Publication Abstracts

Ebtehaj et al. 2022

Ebtehaj, A., M. Durand, and M. Tedesco, 2022: Constrained inversion of a microwave snowpack emission model using dictionary matching: Applications for GPM satellite. IEEE Trans. Geosci. Remote Sens., 60, 4302114, doi:10.1109/TGRS.2021.3115663.

This article presents a new algorithmic framework for multilayer inversion of the dense media radiative transfer (DMRT) equations of snowpack emission, with particular emphasis on the role of high-frequency microwave channels above 60 GHz. The approach relies on dictionary matching and locally constrained least squares. The results demonstrate that the algorithm can invert the DMRT model and retrieve depth, density, and grain size of a single-layer snowpack when dependencies of density and grain size on depth are properly accounted for. However, as the number of layers increases, the sensitivity of the inversion to observation noise grows markedly. Using observations, over the Great Plains in the United States, from the microwave imager onboard the global precipitation measurement (GPM, 10-166 GHz) core satellite, the initial results demonstrate that under a clear-sky condition and no vegetation canopy, the algorithm is capable to retrieve the snow depth and water equivalent of seasonal snow with a mean absolute error (MAE) of less than 0.15 m–when compared to the high-resolution analysis data from the SNOw Data Assimilation System (SNODAS).

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BibTeX Citation

@article{eb03000d,
  author={Ebtehaj, A. and Durand, M. and Tedesco, M.},
  title={Constrained inversion of a microwave snowpack emission model using dictionary matching: Applications for GPM satellite},
  year={2022},
  journal={IEEE Trans. Geosci. Remote Sens.},
  volume={60},
  pages={4302114},
  doi={10.1109/TGRS.2021.3115663},
}

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RIS Citation

TY  - JOUR
ID  - eb03000d
AU  - Ebtehaj, A.
AU  - Durand, M.
AU  - Tedesco, M.
PY  - 2022
TI  - Constrained inversion of a microwave snowpack emission model using dictionary matching: Applications for GPM satellite
JA  - IEEE Trans. Geosci. Remote Sens.
VL  - 60
SP  - 4302114
DO  - 10.1109/TGRS.2021.3115663
ER  -

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