RSIF: A 0.005° Global SIF Dataset Based on an End-to-End Convolutional Neural Network with Spatial Redistribution – 2020

To improve the spatial resolution and preserve the spatial fidelity of TROPOMI Solar-Induced chlorophyll Fluorescence (SIF), a global 0.005-degree SIF product (namely, RSIF) from May 2018 to December 2020 was generated using the proposed One-Step Learned Spatial Redistribution Convolutional Neural Network (OSRNet). The OSRNet model directly redistributes coarse-resolution TROPOMI SIF into fine grids using high-resolution auxiliary variables, including MODIS reflectance, ERA5 reanalysis, GEBCO DEM, and cos(SZA). This dataset has been validated against both the original TROPOMI SIF and long-term tower-based SIF from five flux sites, demonstrating improved agreement with coarse-resolution inputs and better representation of fine-scale spatial details compared to traditional downscaling methods. RSIF represents clear-sky SIF and if needed, it can be readily converted to all-sky SIF using established correction factors. This database contains the subset of the RSIF product for the year 2020.