Analysis of the Dielectric constant of saline-alkali soils and the effect on radar backscattering coefficient: a case study of soda alkaline saline soils in Western Jilin Province using RADARSAT-2 data

Abstract

Soil salinity is a global problem, especially in developing countries, which affects the environment and productivity of agriculture areas. Salt has a significant effect on the complex dielectric constant of wet soil. However, there is no suitable model to describe the variation in the backscattering coefficient due to changes in soil salinity content. The purpose of this paper is to use backscattering models to understand behaviors of the backscattering coefficient in saline soils based on the analysis of its dielectric constant. The effects of moisture and salinity on the dielectric constant by combined Dobson mixing model and seawater dielectric constant model are analyzed, and the backscattering coefficient is then simulated using the AIEM. Simultaneously, laboratory measurements were performed on ground samples. The frequency effect of the laboratory results was not the same as the simulated results. The frequency dependence of the ionic conductivity of an electrolyte solution is influenced by the ion's components. Finally, the simulated backscattering coefficients measured from the dielectric constant with the AIEM were analyzed using the extracted backscattering coefficient from the RADARSAT-2 image. The results show that RADARSAT-2 is potentially able to measure soil salinity; however, the mixed pixel problem needs to be more thoroughly considered.

Figure 1

Location of the study area (the image on the right is the HH polarization of RADARSAT-2).

Figure 2

Measurement of surface roughness.

Figure 3

Effect of salinity and moisture on the sandy loam soil dielectric constant derived from the Dobson model combined with the saltwater model at 5.4 GHz. (a) Salinity effect on the real part, (b) salinity effect on the imaginary part, (c) soil moisture effect on the real part, and (d) soil moisture effect on the imaginary part.

Figure 4

Effect of soil texture on the dielectric constant in nonsalt-affected soil (on the left) [12] and 60‰ in salt-affected soil (on the right). (a) Texture effect on real part (0‰), (b) texture effect on real part (60‰), (c) texture effect on imaginary part (0‰), and (d) texture effect on imaginary part (60‰).

Figure 5

Effect of frequency on the dielectric constant at a salinity of 30‰ (a) and 60‰ (b).

Figure 6

Measurement of the dielectric constant of the L band and C band (6 sample points are taken with different salinity contents as an example).

Figure 7

Frequency variations of the real (a) and imaginary (b) parts of the dielectric constant of the sample derived from laboratory measurements at different moisture content levels.

Figure 8

AIEM simulations of the simulated dielectric constant for the backscattering coefficient at a C band of 40° incidence angle with a variation in soil moisture and soil salinity. (a) HH polarization, (b) VV polarization, and (c) HV polarization.

Figure 9

Relationship between the Fresnel coefficients and the gradient of the Fresnel coefficients with a variation in soil moisture.

Figure 10

Simulated backscattering coefficient compared to the RADARSAT-2 images of the 16 ground samples, with HH polarization on (a) and VV polarization on (b).

Figure 11

The landscape of saline-alkali soil in the research area.

Figure 12

The improved backscattering coefficient from the water-cloud model compared with the simulated backscattering coefficient of the 16 ground samples, with HH polarization on (a) and VV polarization on (b).