25 Nov 2020

25 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal BG.

Impact of temperature and water availability on microwave-derived gross primary production

Irene E. Teubner1,2, Matthias Forkel3, Benjamin Wild1, Leander Mösinger1, and Wouter A. Dorigo1 Irene E. Teubner et al.
  • 1Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
  • 2Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Hohe Warte 38, 1190 Vienna, Austria
  • 3Environmental Remote Sensing Group, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Helmholtzstraße 10, 01069 Dresden, Germany

Abstract. Vegetation optical depth (VOD) from microwave satellite observations has received much attention in global vegetation studies in recent years due to its relationship to vegetation water content and biomass. We recently have shown that VOD is related to plant productivity, i.e. gross primary production (GPP). Based on this relationship between VOD and GPP we developed a theory-based machine learning model to estimate global patterns of GPP from passive microwave VOD retrievals. The VOD-GPP model generally showed good agreement with site observations and other global data sets in temporal dynamic but tended to overestimate annual GPP across all latitudes. We hypothesized that the reason for the overestimation is the missing effect of temperature on autotrophic respiration in the theory-based machine learning model. Here we aim to further assess and enhance the robustness of the VOD-GPP model by including the effect of temperature on autotrophic respiration within the machine learning approach and by assessing the interannual variability of the model results with respect to water availability. We used X-band VOD from the VOD Climate Archive (VODCA) data set for estimating GPP and used global state-of-the art GPP data sets from FLUXCOM and MODIS to assess residuals of the VOD-GPP model with respect to drought conditions as quantified by the Standardized Precipitation and Evaporation Index (SPEI).

Our results reveal an improvement in model performance for correlation when including the temperature dependency of autotrophic respiration. This increase in temporal dynamic is largest for regions outside the tropics. For error and bias, the results are regionally diverse and are compensated in the global average. On interannual time scales, estimates of the VOD-GPP model agree well with GPP from FLUXCOM and MODIS. We further find that the residuals between VOD-based GPP estimates and the other data sets do not significantly correlate with SPEI which demonstrates that the VOD-GPP model can capture responses of GPP to water availability even without including additional information on precipitation, soil moisture or evapotranspiration. However, some regions reveal significant correlations between VOD-GPP residuals with SPEI, which may indicate different plant strategies for dealing with variations in water availability.

Overall, our findings support the robustness of global microwave-derived estimates of gross primary production for large-scale studies on climate-vegetation interactions.

Irene E. Teubner et al.

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Irene E. Teubner et al.

Irene E. Teubner et al.


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Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry- or wetness conditions.