high-precision data products beyond the satellite's operational lifespan, which is highly beneficial for studying long-term ecosystem changes [12]. For example, [PERSON]'s team's GOSIF project performed temporal reconstruction on raw OCO-2 SIF data, extending its archival range back to the year 2000 [13]. In terms of data reconstruction methods, the main approach is to establish the relationship between SIF and its related explanatory variables, using either physical models or machine learning techniques. Physically based methods are typically built on the concept of light use efficiency to develop nonlinear models relating SIF to relevant variables. For example, [PERSON] et al. constructed a nonlinear model to characterize SIF using NDVI, ET, and LST [14]. These methods can intuitively illustrate the relationships between variables and SIF but require highly accurate input data. A growing number of studies have adopted machine learning approaches [15; 16], as they allow the incorporation of more variables and better capture complex nonlinear relationships among them. Machine learning has been widely applied in the downscaling of satellite data, with various studies using it to enhance the spatiotemporal resolution of satellite products [17; 18]. Each machine learning method has its strengths and weaknesses and may vary in suitability depending on the spatial scale. At the global scale, tree-based models are often adopted due to their high computational efficiency and robust predictive accuracy when handling large-scale aggregated data [19; 20]. In contrast, regional-scale studies typically focus on using higher-resolution data to capture more complex nonlinear relationships.