Global monitoring of vegetation conditions and GPP from 30 years of passive microwave observations
Authors: Moesinger Leander , Wild Benjamin , Zotta Ruxandra-Maria , van der Schalie Robin , Teubner Irene , Forkel Matthias , Sitch Stephen , de Jeu Richard , Dorigo Wouter 
Published: March 2022

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&lt;p&gt;&lt;span&gt;Microwave radiation emitted by the Earth surface is&amp;#160;attenuated by vegetation. The magnitude of attenuation is commonly referred to as Vegetation Optical Depth (VOD) and depends on several factors, such as the water content of the vegetation and the wavelength of the sensor. It can be derived from space-borne microwave radiometers and has been explored for multiple applications including drought and biomass monitoring. Here, we present two&amp;#160;novel applications based on long-term multi-satellite observations of VOD: 1) the Standardized VOD Index (SVODI) for&amp;#160;monitoring vegetation condition and 2) modelling Gross Primary Production (VODCA2GPP). &lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;SVODI [1] is a standard normally distributed index indicating the condition of vegetation.&amp;#160;Traditional index generation methods struggle to combine data from multiple data sources and often either exhibit a changing variance over time due to changing input sensors, or are biased. We solve these issues with a new probabilistic merging method, which in principle can be applied to other datasets as well. We show that SVODI, which is an indicator of vegetation structure, biomass and water content, supplements existing vegetation condition indexes based on optical data, which mostly represent the greenness of the vegetation. In the presentation, we also assess spatio-temporal patterns and trends of extreme SVODI values.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;VODCA2GPP [2] is a global GPP dataset derived from VOD using a sink-driven approach. It uses a machine learning framework using the VOD Climate Archive (VODCA), a long-term coherent dataset based on multiple sensors [3], in combination with FLUXNET in situ GPP observations to predict GPP at a global scale for a period of 30 years. VODCA2GPP exhibits similar spatial and temporal patterns as existing observation-based global GPP datasets from FLUXCOM and MODIS, but with a consistent positive bias. While VODCA2GPP reveals no significant global trend, it does show several regional trends, some of which corroborate trends found in the TRENDY model ensemble.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Both datasets are available for free from open data repositories.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;[1]&amp;#160;Moesinger, L., Zotta, R.M., van der Schalie, R.,&amp;#160;Scanlon, T., de Jeu, R.A.M., Dorigo, W.&amp;#160;Monitoring Vegetation Condition using Microwave Remote Sensing: The Standardized Vegetation Optical Depth Index SVODI, in rev.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;[2] Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S., Dorigo, W. A. (2021). VODCA2GPP -- A new global, long-term (1988--2020) GPP dataset from microwave remote sensing. Earth System Science Data Discussions, 2021, 1&amp;#8211;37. &lt;/span&gt;&lt;span&gt;https://doi.org/10.5194/essd-2021-209&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;[3] Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177&amp;#8211;196, &amp;#160;&lt;/span&gt;&lt;span&gt;https://doi.org/10.5194/essd-12-177-2020&lt;/span&gt;&lt;span&gt;, 2020.&lt;/span&gt;&lt;span&gt;&lt;br&gt;&lt;/span&gt;&lt;/p&gt;
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