Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

Abstract

The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ˚) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ˚) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.

Mineral Soil Texture–Land Cover Dependency on Microwave Dielectric Models in an Arid Environment

“…The average annual precipitation of this site is 140 mm, and the climate is arid and semi-arid. Based on the U.S soil taxonomy, the soils in this site are categorized as belonging to the Aridisols class (from Latin Land 2020, 9, 39 3 of 13 aridus, "dry"), which exhibit at least some subsurface horizon development [17,29,30]. Based on the temperature and humidity regime of the soil classification, the soil of this region is categorized as Aridic.…”

“…Land 2020, 9, x FOR PEER REVIEW 3 of 13 aridus, "dry"), which exhibit at least some subsurface horizon development [17,29,30]. Based on the temperature and humidity regime of the soil classification, the soil of this region is categorized as Aridic.…”

“…The first step in the analysis of the data was to compare the measured values with those calculated on the natural soil basis of the ε′ and ε" outlined in the dielectric constant toolkit ( Figures 5 and 6). The real and imaginary parts of the complex dielectric constant of different soil textures under different land covers with varied moisture content were determined experimentally under laboratory condition using Equation (1) [5,8,11,29,30].…”

“…The particle size of the different soil textures can affect the speed and height of the water movement through capillary action in the soil. In general, the capillary water in loamy soil rises relatively quickly and to a greater height compared to clay and sandy soils [29][30][31]. The five different soil textures from different land cover types were all analyzed, and the representative texture types of this region are sandy and clay as well as silt and loam soils, which were thoroughly analyzed at 0.3-3 GHz (Figure 8).…”

“…where σ • total is the calibrated total backscatter. The sensitivity of radar backscattering to soil moisture and roughness has been demonstrated in different papers [25,27,[29][30][31], considering the variation in dielectric constant of wet (~80) and dry (~6) soils. In this study, we conducted laboratory experiments of the dielectric constant of mineral soils in the 0.3-3 frequency range under natural (i.e., field) conditions as well as a range of different moisture contents.…”