The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA
Authors: Moesinger Leander , Zotta Ruxandra , van der Schalie Robin , Forkel Matthias , Scanlon Tracy , Teubner Irene , de Jeu Richard , Dorigo Wouter 
Published: March 2020

&lt;p&gt;Since the late 1970s, spaceborne microwave radiometers have been providing measurements of radiation emitted by the Earth&amp;#8217;s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions and coverage need to be overcome.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA; Moesinger et al., 2019). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, Windsat and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely Ku-band (period 1987-2017), X-band (1997-2018) and C-band (2002-2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture&lt;sup&gt;1,2&lt;/sup&gt;.&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;br&gt;The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with Leaf Area Index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-Band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China and central north America. In summary, VODCA shows vast potential for monitoring spatial-temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such it complements existing long-term optical indices of greenness and leaf area.&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;1&lt;/sup&gt;Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739. https://doi.org/10.5194/essd-11-717-2019&lt;/p&gt;&lt;p&gt;&lt;sup&gt;2&lt;/sup&gt;Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001.&lt;/p&gt;