Satellite SIF and Ground EC Observation Jointly Constrained Estimation of Global Gross Primary Productivity

Accurate estimation of global gross primary productivity (GPP) is fundamental for understanding terrestrial carbon cycling. Eddy covariance (EC) flux observations provide reliable site-scale GPP estimates, but the spatially sparse distribution limits their applicability at large scales. Satellite-based solar-induced chlorophyll fluorescence (SIF) has emerged as a promising proxy for large-scale GPP estimation; however, current satellite SIF observations also suffer from limited spatiotemporal coverage, and uncertainties remain in the SIF–GPP conversion. Moreover, conventional machine learning models trained solely on EC observations often exhibit limited spatial generalization due to the scarcity of spatially representative training samples.

To address these challenges, this study proposes a satellite–ground jointly constrained framework that integrates EC flux measurements and satellite SIF observations using transfer learning and multi-task learning techniques to exploit the complementary strengths of both data sources for global GPP estimation. First, for TROPOMI SIF data that has global spatial coverage but short temporal records, SIF is treated as a source domain to pre-train the model, which is then fine-tuned using long-term EC-derived GPP data as a target domain. This transfer learning-based model (SIFTML) demonstrates improved spatial generalization compared to models trained solely on SIF or EC data, effectively reducing systematic underestimation and overestimation at high and low GPP levels, respectively, while remaining insensitive to the magnitude scaling of source-domain SIF inputs.

Second, for the spatially sparse and track-like distributed OCO-2 SIF observations, a multi-task learning framework based on a mixture-of-experts architecture is developed. A physically constrained loss function derived from the SIF–GPP relationship is introduced to simultaneously achieve seamless SIF reconstruction and high-accuracy GPP estimation by jointly leveraging SIF and EC constraints. Results indicate that the multi-task model outperforms traditional single-task approaches in both GPP estimation and SIF reconstruction.

Overall, this study provides a new paradigm for long-term, high-accuracy global GPP estimation by alleviating limitations associated with the spatiotemporal coverage of ground EC and satellite SIF observations, as well as the uncertainties in SIF–GPP conversion, thereby offering improved support for global carbon cycle research.