Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China’s Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF’s reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R² ≥ 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m⁻² nm⁻¹ sr⁻¹) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (SIF_ECA), and EC footprint-weighted SIF (SIF_ECW) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R² increased from the range of 0.47–0.65 to 0.78–0.85). SIF_ECW exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship.

The leaf area index (LAI) is a crucial variable in climate, ecological, and land surface modeling. However, the estimation of the LAI from coarse-resolution remote sensing data can be affected by the spatial scaling bias, which arises from the nonlinearity of retrieval models and the heterogeneity of the land surface. This study provides an algorithm named Arithmetic Mean and Geometric Mean (AM–GM) to correct the spatial scaling bias. It is established based on negative logarithmic functions and avoids second-order stationarity. In this algorithm, relationships are derived between the scaling bias of LAI and the arithmetic and geometric means of directional gap probability for two commonly used remote sensing models, the Beer–Lambert law and a semi-empirical transfer function, respectively. According to the AM–GM algorithm, the expression representing the model nonlinearity is derived and utilized for the analysis of LAI scaling bias. Furthermore, the AM–GM algorithm is simplified by a linear relationship, which is constructed between two quantities related to the directional gap probability between two specific resolutions. Two scenes simulated by the LargE-Scale remote sensing data and image Simulation framework (LESS) model and three sites are used to evaluate the proposed algorithm and analyze the scaling bias of LAI. The validation results show that the AM–GM algorithm provides accurate correction of LAI scaling bias. The analyses based on the AM–GM algorithm demonstrate that the scaling bias of LAI increases with the increase in the LAI value, with stronger surface heterogeneity and coarser spatial resolution. The validation results of the simplified AM–GM algorithm demonstrate that at the Sud-Ouest site, the absolute value of the bias for the estimated LAI decreases from 0.10, 0.22, 0.29, and 0.31 to 0.04, 0.01, 0.04, and 0.05 at 200 m, 500 m, 1000 m, and 1500 m resolutions, respectively. In conclusion, the proposed algorithm is effective in the analysis and correction of the scaling bias for coarse-resolution LAI.