CNSIF: A reconstructed monthly 500-m spatial resolution solar-induced chlorophyll fluorescence dataset in China

Abstract. Satellite-derived solar-induced chlorophyll fluorescence (SIF) offers valuable opportunities for monitoring large-scale ecosystem functions. However, the inherent trade-off between satellite scan range and spatial resolution, along with incomplete spatial coverage and irregular temporal sampling, limits its broader application. In this study, we developed a 500-m spatial resolution monthly SIF dataset for the China region (CNSIF) from 2003 to 2022, using a data-driven deep learning approach based on high-resolution apparent reflectance and thermal infrared data. The results indicate that CNSIF effectively captures the spatial patterns of vegetation photosynthetic activity and exhibits a positive annual growth trend of 0.054. Comparisons with tower-based observations validated the ability of CNSIF to track changes in photosynthetic intensity over time across different ecosystems. Furthermore, the strong correlation (R²_{_2016} = 0.768, R²_{_2020} = 0.743; P<0.001) between CNSIF and the MODIS monthly Gross Primary Production (GPP) product demonstrates its potential for estimating carbon flux. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics across China and estimating fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. The CNSIF dataset is available at https://doi.org/10.6084/m9.figshare.27075145 (Du et al., 2024).