Global 0.1-Degree Monthly Mean Hourly Total Canopy Solar-Induced Chlorophyll Fluorescence Dataset Derived from Random Forest

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIFtotal_01) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIFtotal_01 showed a strong correlation (R2 = 0.81) and reduced root mean square error when compared with SIFtotal (2.89→2.8 mW m−2 nm−1), providing notable gains in broadleaved forests (R2: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m−2 nm−1). The SIFtotal_01 dataset revealed a distinct double-peak in the SIFtotal_01–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIFtotal_01 difference increased from 0.02 to 0.10 mW m−2 nm−1 across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10−3 nm−1), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m−2). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress.