EGU2020-10102, updated on 15 Dec 2020
https://doi.org/10.5194/egusphere-egu2020-10102
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Assessing prospects of sub-daily radar-observations to improve the understanding of soil- and vegetation dynamics.

Raphael Quast1, Wolfgang Wagner1, Jean-Christophe Calvet2, Clèment Albergel2, Bonan Bertrand2, Luca Brocca3, Paolo Filippucci3, and Stephen Hobbs4
Raphael Quast et al.
  • 1Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria (raphael.quast@geo.tuwien.ac.at)
  • 2CNRM|Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France
  • 3Research Institute for Geo-Hydrological Protection, National Research Council, 06128 Perugia, Italy
  • 4Cranfield University, Cranfield, Bedford, MK43 0AL, UK

The geosynchronous C-band SAR mission Hydroterra (initially called G-CLASS) is one of three candidate missions for ESA's upcoming Earth Explorer 10 programme (scheduled for launch in 2027-2028). While current available satellite-borne C-band radar instruments have a rather long re-visit time (ASCAT METOP A,B,C: daily, Sentinel-1 A,B: 3-6 days), the fact that the Hydroterra satellite would be in a geosynchronous orbit opens the possibility for a C-band radar dataset with much finer temporal resolution. The image-formation process and operations concept incorporated within the Hydroterra system however requires choices of spatial and temporal resolution of the final product.

The presented experiment is intended to highlight potential benefits associated with high temporal sampling of Hydroterra observations for the understanding of daily and sub-daily soil-moisture and vegetation processes. In order to generate a backscatter dataset that simulates observations at high temporal resolution, a parametric first-order radiative transfer model (RT1) [1] is first calibrated with incidence-angle dependent Sentinel-1 C-band backscatter data as well as auxiliary soil-moisture (SM) and leaf-area-index (LAI) timeseries provided by the SURFEX-ISBA [2] land-surface model over south-western France. Once the model-parameters are obtained, a simulated backscatter timeseries at high temporal resolution is generated by performing a forward-simulation using the retrieved model-parametrizations and auxiliary SM and LAI datasets at hourly intervals.

The simulated dataset is then used (in conjunction with the LAI dataset) to simulate a retrieval of SM under a set of possible observation conditions, e.g. varying soil- and vegetation properties (represented via the RT1 model parameters), different temporal resolutions (1,3,6,12 hourly), incidence-angles and noise-levels. In a final step, the obtained SM retrievals from the simulated dataset are used to assess the effects on rainfall estimates obtained via the SM2RAIN [3] algorithm.

The outcome of those simulations is intended to help quantifying the choices of spatial and temporal resolution for the Hydroterra mission concept from a soil properties applications point of view.

 

The work has been supported by the FFG-ASAP project "DWC-Radar" and the ESA project "Hydroterra (former G-CLASS) Phase-0 Science and Requirement".

 

References:

[1] Quast, R.; Albergel, C.; Calvet, J.-C.; Wagner, W. A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations. Remote Sens. 2019, 11, 285.

[2] Masson, V.; Le Moigne, P.; Martin, E.; Faroux, S.; Alias, A.; Alkama, R.; Belamari, S.; Barbu, A.; Boone, A.; Bouyssel, F.; et al. The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev. 2013, 6, 929–960.

[3] Brocca, L., Massari, C., Ciabatta, L., Moramarco, T., Penna, D., Zucco, G., Pianezzola, L., Borga, M., Matgen, P., Martínez-Fernández, J. (2015). Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of SM2RAIN algorithm. Journal of Hydrology and Hydromechanics, 63(3), 201-209, doi:10.1515/johh-2015-0016. .

How to cite: Quast, R., Wagner, W., Calvet, J.-C., Albergel, C., Bertrand, B., Brocca, L., Filippucci, P., and Hobbs, S.: Assessing prospects of sub-daily radar-observations to improve the understanding of soil- and vegetation dynamics., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10102, https://doi.org/10.5194/egusphere-egu2020-10102, 2020

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