The TerrA-P project: towards a global monitoring system for terrestrial primary production

Field: Environmental Science (Physical Sciences)

&lt;p&gt;Most land surface models (LSM) require, inter alia, inputs of temperature and moisture to generate predictions of gross primary production (GPP). &amp;#160;But air temperature measured at an arbitrary height in (or above) the canopy may offer only a poor estimate of leaf temperature. &amp;#160;Differences between leaf and air temperature have been shown to vary temporally and spatially and, due to depressed transpiration, may be especially pronounced under conditions of low soil moisture availability. &amp;#160;The Sentinel-3 satellite program offers modellers estimates of the land surface temperature (LST) which for vegetated pixels can be adopted as the canopy (or leaf) temperature. &amp;#160;But retrieving plant-available moisture remains problematic and to date remote-sensing tools lack the ability to penetrate beyond the upper soil-layer to the root zone. &amp;#160;Could remotely-sensed estimates of LST offer a parsimonious LSM input by uniting information on leaf temperature and hydration - avoiding the need for explicit modelling of soil moisture effects? &amp;#160;In a modelling experiment, we hypothesised that agreement with flux-derived GPP estimates would be stronger for simulations forced with LST versus gridded meteorological air temperature and superior performance would be most evident in dry summers.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Using a first-principles, process-based, light use efficiency model (the P-model) that requires only a handful of input variables (not including soil moisture), we generated alternate GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. &amp;#160;Remotely-sensed temperature and greenness (the fraction of photosynthetically active radiation absorbed by vegetation, fAPAR) data were input from Sentinel-3 sources. &amp;#160;Pre-processing steps included interpolation and smoothing before averaging to ten-day timesteps. &amp;#160;Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. &amp;#160;We chose the years 2018-2019 to exploit the natural experiment of a pronounced European drought. &amp;#160;For each site, timesteps were assigned a drought index (Standardised Precipitation-Evapotranspiration Index) using a 30-year time-series of climatic water balance. &amp;#160;Unusually dry conditions, for a given site, were characterised as those having SPEI &lt; -1.5.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Overall, simulated GPP showed good agreement with flux-derived estimates, but the experimental effect on simulated GPP was modest and the hypothesis found only partial, biome-dependent support. &amp;#160;During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savannah sites. &amp;#160;For certain sites, we found pronounced early-season deltas with simulations consistently exceeding flux-derived GPP. &amp;#160;That finding was not general to whole biomes or both years. &amp;#160;We speculate that these deltas arose, in part at least, from fAPAR values inflated by neighbouring vegetation not incorporated in the flux-tower&amp;#8217;s footprint.&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;br&gt;This study advances the prospect for LSMs that will require as few parameters as possible and rely, as far as is practical, on remotely-sensed input data. &amp;#160;In subsequent steps, we envisage further experiments to assess (i) the desirability of adopting a seasonally weighted diurnal average LST versus the single morning overpass employed here and (ii) whether the Sentinel-3 LST pixel values can be usefully disaggregated to distinguish vegetation and bare ground components.&lt;/p&gt;