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

Niko E.C. Verhoest, Martinus Johannes Van Den Berg, Brecht Martens, Hans Lievens, Eric F. Wood, Ming Pan, Yann H. Kerr, Ahmad Al Bitar, Sat K. Tomer, Matthias Drusch, Hilde Vernieuwe, Bernard De Baets, Jeffrey P. Walker, Gift Dumedah, Valentijn R.N. Pauwels

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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 va riable 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.

Original languageEnglish (US)
Article number7004843
Pages (from-to)3507-3521
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number6
DOIs
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Keywords

  • Hydrology
  • microwave radiometry
  • soil moisture

Fingerprint Dive into the research topics of 'Copula-based downscaling of coarse-scale soil moisture observations with implicit bias correction'. Together they form a unique fingerprint.

Cite this