Abstract
Abstract Significant uncertainties in terrestrial carbon fluxes exist in regions with limited ground-based observations, impacting our understanding of ecosystem carbon dynamics and emission reduction needs. This is particularly true for areas with sparse measurement networks, like India. To address this, we explore the potential of satellite measurements from various missions such as Sentinel-5 Precursor (S5P) and the Orbiting Carbon Observatory-2 (OCO-2) to improve terrestrial biosphere CO2 fluxes of India. We follow a data-driven approach, which simulates spatial and temporal distributions of Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), and ecosystem Respiration (Reco). We improve these model predictions by additionally using satellite-based Solar Induced chlorophyll Fluorescence (SIF), Soil Temperature (ST), and Soil Moisture (SM) specific to the vegetation classes of the domain. Different model refinements were performed to present the improved hourly distributions of terrestrial biospheric CO2 fluxes on a 0.1◦×0.1◦ grid from 2012 to 2020. Among them, the best-performing model simulations show reasonable agreement with eddy covariance observations for 2012 - 2018. For example, our best NEE and GPP predictions are highly correlated with observations with squared correlation coefficient (R2) values of 0.68 (NEE) and 0.74 (GPP) at the monthly scale for 2018. Based on our improved estimations, the annual NEE and GPP show values within the range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively over India for 2012 - 2020. Our novel approach and findings highlight the potential of satellite-based SIF measurements to detail the ecosystem-scale vegetation responses across various biomes in India. The use of satellite observations, as demonstrated in this study, offers a scalable solution for regions lacking sufficient ground-based observations to estimate biospheric carbon fluxes reliably.
Published Version
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