Microwave remote sensing of soil moisture has been an active area of research since the 1970s but has yet found little use in operational applications. Given recent advances in retrieval algorithms and the approval of a dedicated soil moisture satellite, it is time to re-assess the potential of various satellite systems to provide soil moisture information for hydrologie applications in an operational fashion. This paper reviews recent progress made with retrieving surface soil moisture from three types of microvi'ave sensors -radiometers, Synthetic Aperture Radars (SARs), and scatterometers. The discussion focuses on the operational readiness of the different techniques, considering requirements that are typical for hydrological applications. It is concluded that operational coarse-resolution (25-50 km) soil moisture products can be expected within the next few years from radiometer and scafferometer systems, while scientific and technological breakthroughs are still needed for operational soil moisture retrieval at finer scales {< 1 km) from SAR. Also, furiher research on data assimilation methods is needed to make best use of the coarse-resolution surface soil moisture data provided by radiometer and scatterometer systems in a hydroiogic context and to fully assess the value of these data for hydrological predictions.

No abstract

In this paper, we present an intercomparison of algorithms for retrieving soil moisture content (SMC) from ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar images. The algorithms taken into consideration were a feedforward artificial neural network (ANN) with two hidden layers, a statistical approach based on Bayes' theorem, and an iterative algorithm based on the Nelder-Mead direct-search method. The comparison was carried out by using both simulated and experimental data. Simulated data were obtained by means of the Integral Equation Model (IEM). Experimental data were collected in an agricultural area in Northern Italy during 2003-2005; they included backscattering coefficient at HH and HV polarizations and at an incidence angle of θ = 23 • , as well as detailed ground truth measurements of SMC, surface roughness, and vegetation parameters. HH-polarized data were related to SMC, whereas the information of the cross-polarized channel was used to correct the backscatter for the effects of surface roughness. A comparison of the algorithms with experimental data showed that all the tested approaches produced SMC values that are very close to the measured ones. However, the predictions of the ANN were slightly more suitable than the other methods for generating maps in reasonable time. The production of moisture maps carried out at different dates using this algorithm pointed out the feasibility of separating up to six levels of spatial/temporal variations of SMC in the range of 10%-35%. Index Terms-Artificial neural network (ANN), backscattering coefficient, ENVIronmental SATtellite (ENVISAT)/AdvancedSynthetic Aperture Radar (ASAR), inversion algorithms, soil moisture content (SMC) maps.

No abstract

No abstract