Articles | Volume 13, issue 3
Hydrol. Earth Syst. Sci., 13, 343–356, 2009
https://doi.org/10.5194/hess-13-343-2009

Special issue: Remote sensing in hydrological sciences

Hydrol. Earth Syst. Sci., 13, 343–356, 2009
https://doi.org/10.5194/hess-13-343-2009

  13 Mar 2009

13 Mar 2009

Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

F. Mattia1, G. Satalino1, V. R. N. Pauwels2, and A. Loew3 F. Mattia et al.
  • 1Consiglio Nazionale delle Ricerche, Istituto di Studi sui Sistemi Intelligenti per l'Automazione (ISSIA), Bari, Italy
  • 2Ghent University, Laboratory of Hydrology and Water Management (LHWM), Ghent, Belgium
  • 3Max-Planck-Institute for Meteorology, The Land in the Earth System, Hamburg, Germany

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.