Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018

Abstract

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs.

Keywords

• land surface temperature
• drought
• remote sensing
• GPP
• ICOS
• eddy covariance