On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Abstract

Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

“…In addition, warm day time temperature aided in the drying of the top soil prior to 16 May [14]. A relatively high error in the field measurement of correlation length (CL) was likely the result of its sensitivity to profile length [35]. As outlined by Merzouki et al, (2011), relatively short lengths (1 m) were used.…”

Analysis of soil texture using TERRASAR X-band SAR

“…In general, we observe an over-estimation in the backscattering simulations, with respect to the real radar data. This over-estimation could be explained by various factors, including: the roughness description [10], instrumental errors [9][10], and finally the soil moisture estimation. This backscattering behaviour is confirmed in Fig.…”

“…In order to improve the accuracy of the roughness computations, approximately 10 profiles were recorded for each field. As the surface height profile is considered to be ergodic and stationary, we can compute the correlation function for each profile [10], and derive two statistical parameters: the rms surface height (vertical scale of roughness), and the correlation length (l), which represents the horizontal scale over which similar roughness conditions are detected. The rms height values varied between 0.6cm and 1.5cm, and the correlation length between 3 and 8cm.…”

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

“…The SAR backscatter is controlled by vegetation structure, surface geometry and dielectric properties of the ground targets [44]. The dielectric properties are influenced by soil moisture and vegetation water content [32,44]. Microwave radiation penetrates vegetation canopies interacting with the canopy scatterers, i.e., leaves, twigs, branches, and the trunk [45].…”

“…As such, SAR observations have been used in the past for soil moisture monitoring [32,33], flood mapping [34][35][36] and vegetation mapping [37][38][39][40][41][42][43]. The SAR backscatter is controlled by vegetation structure, surface geometry and dielectric properties of the ground targets [44]. The dielectric properties are influenced by soil moisture and vegetation water content [32,44].…”