Research project SR/00/159 (Research action SR)
Context and objectives
Knowledge of the spatial distribution and dynamics of soil moisture is essential for optimal and sustainable management of soil and water resources as it governs all important key processes of the hydrological cycle such as infiltration, runoff, root water uptake, evaporation, as well as energy exchanges with the atmosphere. Synthetic aperture radar (SAR) remote sensing in particular provides soil moisture information from the field to the catchment and larger scales. Yet, soil moisture products derived from remote sensing still suffer from large uncertainties due to the relatively poor relevance of traditional soil sampling for calibrating and validating SAR processing algorithms. In that respect, the latest ground-penetrating radar (GPR) developments (UCL) permit now to accurately and rapidly map soil moisture at the field scale and thereby circumvent major limitations related to the inherent field-scale variability. In that context, the general objective of the SENSAR project is to improve remote sensing of soil moisture by SAR using advanced GPR technology for providing relevant ground-truthing to properly calibrate and validate SAR inversion algorithms.
Methodology
The SENSAR methodology is organized around five specific objectives. (1) Time-lapse acquisitions of GPR data over bare and vegetated agricultural fields in Belgium will be performed to provide high-resolution maps of soil permittivity and correlated moisture. (2) A series of SAR images will be simultaneously acquired over the same areas, using in particular Radarsat-2. (3) GPR full-wave inversion algorithms will be improved to maximize information retrieval in terms of surface soil moisture, depth-dependent soil moisture, surface roughness and vegetation cover. (4) SAR data–based soil moisture extraction algorithms, using a combination of physical and semi-empirical (effective) approaches for calculating the backscattering coefficient, will be improved by using GPR-derived dielectric permittivity as ground-truth. (5) SAR-derived surface soil moisture algorithms will be validated and uncertainty will be quantified.
Results expected
Expected SENSAR major outcomes are: (1) improved SAR processing algorithms and calibration protocols for soil moisture products, (2) improved quantification of uncertainty in SAR-derived soil moisture estimates, (3) improved understanding of scaling issues to which remote sensing typically face and SAR downscaling methods, which is important given the inherent variability of soil moisture at the field scale, (4) adapted GPR processing algorithms to tackle roughness, vegetation and vertical variability issues. The major remote sensing innovation of the project situates in the use of high-resolution soil moisture maps, provided by GPR, to estimate soil moisture from SAR. Such high-resolution and accurate information was not available before, which has strongly limited advances in SAR processing for soilmoisture, and hence, underlying applications.
