Deep learning framework for high spatiotemporal resolution monitoring of carbon uptake usng multi-source satellite imagery

With the increasing emphasis on climate change and carbon neutrality, accurately quantifying gross primary productivity (GPP) has become a key strategic objective. The spatiotemporal variability of GPP across vegetation types underscores the necessity of high-resolution data for precise estimation. While satellite imagery is a valuable tool for large-scale GPP monitoring, its effectiveness is constrained by trade-offs between spatial and temporal resolution, particularly impacting accuracy in heterogeneous vegetated areas. To address this limitation, we proposed a novel framework named UNified, high-resolution Intelligent carbon QUantification and Estimation (UNIQUE), which generates 30 m GPP maps by learning the spatial relationships between daily 500 m MODIS and 16-day 30 m Landsat imagery. The UNIQUE framework comprises two steps. In the first step, two independent artificial intelligence models were developed to estimate daily GPP using MODIS and Landsat vegetation indices tailored to their respective temporal resolutions, combined with a reanalysis of meteorological data. These models were trained and validated using 309 eddy- covariance flux observations from the Northern Hemisphere. As a result, GPPM represents the AI-based GPP estimated from MODIS data, while GPPL represents the AI-based GPP estimated from Landsat data. Among the various AI algorithms tested using AutoML packages, the light gradient boosting machine model demonstrated the best performance. For GPPM, it achieved an r of 0.80 and a root mean squared error (RMSE) of 2.47 gC/m2/day from a 20-fold spatial cross-validation. Similarly, for GPPL, the model achieved an r of 0.83 and an RMSE of 2.43 gC/m2/day. In the second step of UNIQUE, we downscaled GPPM to produce GPPL-like daily 30 m GPP maps using a generative AI model, the denoising diffusion probabilistic model (DDPM). This process was applied to South Korea, which is characterized by dominant mountainous regions and heterogeneous land cover. To produce reliable 30 m GPP maps corresponding to real-world land cover, two schemes were employed: (1) a DDPM model that uses only GPPM as input (GPPUNIQUE (S1)) and (2) a DDPM model incorporating high-resolution spatial topography information from 30 m digital elevation models and fractional land cover ratios within 30 m, derived from 1 m land cover data provided by the Korean Ministry of Environment (GPPUNIQUE (S2)). Training data were randomly extracted as 150 by 150-pixel patches, each covering 4,500 m × 4,500 m from 2020 to 2022. The test dataset was constructed using data from 2023. GPPUNIQUE (S2) outperformed both GPPUNIQUE (S1) and GPPM, demonstrating the lowest average RMSE (2.24 gC/m2/day). In contrast, GPPUNIQUE (S1) showed an RMSE of 3.36 gC/m2/day, which is a higher value compared to GPPM, which had an RMSE of 2.85 gC/m2/day. Incorporating auxiliary variables with high spatial information—here, topography and fractional land cover data—proved to be essential for producing stable generated images that accurately correspond to real-world land cover. GPPUNIQUE (S2) effectively identified carbon absorption sources that were previously undetectable with MODIS data alone. Furthermore, this approach enabled the analysis of spatiotemporal characteristics of GPP across different plant functional types, facilitating enhanced high-resolution carbon flux monitoring in diverse ecosystems.