## 2 Materials and Methods  
### Materials  
#### 2.1.1 Explanatory Variable  
Explanatory variables are the parameters used to scale down the SIF data and are the input features of the model. Based on the light energy utilization model of GPP [16], a similar SIF formula can be obtained:  
\[SIF=SIFyield\cdot APAR=F\cdot fesc\cdot PAR\cdot fPAR \tag{1}\]  
where SIFyield is the amount of fluorescence emitted per unit photon absorbed, generally expressed as the product of fluorescence quantum yield F and fluorescence escape probability _fesc_; PAR denotes the photosynthetically active radiation, i.e., the portion of sunlight that can be absorbed by vegetation, which is usually between 400 and 800 nm; fPAR is the proportion of photosynthetically active radiation absorbed by the vegetation, which is related to the nature of the vegetation itself; their product is expressed as APAR, the absorbed photosynthetically active radiation.  
This physical relationship forms the theoretical basis for selecting explanatory variables in the downscaling model. Our goal is to use machine learning algorithms to build predictive models from these driving factors to reconstruct high-resolution SIF. Since specific values such as fluorescence quantum yield are generally difficult to measure at the satellite scale, this study, based on the above principles, selects typical factors influencing vegetation photosynthesis as explanatory variables. These variables are categorized into three groups based on their source of influence: vegetation variables, meteorological variables, and land use/land cover type variables. Vegetation variables reflect the growth status of vegetation, such as vegetation indices and reflectance. Meteorological variables represent the environmental conditions for vegetation growth and are key factors affecting vegetation dynamics, including temperature and radiation. Land use types, as categorical variables, represent different vegetation types. Studies have shown that different vegetation types exhibit different levels of SIF intensity [4].  
The dataset of explanatory variables used in this study includes reflectance, normalized difference vegetation index (NDVI), land surface temperature (LST), photosynthetically active radiation (PAR), and land use/land cover (LULC). Reflectance, NDVI, LST, and LULC data were obtained from MODIS sensors aboard the Terra and Aqua satellites, which provide extensive information on land surface characteristics. We selected the MCD43C4 daily nadir BRDF-adjusted reflectance product [24], which offers 0.05\({}^{\circ}\) daily reflectance data across 7 spectral bands. NDVI was calculated using the red and near-infrared bands. LST data were derived from the MOD11C1 product [25], which provides 0.05\({}^{\circ}\) daily land surface temperature data. Land cover data were obtained from the MCD12C1 product [26], which provides land cover classification at 0.05\({}^{\circ}\) resolution and includes three classification schemes. This study adopted the IGBP classification scheme, which includes 17 land cover types, as shown in the caption of Figure 1. Since this data are discrete categorical data, we employed a one-hot encoder to convert them from a categorical form to a numerical form, aligning with the input data type recognizable by machine learning algorithms. PAR represents the portion of light that is effective for vegetation photosynthesis. PAR data were downloaded from the Clouds and Earth's Radiant Energy System (CERES) SYN1 deg global PAR product [27], which has a spatial resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) and a daily temporal resolution. This dataset includes both direct and diffuse radiation under all-sky and clear-sky conditions, with total PAR calculated as the sum of all-sky direct and diffuse components.