A Geographically Random Machine Learning Model for GOME-2 Global Seamless Sun-Induced Chlorophyll Fluorescence Downscaling Products With High Spatiotemporal Resolution

Several downscaled datasets of sun-induced chlorophyll fluorescence (SIF) enhance the quality of raw SIF satellite retrievals and offer a better perspective for monitoring terrestrial ecosystems. They are, however, still under pressure in studies that rely on long-term fine-scale observations, such as drought monitoring, climate capture, and gross primary productivity (GPP) estimation, due to persistent temporal and spatial discrimination deficits, spatial gaps, or nonnegligible numerical errors. These limitations are especially noticeable in GOME-2 and its related datasets. This study presented a new machine learning model [called “Geographically random light gradient boosting machine” (GR-LGBM)] and successfully produced a seamless dataset of global daily-mean SIF (GR-LGBM produced SIF) with a spatiotemporal resolution of 0.01° and 8 days for the period 2008–2018, based on GOME-2 SIF, visible-near-infrared reflectance, meteorological, and radiological variables. GR-LGBM performed well in the task of establishing the mapping relationship between the original SIF retrieval and the explanatory variables (fivefold cross-validated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{2}$ </tex-math></inline-formula>: 0.885), outperforming widely used machine learning models such as extreme random forest (EXT), light gradient boosting machine (LGBM), random forest (RF), categorical boosting (CatBoost), Cubist, eXtreme gradient boosting (XGBoost), neural network (NN), and possesses good temporal prediction ability and spatial robustness. The GRSIF001 was highly consistent with SIF satellite retrievals from GOME-2, OCO-2, TROPOMI, and ground-based SIF site observations, demonstrating the spatiotemporal accuracy and temporal scalability of GRSIF001. Additionally, evidence that GRSIF001 precisely captured the actual photosynthesis in vegetation was presented. The downscaling model is better suited for characterizing spatial and temporal heterogeneity of geographic variables, and GRSIF001 will provide greater value in accurately understanding the dynamics of terrestrial photosynthesis.