Measuring soil moisture with imaging radars

Abstract

An empirical algorithm for the retrieval of soil moisture content and surface Root Mean Square (RMS) height from remote.ly sensed radar data was developed using scatterorneter data. The algorithm is optimized for bare surfaces and requires two co-polarized channels at a frequency between 1.5 G1lz and 11 GHz. It gives best results for kh <2.5, p, < 35% and 0230°O mitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplify the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI > 0.4) cause the algorithm to underestimate soil moisture and overestimate RMS hc.ight. A simple criteria based on the @v/cJ~v ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometm data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1989 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 3.5% soil moisture.

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

“…Theoretical modeling results proved that large aboveground biomass in dense forests can decrease backscatter, due to the saturation of the free-space (or not attenuation) [42]. This decrease in the backscatter can have strong implications for the use of L-band radar data for mapping, such as severe underprediction of soil water content and aboveground biomass [8,42]. This is especially important because the soil water content and dynamics play an important role to connect soil formation factors (vegetation and topography) with soil formation processes and soil attributes.…”

The potential application of fractal approach for surface roughness retrieval: A study for simulated surfaces

“…Again, Zribi and Dechambre (2002) reported that a number of analytical models have been proposed, but the inversion approach to retrieve roughness parameters was not accurately developed. As mentioned in the literature (Oh et al 1992, Dubois et al 1995, Zribi et al 2000, Baghdadi et al 2002, Singh 2005, Zribi et al 2005a, b, Rahman et al 2007) many inversion studies for roughness parameter estimation are based on the relation between the scattering coefficient and roughness parameters. On the other hand, researchers are using alternative approaches such as the fractal and power law technique for describing natural surfaces.…”

“…(Baghdadi et al 2002, Satalino et al 2002, Van der Wal et al 2005. A fundamental element to understand the nature of global change is the ability to model the two-way interaction between land and atmosphere (Dubois et al 1995). Again, surface roughness is a key parameter of radar backscatter models to retrieve soil information from radar images (Singh 2005, Oh and Hong 2007, Rahman et al 2007).…”

A soil moisture algorithm using tilted Bragg approximation

“…The ground azimuth slope causes the polarization rotation as shown in equation (1). Unlike the SPM, the cross polarization component can be obtained using the tilted Bragg approximation as expressed in (2). The range direction slope that causes the incidence angle change was ignored since the surface power spectrum modification is independent of polarization.…”

“…A space-borne mission, like HYDROS, will revolutionize the global soil moisture measurement. For SAR measurements, several successful algorithms have been reported using polarimetric radar data to estimate soil moisture [1,2,3]. These algorithms were developed based on experimental data or numerical scattering data.…”