OSRNet: A One-Step Learned Spatial Redistribution Convolutional Neural Network for satellite SIF downscaling

Abstract

Solar-induced chlorophyll fluorescence (SIF) is a direct proxy for photosynthetic activity, yet existing satellite SIF products are constrained by coarse spatial resolution, limiting their application in ecological and agricultural studies. In this work, we propose a One-Step Learned Spatial Redistribution Convolutional Neural Network (OSRNet) that downscales 0.05° TROPOMI SIF to 0.005° by learning spatially adaptive redistribution fields from high-resolution drivers, which allocate coarse-resolution satellite SIF into fine-resolution grids. Based on this framework, we generate RSIF, a global 16-day 0.005° SIF dataset for 2018–2020. Comprehensive evaluation against both satellite and tower-based SIF shows that RSIF maintains strong consistency with TROPOMI observations (R² = 0.976, RMSE = 0.036) while recovering fine-scale spatial details. OSRNet substantially outperforms established direct prediction methods such as RF and SIFNet, and, compared with post hoc corrected RF approach from prior studies, achieves the highest R² across all tower sites and generally the lowest RMSE, enabling more accurate representation of seasonal dynamics with improved spatial fidelity.