Cross-comparing different avenues for improving our estimation of GPP by exploiting Sentinel remote sensing data

Abstract

Photosynthesis is a major driver of terrestrial ecosystem dynamics. Unfortunately, gross primary productivity (GPP), or the rate at which solar energy is captured and stored into sugar molecules during photosynthesis, cannot be directly measured from remote sensing (RS) signals. Several RS signals related to vegetation pigments and to canopy structure can, however, serve as proxies for GPP. They can further be combined with different types and degrees of modelling to derive spatio-temporal estimations of GPP. Different strategies exist to do so, which often vary with respect to how much they depend on an in-situ reference for GPP, the gold standard being those derived from eddy covariance (EC) measurements at flux tower sites.Here we investigate several such strategies with a specific goal: to explore the potential contribution of Sentinel satellites to improve GPP estimation. The Sentinel fleet is maintained by the European Union’s Copernicus programme, thereby guaranteeing a certain longevity and enabling the establishment of operational services that do not depend on single satellite missions. The main RS signals we consider are: the OLCI global vegetation index (OGVI) and OLCI terrestrial chlorophyll index (OTCI) from the Sentinel-3 OLCI instrument; daytime and night-time land surface temperature (LST) from Sentinel-3 SLSTR; and sun-induced chlorophyll fluorescence (SIF) from TROPOMI on-board of Sentinel-5-P. We further use time series of Sentinel-2 data to quantify the spatial homogeneity within the observational footprints of these coarser spatial resolution products in order to ensure a proper comparison to flux-tower data. The whole exercise is part of the Sen4GPP project funded by the European Space Agency (ESA).The three strategies we explore to derive GPP are: (1) empirical SIF-based estimation of GPP, including a version involving spatial downscaling to reach a finer resolution of SIF; (2) deterministic modelling based on a quantum yield light use efficiency (LUE) model calibrated on EC flux towers; and (3) purely data-driven machine learning (ML) based on EC measurements at flux towers using dedicated 10-fold cross-validation using the FLUXCOM-X framework. The cross-comparison is done for independent flux tower sites over Europe based on the Warm Winter 2020 database, covering the recent past (2018-2020) when TROPOMI SIF observations are available.The results indicate that the ML approach clearly outperforms the process-based LUE approach, which itself performs better than SIF. However, this order also reflects a decreasing reliance in flux tower data and possibly increasing capacity to extrapolate to situations not present in the learning dataset. The results further indicate that the ML approach using Sentinel data can perform better than a baseline using MODIS data alone, probably due to the inclusion of SIF information. Results also illustrate how ensuring the spatial consistency between grid and tower does improve performance, strengthening the rational for spatially downscaling coarse RS signals such as SIF. Overall, these encouraging results bode well for the potential use of Sentinel data to improve our current capacity to monitor biogeochemical process at global scale.