Articles | Volume 20, issue 5
https://doi.org/10.5194/bg-20-1027-2023
https://doi.org/10.5194/bg-20-1027-2023
Research article
 | 
16 Mar 2023
Research article |  | 16 Mar 2023

Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties

Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra

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Cited articles

Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. 
Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and McVicar, T. R.: Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data, Biogeosciences, 10, 6657–6676, https://doi.org/10.5194/bg-10-6657-2013, 2013. 
Apley, D. W. and Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models, J. R. Stat. Soc. B, 82, 1059–1086, https://doi.org/10.1111/RSSB.12377, 2020. 
Baur, M. J., Jagdhuber, T., Feldman, A. F., Akbar, R., and Entekhabi, D.: Estimation of relative canopy absorption and scattering at L-, C- and X-bands, Remote Sens. Environ., 233, 111384, https://doi.org/10.1016/j.rse.2019.111384, 2019. 
Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Prigent, C., Wagner, F. H., and Kerr, Y. H.: Influence of surface water variations on VOD and biomass estimates from passive microwave sensors, Remote Sens. Environ., 257, 112345, https://doi.org/10.1016/j.rse.2021.112345, 2021. 
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Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
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