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Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction

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Abstract
Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the variable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.
Keywords
Hydrology, soil moisture, microwave radiometry

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Please use this url to cite or link to this publication:

Chicago
Verhoest, Niko, Martinus van den Berg, Brecht Martens, Hans Lievens, Eric F Wood, Ming Pan, Yann H Kerr, et al. 2015. “Copula-based Downscaling of Coarse-scale Soil Moisture Observations with Implicit Bias Correction.” Ieee Transactions on Geoscience and Remote Sensing 53 (6): 3507–3521.
APA
Verhoest, N., van den Berg, M., Martens, B., Lievens, H., Wood, E. F., Pan, M., Kerr, Y. H., et al. (2015). Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 53(6), 3507–3521.
Vancouver
1.
Verhoest N, van den Berg M, Martens B, Lievens H, Wood EF, Pan M, et al. Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2015;53(6):3507–21.
MLA
Verhoest, Niko, Martinus van den Berg, Brecht Martens, et al. “Copula-based Downscaling of Coarse-scale Soil Moisture Observations with Implicit Bias Correction.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 53.6 (2015): 3507–3521. Print.
@article{5861260,
  abstract     = {Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Donn{\'e}es SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the variable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.},
  author       = {Verhoest, Niko and van den Berg, Martinus and Martens, Brecht and Lievens, Hans and Wood, Eric F and Pan, Ming and Kerr, Yann H and Al Bitar, Ahmad and Tomer, Sat K and Drusch, Matthias and Vernieuwe, Hilde and De Baets, Bernard and Walker, Jeffrey P and Dumedah, Gift and Pauwels, Valentijn},
  issn         = {0196-2892},
  journal      = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
  language     = {eng},
  number       = {6},
  pages        = {3507--3521},
  title        = {Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction},
  url          = {http://dx.doi.org/10.1109/TGRS.2014.2378913},
  volume       = {53},
  year         = {2015},
}

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