Estimation of Soil Moisture and Surface Roughness From Single-Polarized Radar Data for Bare Soil Surface and Comparison With Dual- and Quad-Polarization Cases

2014 ◽  
Vol 52 (7) ◽  
pp. 4056-4064 ◽  
Author(s):  
Soon-Koo Kweon ◽  
Yisok Oh
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Soil Surface ◽  
Bare Soil ◽  
Radar Data

scholarly journals An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

2009 ◽  
Vol 6 (1) ◽  
pp. 207-241 ◽  
Author(s):  
M. R. Sahebi ◽  
J. Angles
Keyword(s):  
Surface Roughness ◽  
Dielectric Constant ◽  
Soil Moisture ◽  
Soil Surface ◽  
Bare Soil ◽  
Earth Observation ◽  
Radar Signal ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Soil Surface Roughness

Abstract. The radar signal recorded by earth observation (EO) satellites is known to be sensitive to soil moisture and soil surface roughness, which influence the onset of runoff. This paper focuses on the inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35° and 47° (in mode S3 and S7). This inversion was done based on three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM) and Modified Dubois Model (MDM), which are compared in order to obtain the best configuration. For roughness expressed in rms of heights, mean absolute errors of 1.23 cm, 1.12 cm and 2.08 cm, and for dielectric constant, mean absolute errors of 2.46, 4.95 and 3.31 were obtained for the MDM, GOM and the OM simulation, respectively. This means that the MDM provided the best results with minimum errors. Based on these results, the latter inversion algorithm was applied on the images and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.

scholarly journals An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

2010 ◽  
Vol 14 (11) ◽  
pp. 2355-2366 ◽  
Author(s):  
M. R. Sahebi ◽  
J. Angles
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Soil Surface ◽  
Bare Soil ◽  
Earth Observation ◽  
Radar Signal ◽  
Inversion Method ◽  
Accurate Data ◽  
Inversion Algorithm ◽  
Volumetric Soil Moisture

Abstract. The radar signal recorded by earth observation (EO) satellites is sensitive to soil moisture and surface roughness, which both influence the onset of runoff. This paper focuses on inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35° and 47° (in mode S3 and S7). This inversion was performed with three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM), and Modified Dubois Model (MDM), which were compared to obtain the best configuration. Mean absolute errors of 1.23, 1.12, and 2.08 cm for roughness expressed in rms height and for dielectric constant, mean absolute errors of 2.46 – equal to 3.88 (m3 m−3) in volumetric soil moisture, – 4.95 – equal to 8.72 (m3 m−3) in volumetric soil moisture – and 3.31 – equal to 6.03 (m3 m−3) in volumetric soil moisture – were obtained for the MDM, GOM, and OM simulation, respectively. These results indicate that the MDM provided the most accurate data with minimum errors. Therefore, the latter inversion algorithm was applied to images, and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.

scholarly journals A Simulation Study of GNSS-R Polarimetric Scattering from the Bare Soil Surface Based on the AIEM

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xuerui Wu ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Circular Polarization ◽  
Soil Surface ◽  
Bare Soil ◽  
Satellite System ◽  
Equation Model ◽  
The Other ◽  
Scattering Coefficients ◽  
Remote Sensing Technique ◽  
Transmitted Signal

In the past two decades, global navigation satellite system-reflectometry (GNSS-R) has emerged as a new remote sensing technique for soil moisture monitoring. Some experiments showed that the antenna of V polarization is more favorable to receive the reflected signals, and the interference pattern technique (IPT) was used for soil moisture and retrieval of other geophysical parameters. Meanwhile, the lower satellite elevation angles are most impacted by the multipath. However, electromagnetic theoretical properties are not clear for GNSS-R soil moisture retrieval. In this paper, the advanced integral equation model (AIEM) is employed using the wave-synthesis technique to simulate different polarimetric scatterings in the specular directions. Results show when the incident angles are larger than 70°, scattering at RR polarization (the transmitted signal is right-hand circular polarization (RHCP), while the received one is also RHCP) is larger than that at LR polarization (the transmitted signal is RHCP, while the received one is left-hand circular polarization (LHCP)), while scattering at LR polarization is larger than that at RR polarization for the other incident angles (1°∼70°). There is an apparent dip for VV and VR scatterings due to the Brewster angle, which will result in the notch in the final receiving power, and this phenomenon can be used for soil moisture retrieval or vegetation corrections. The volumetric soil moisture (vms) effects on their scattering are also presented. The larger soil moisture will result in lower scattering at RR polarization, and this is very different from the scattering of the other polarizations. It is interesting to note that the surface correlation function only affects the amplitudes of the scattering coefficients at much less level, but it has no effects on the angular trends of RR and LR polarizations.

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