A carbon sink-driven approach to estimate gross primary production from microwave satellite observations
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A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

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A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

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dc.contributor.author Teubner, Irene E.
dc.contributor.author Forkel, Matthias
dc.contributor.author Camps-Valls, Gustau
dc.contributor.author Jung, Martin
dc.contributor.author Miralles, Diego G.
dc.contributor.author Tramontana, Gianluca
dc.contributor.author Van der Schalie, Robin
dc.contributor.author Vreugdenhil, Mariette
dc.contributor.author Mösinger, Leander
dc.contributor.author Dorigo, Wouter A.
dc.date.accessioned 2020-12-14T19:04:37Z
dc.date.available 2020-12-14T19:04:37Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10550/76673
dc.description.abstract Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.
dc.language.iso eng
dc.relation.ispartof Remote Sensing of Environment, 2019, vol. 229, p. 100-113
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Teubner, Irene E. Forkel, Matthias Camps Valls, Gustavo Jung, Martin Miralles, Diego G. Tramontana, Gianluca Van der Schalie, Robin Vreugdenhil, Mariette Mösinger, Leander Dorigo, Wouter A. 2019 A carbon sink-driven approach to estimate gross primary production from microwave satellite observations Remote Sensing of Environment 229 100 113
dc.subject Teledetecció
dc.subject Vegetació
dc.title A carbon sink-driven approach to estimate gross primary production from microwave satellite observations
dc.type info:eu-repo/semantics/article
dc.date.updated 2020-12-14T19:04:38Z
dc.identifier.doi https://doi.org/10.1016/j.rse.2019.04.022
dc.identifier.idgrec 140535

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