Abstract
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 (average correlation increase of 0.18). This improvement in temporal dynamic is larger for temperate and cold regions than for the tropics. For unbiased root-mean-square error (ubRMSE) and bias, the results are regionally diverse and are compensated in the global average. Improvements are observed in temperate and cold regions, while decreases in performance are obtained mainly in the tropics. The overall improvement when adding temperature was less than expected and thus may only partly explain previously observed differences between the global GPP data sets. On interannual timescales, 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. Exceptions from this rule were found in some regions: significant negative correlations between VOD-GPP residuals and SPEI were observed in the US corn belt, Argentina, eastern Europe, Russia and China, while significant positive correlations were obtained in South America, Africa and Australia. In these regions, the significant correlations 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.
Highlights
Vegetation optical depth (VOD) from microwave satellite observations provides the opportunity for studying large-scale vegetation dynamics due to its sensitivity to the vegetation water content and aboveground biomass
We find that the sensitivity of VOD to gross primary production (GPP) increases with temperature as shown by the partial dependency plots (Fig. 1)
In addition to the interannual variability, we find that the spring peak is more pronounced in GPPfluxcom and GPPmodis than in GPPvodtemp, which might point towards a surplus of carbohydrates in spring that are incorporated for building up biomass later in the year or may be related to differences in land cover
Summary
Vegetation optical depth (VOD) from microwave satellite observations provides the opportunity for studying large-scale vegetation dynamics due to its sensitivity to the vegetation water content and aboveground biomass. The VOD-GPP model relies on estimating carbon sink terms, i.e., net primary production (NPP) and autotrophic respiration (Ra), based on VOD as a proxy for aboveground living biomass. We have shown that the VOD-GPP model can represent temporal dynamics of GPP well but that it overestimates GPP, especially in temperate and boreal regions (Teubner et al, 2019). We hypothesize that this overestimation may be caused by a missing representation of temperature dependency of autotrophic respiration in the VOD-GPP model
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