Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA

Mucia, Anthony; Bonan, Bertrand; Albergel, Clément; Zheng, Yongjun; Calvet, Jean-Christophe

doi:https://doi.org/10.5194/bg-19-2557-2022

Articles | Volume 19, issue 10

https://doi.org/10.5194/bg-19-2557-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Microwave remote sensing for improved understanding of vegetation–water...

https://doi.org/10.5194/bg-19-2557-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 10

Research article

|

19 May 2022

Research article |

| 19 May 2022

Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA

Anthony Mucia, Bertrand Bonan, Clément Albergel, Yongjun Zheng, and Jean-Christophe Calvet

Supplement

https://doi.org/10.5194/bg-19-2557-2022-supplement

Data sets

The global long-term microwave vegetation optical depth climate archive VODCA (1.0) L. Moesinger, W. Dorigo, R. De Jeu, R. Van der Schalie, T. Scanlon, I. Teubner, and M. Forkel https://doi.org/10.5281/zenodo.2575599

ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 04.7 (https://data.ceda.ac.uk/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v04.7) W. Dorigo, W. Preimesberger, A. Pasik, L. Moesinger, R. Van der Schalie, S. Hahn, T. Scanlon, W. Wagner, A. Gruber, R. Kidd, C. Paulik, C. Reimer, and R. De Jeu https://doi.org/10.5285/0683e320d8634a37aa1d9ef62dd41a0d

Model code and software

SURFEX code CNRM http://www.umr-cnrm.fr/surfex/data/OPEN-SURFEX/open_surfex_v8_1_20210914.tar.gz

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Short summary

For the first time, microwave vegetation optical depth data are assimilated in a land surface model in order to analyze leaf area index and root zone soil moisture. The advantage of microwave products is the higher observation frequency. A large variety of independent datasets are used to verify the added value of the assimilation. It is shown that the assimilation is able to improve the representation of soil moisture, vegetation conditions, and terrestrial water and carbon fluxes.