Verhoest N. E. C., van den Berg M. J., Martens B., Lievens H., Wood E. F., Pan M., Kerr Yann, Al Bitar A., Tomer S. K., Drusch M., Vernieuwe H., De Baets B., Walker J. P., Dumedah G., Pauwels V. R. N. (2015). Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction. Ieee Transactions on Geoscience and Remote Sensing, 53 (6), p. 3507-3521. ISSN 0196-2892.
Titre du document
Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction
Année de publication
2015
Auteurs
Verhoest N. E. C., van den Berg M. J., Martens B., Lievens H., Wood E. F., Pan M., Kerr Yann, Al Bitar A., Tomer S. K., Drusch M., Vernieuwe H., De Baets B., Walker J. P., Dumedah G., Pauwels V. R. N.
Source
Ieee Transactions on Geoscience and Remote Sensing, 2015,
53 (6), p. 3507-3521 ISSN 0196-2892
Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Donnees 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.
Plan de classement
Hydrologie [062]
;
Télédétection [126]
Description Géographique
ETATS UNIS ; MISSISSIPI BASSIN
Identifiant IRD
PAR00012922
