UNIQUE: UNified, High-Resolution Intelligent Carbon QUantification and Explanation

Satellite-based solar-induced chlorophyll fluorescence (SIF) has opened a new era in monitoring gross primary productivity (GPP), and near-infrared reflectance of vegetation (NIRv), an effective proxy for SIF, has enabled observations at high spatial resolution. However, the inherent trade-off between spatial and temporal resolution in remote sensing often leads to the loss of critical information on carbon cycling, particularly in heterogeneous and cloud-prone regions. To address this challenge, this study introduces the unified, high-resolution intelligent carbon quantification and explanation (UNIQUE) framework for high-spatiotemporal-resolution satellite-based GPP monitoring. UNIQUE comprises two components. Part 1 generates high-accuracy GPPL and GPPM from Landsat and the Moderate Resolution Imaging Spectroradiometer, respectively. Part 2 fuses these products to create GPPUNIQUE, which provides daily GPP estimates on a 30 m grid, with fine-scale spatial variability primarily derived from Landsat-based vegetation information. In Part 1, a light gradient boosting machine model using NIRvP, the product of NIRv and photosynthetically active radiation, as the key predictor achieved a root mean square error (RMSE) of 2.17–2.29 gC m-2 d-1, representing a 13–28% reduction relative to existing global GPP products. In Part 2, the proposed stochastic–deterministic combined architecture for fine-level regression (U-SCALER) achieved performance comparable to that of a deterministic UNET baseline. Its performance improved when available Landsat observations were incorporated, achieving an RMSE of 2.60 gC m-2 d-1 at test sites. U-SCALER also showed lower sensitivity to Part 1 errors and image-pair selection. Although meteorological drivers were incorporated at coarser spatial resolutions, GPPUNIQUE represents fine-scale spatial variability on a 30 m grid by leveraging Landsat-derived vegetation information. Overall, UNIQUE provides a practical approach for integrating satellite observations with different spatial and temporal characteristics. U-SCALER in Part 2 also contributes to the evaluation of generative modeling for image fusion and super-resolution. With potential extensions to multiple satellite sensors and to vegetation and carbon-related variables beyond GPP, UNIQUE could support more comprehensive carbon monitoring across diverse domains and spatiotemporal scales.