Preprints
https://doi.org/10.5194/essd-2021-209
https://doi.org/10.5194/essd-2021-209

  24 Jun 2021

24 Jun 2021

Review status: this preprint is currently under review for the journal ESSD.

VODCA2GPP – A new global, long-term (1988–2020) GPP dataset from microwave remote sensing

Benjamin Wild1, Irene Teubner1,2, Leander Moesinger1, Ruxandra-Maria Zotta1, Matthias Forkel3, Robin van der Schalie4, Stephen Sitch5, and Wouter Arnoud Dorigo1 Benjamin Wild 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
  • 4VanderSat, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
  • 5College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QE, UK

Abstract. Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other.

Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble).

These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures.

The VODCA2GPP dataset is available at TU Data (https://doi.org/10.48436/1k7aj-bdz35; Wild et al., 2021).

Benjamin Wild et al.

Status: open (until 19 Aug 2021)

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  • RC1: 'Comment on essd-2021-209', Anonymous Referee #1, 22 Jul 2021 reply

Benjamin Wild et al.

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VODCA2GPP Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch and Wouter Arnoud Dorigo https://doi.org/10.48436/1k7aj-bdz35

Benjamin Wild et al.

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Short summary
Gross Primary Production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as filter for our atmosphere from the primary greenhouse gas CO2. We developed a new GPP dataset that is based on Vegetation Optical Depth from microwave remote sensing and temperature. Thus it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Our analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.