## 1 Introduction  
Solar-induced chlorophyll fluorescence (SIF) within the wavelengths ranging from 650 to 850 nm, and which has two peaks centered at 685 and 740 nm [1], is well known as a good proxy for photosynthetic activity [2; 3]. Numerous studies have shown that the gross primary productivity (GPP) of vegetation photosynthesis can be directly estimated using satellite, airborne, in-situ and ground level SIF data [4; 5; 6; 7; 8; 9]. Existing satellite fluorescence products include the Scanning Imaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY), Greenhouse gases Observing SATellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2), Orbiting Carbon Observatory-2 (OCO-2), Chinese Carbon Dioxide Observation Satellite (TanSat) and TROPOspheric Monitoring Instrument (TROPOMI) datasets. The first global fluorescence map is based on data acquired by the GOSAT satellite, which has a circular footprint and a spatial resolution of 10.5 km. However, due to the point observation, spatial sampling frequency and the low accuracy of single observations, the average spatial resolution of monthly satellite products is \(2^{\circ}\times 2^{\circ}\)[10; 11; 12]. The spatial resolution of the SCIAMACHY satellite is \(30\times 240\) km (\(30\) km \(\times\) 60 km in nadir mode), whereas the average spatial resolution of daily satellite products is \(1.5^{\circ}\times 1.5^{\circ}\)[10; 13; 14; 15]. Because GOME-2 carries out continuous sampling, the same as for SCIAMACHY, the spatial resolution of its synthetic fluorescence products is higher than that of GOSAT, and is about \(0.5^{\circ}\times 0.5^{\circ}\)[13; 15; 16]. TROPOMI, with a spatial resolution of 3.5 km \(\times\) 7 km and a swath of 2600 km, has produced daily global coverage since February 2018 [17; 18]. Apart from TROPOMI, the satellite SIF products described above are limited either by low spatial resolution or sparse sampling, and are severely disturbed by noise at low signal levels, which makes it difficult to use then for ecosystem monitoring [17]. The OCO-2 and TanSat SIF data, which have much smaller footprints (i.e., \(1.3\times 2.25\) km and \(2\times 2\) km), provide the chance of linking satellite SIF to flux tower-based GPP at the ecosystem scale [19; 20]. However, due to the sparse sampling, these data cannot be used for small-region applications in all parts of the globe. Therefore, the spatial expansion of high-resolution SIF data from OCO-2 and TanSat is of great significance for GPP estimation [21; 22]. SIF can be expressed as [23; 24; 25]  
\[SIF=PAR\cdot fPAR\cdot SIF_{yield} \tag{1}\]  
where \(PAR\) is the downwelling photosynthetically active radiation (\(PAR\)), \(fPAR\) is the fraction of PAR absorbed by vegetation, and \(SIF_{yield}\) is the fluorescence quantum yield at the canopy level. The SIF contains plant physiological and structural information together with the mixed effects of vegetation biochemistry (i.e., pigments) and structure [26; 27], whereas the reflectance provides only canopy structural and biochemic information. \(SIF_{yield}\), influenced by vegetation biochemistry, contains plant physiologic information. While \(fPAR\), which can be estimated using reflectance data, mainly contains vegetation structural information. [PERSON] et al. [22] and [PERSON] and [PERSON] [28] constructed the relationship between reflectance and SIF, and reflectance only contains vegetation structural information. Other researchers have taken the land-cover type, Vapor Pressure Deficit (VPD), air temperature, Evapotranspiration (ET) and Normalized Difference Vegetation Index (NDWI) into consideration [21; 29; 30; 31] in order to better model the SIF; however, this can only provide part of the physiological information provided by the SIF. According to [PERSON] et al. [32], \(SIF_{yield}\) is affected by NPQ and \(q_{L}\), and the solar radiation intensity is a dominant factor for these two physiological variables.