Retrieving Soil Moisture Over Bare Soil from ERS 1 Synthetic Aperture Radar Data: Sensitivity Analysis Based on a Theoretical Surface Scattering Model and Field Data

Abstract

In order to assess the retrieval of soil moisture from ERS 1 (European Remote Sensing Satellite) synthetic aperture radar (SAR) data, an inversion procedure based on the integral equation model (IEM) [Fung et al., 1992] is developed. First, the IEM is used to analyze the sensitivity of radar echoes (in terms of the backscattering coefficient tr ø) to the surface parameters (roughness and dielectric constant) under ERS 1 SAR configuration. Results obtained for random rough bare soil fields show that the effect of surface roughness is very strong, particularly in the case of smooth surfaces, and that the sensitivity of tr ø to dielectric constant is independent of the radar configuration and the roughness conditions. This means that the range of variation of backscattering with respect to the dielectric constant variation of dry to wet soil remains the same (about 5 dB) for any roughness condition and radar configuration. The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected in a test site situated near Naples, Italy, during the Sele Synthetic Aperture Radar experiment (SESAR) campaign (November 1993). Simultaneous with ERS 1 overpasses, dielectric constant and roughness measurements were taken over two flat bare fields. From this analysis it is found that the inversion of backscattering from ERS 1 SAR into soil moisture is not reliable without accurate information on roughness if the surface is smooth. In this case it is observed that the sensitivity to the roughness parameters is much higher than the sensitivity to dielectric constant, so that even a small error in the measurement of this parameter can affect the retrieved value of soil moisture significantly. The inversion procedure provides more reliable soil moisture estimates when surfaces rougher than those analyzed in the field experiment are considered.

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

“…Whereas the complete version describes the backscattering process from a bare soil surface without any limitation on frequency and roughness, the approximate solutions are only valid for low to medium frequencies and surfaces with low roughness. Many approximate solutions (and improvements) to the original version of the IEM have been developed [44,57,[68][69][70][71] and used in numerous studies with varying results [72,73]. Baghdadi et al [74] proposed a semi-empirical calibration of the IEM by replacing the required correlation length (l) with a calibration parameter derived from SAR data and field measurements and found the calibrated IEM to agree well with SAR backscatter measurements, as did Sahebi et al [75].…”

“…Since the IEM is valid only for single scattering terms attributable to surface scattering, the model is generally only used to invert soil moisture from bare soil surfaces [44,65] where second order scattering is not considered, although in a later effort, Fung et al [66] improved the model to take into account multiple scattering terms. However, due to the complexity of the model, the original version is rarely used and often replaced by approximate solutions [67].…”

“…Altese et al [43] studied the sensitivity of the radar signal to various land surface parameters over a short grass canopy and found that the most influencing parameter appeared to be the surface roughness. The effect of surface roughness can often be equal to or greater than the effects of soil moisture content on the backscatter [44,45] and therefore determining these parameters and separating them from their contribution to the total backscatter is perhaps one of the most challenging aspects for active microwave soil moisture estimation.…”

Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy

“…This is an important ecohydrological issue [Rodriguez-Iturbe, 2000;Albertson and Kiely, 2001;Williams and Albertson, 2004;Montaldo et al, 2005] for both prognostic models, in which predictions of q and land surface fluxes (e.g., ET) are required for a projected radiative and precipitation forcing time series, and diagnostic models, in which land surface fluxes are estimated for a set of observed atmospheric and surface states (q and surface temperature, T s ) using satellite remote sensing observations Kustas et al, 2002;Caparrini et al, 2004;Reichle et al, 2004]. Regarding the diagnostic perspective, the mapping of surface q at high spatial resolutions may be derived from active microwave sensor (radar) observations (up to 10 m of spatial resolution); however the uncertainties on the effectiveness of the radar signal remain large, especially in heterogeneous terrain [e.g., Altese et al, 1996;Mancini et al, 1999;Holah et al, 2005]. While more accurate q estimates may be provided by passive microwave remote sensor observations, they are at extremely coarse spatial resolutions (25 -50 km) [Jackson, 1997a;Entekhabi et al, 2004], arguably unsuited to heterogeneous Mediterranean ecosystems.…”

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

“…Advantages of data assimilation as a soil moisture estimation technique include the ability to: (1) use observations and models of different spatial resolutions [5], (2) ingest observations of geophysical variables that are indirectly related to soil moisture [6][7][8], and (3) constrain model soil moisture predictions to observations that are intermittently available [9]. Ensemble-based techniques, such as the ensemble Kalman Filter (EnKF) [10,11] and Markov Chain Monte Carlo [12] methods, are a particular class of data assimilation methods that support the use of Monte Carlo simulation to resolve the spatiotemporal distribution of soil moisture in a probabilistic way.…”