Abstract
AbstractThe Orbiting Carbon Observatory‐2 (OCO‐2) collects solar‐induced chlorophyll fluorescence (SIF) at high spatial resolution along orbits (oco2_orbit), but its discontinuous spatial coverage precludes its full potential for understanding the mechanistic SIF‐photosynthesis relationship. This study developed a spatially contiguous global OCO‐2 SIF product at 0.05° and 16‐day resolutions (oco2_005) using machine learning constrained by physiological understandings. This was achieved by stratifying biomes and times for training and predictions, which accounts for varying plant physiological properties in space and time.oco2_005accurately preserved the spatiotemporal variations ofoco2_orbitacross the globe. Validation ofoco2_005with Chlorophyll Fluorescence Imaging Spectrometer airborne measurements revealed striking consistency (R2 = 0.72; regression slope = 0.96). Further, without time and biome stratification, (1)oco2_005of croplands, deciduous temperate, and needleleaf forests would be underestimated during the peak season, (2)oco2_005of needleleaf forests would be overestimated during autumn, and (3) the capability ofoco2_005to detect drought would be diminished.
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