Towards a better estimation of GPP for greenhouse gas accounting through the combined use of SIF, spherical grids and knowledge-guided AI
Some remote sensing signals provide valuable information only at spatial scales that are too coarse for comfort for various applications. Examples include sun-induced chlorophyll fluorescence (SIF), whose signal is related to gross primary productivity (GPP) of vegetation and how this is impacted by stress, or vegetation optical depth (VOD), which is related to how water content is distributed within a canopy, which itself is informative on forest biomass and structure. Within OEMC WP6, we are developing an EO spatial downscaling framework that will be specifically tailored towards improving carbon flux estimations. The spatial downscaling will employ finer resolution EO variables to achieve this super-resolution, but this will not be done merely numerically. It will instead combine our process-based knowledge of how these variables relate to each other to develop a hybrid modelling approach with knowledge-guided AI. This will further be implemented using a moving window adaptative approach over a spherical grid, which will be made possible by novel developments from OEMC WP3. The use case within OEMC is to develop SIF-based 1km spatial resolution GPP flux estimations based on measurements from the TROPOMI sensor on Sentinel-5P, which has a spatial resolution that is larger than 5 km. The main stakeholder for this product will be the Global Carbon Project (GCP), and in particular RECCAP, which aims to establish the greenhouse gas (GHG) budgets of large regions covering the entire globe at the scale of continents. Such endeavor would greatly benefit from the fine-level spatialized GPP fluxes we aim to provide. This use-case will largely leverage on in-situ data provided in OEMC WP4 for validation and calibration, particularly after specific developments to optimize the matching between in-situ points and remote sensing grids is achieved. This presentation will detail the blueprint for this task, which will combine synergistic efforts across various elements within the OEMC project.
