## 1 Introduction  
Solar-induced chlorophyll fluorescence (SIF) offers a more direct method for assessing vegetation photosynthesis compared to traditional vegetation indices [1, 2, 3, 4], particularly in estimating gross primary productivity (GPP) and monitoring vegetation health status [5, 6, 7]. However, satellite-derived SIF data often suffer from low low spatial resolution and sparse sampling, which hinders the detection of subtle variations in small-scale regions. This issue is especially critical in agricultural and ecological research [8, 9]. Therefore, conducting downscaling studies on SIF data is of great importance to enhance the applicability in these fields [10, 11, 12]. Satellite-based SIF products are widely utilized for large-scale vegetation monitoring, productivity estimation, and spatial-temporal pattern analysis [13, 14, 15, 16]. Nevertheless, these products are limited by their coarse resolution and fragmented data coverage. Key satellites involved in SIF observation include the Scanning Imaging Absorption Spectrometer for the Atmosphere (SCIAMACHY), the Greenhouse Gas Observing Satellite (GOSAT), the Global Ozone Monitoring-2 (GOME-2), the Orbiting Carbon Observatory-2 (OCO-2), the China Carbon Dioxide Observing Satellite (TanSat), and the Tropospheric Monitoring Instrument (TROPOMI). GOSAT was the first satellite to provide global SIF data products [17], with a spatial resolution of 10.5 km and monthly scale products at a resolution of 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\). SCIAMACHY continuously samples data with an average resolution of 1.5\({}^{\circ}\)\(\times\) 1.5\({}^{\circ}\) for daily products, while GOME-2 offers SIF products at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)[18]. On the other hand, the SIF products from OCO-2 and TanSat have smaller sampling footprints (1.3 \(\times\) 2.25 km for OCO-2 and 2 \(\times\) 2 km for TanSat), but their spatial discretization makes it challenging to achieve global coverage. Although TROPOMI provides continuous SIF data, which is advantageous compared to the other satellites, its spatial resolution remains relatively coarse at 3.5 km \(\times\) 7 km. This still poses challenges for applications that require high spatial resolution, such as monitoring crop growth, estimating crop yields, and tracking pests and diseases at small regional scales [19]. Reconstructing SIF can address the challenges of low spatial resolution and data discretization. For example, [PERSON] used MODIS reflectance, NDVI, COS (SZA) and reanalyzed meteorological temperature data to develop a downscaling model for the 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) GOME-2 SIF product through the Random Forest model. This model enabled the generation of a global DSIF product with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)[20]. Similarly, the discrete strip TanSat SIF (2 km \(\times\) 2 km) was also generated into the global coverage spatial scale extension product TanSIF with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) based on the same driving variables and algorithms [21]. [PERSON] and [PERSON] applied the Cubist model, using EVI, PAR, VPD, and T to reconstruct the discontinuous OCO-2 SIF product into the long-term global GOSIF product at a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)[22]. [PERSON] et al. combined the OCO-2 and MODIS MCD43A4 products, incorporating one to seven reflectance bands, to reclassify land cover types through [PERSON]'s ideas [23], producing 16-day SIF products with a resolution of 0.05\({}^{\circ}\)[24], and it was found that they have significant advantages in plant growth analysis. [PERSON] et al. utilized OCO-3 based on the continuous GK-2A near-infrared, blue, rate, and red bands, as well as shortwave radiation and VPD as the driving variables to study the reconstruction of the SIF for the continents of East Asia and Oceania using the XGBoost model and to produce the dataset GEOSIF [25].