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 USING MULTI-DIMENSIONAL MICROWAVE REMOTE SENSING INFORMATION FOR THE RETRIEVAL OF SOIL SURFACE ROUGHNESS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1257-1262 ◽  
Author(s):  
P. Marzahn ◽  
R. Ludwig
Keyword(s):  
Surface Roughness ◽  
Hydrological Modeling ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Ground Truth ◽  
Bare Soil ◽  
North East ◽  
The North ◽  
Soil Surface Roughness ◽  
Matching Techniques

In this Paper the potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the Demmin testsite in the North-East Germany during AgriSAR 2006 campaign using fully polarimetric L-Band airborne SAR data. For ground truthing extensive soil surface roughness in addition to various other soil physical properties measurements were carried out using photogrammetric image matching techniques. The correlation between ground truth roughness indices and three well established polarimetric roughness estimators showed only good results for Re[ρRRLL] and the RMS Height s. Results in form of multitemporal roughness maps showed only satisfying results due to the fact that the presence and development of particular plants affected the derivation. However roughness derivation for bare soil surfaces showed promising results.

Accounting for soil surface roughness in the inversion of ultrawideband off-ground GPR signal for soil moisture retrieval

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. H1-H7 ◽  
Author(s):  
François Jonard ◽  
Lutz Weihermüller ◽  
Harry Vereecken ◽  
Sébastien Lambot
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Surface ◽  
Waveform Analysis ◽  
Surface Reflection ◽  
Full Waveform ◽  
Roughness Model ◽  
Soil Surface Roughness ◽  
Roughness Amplitude

We combined a full-waveform ground-penetrating radar (GPR) model with a roughness model to retrieve surface soil moisture through signal inversion. The proposed approach was validated under laboratory conditions with measurements performed above a sand layer subjected to seven different water contents and four different surface roughness conditions. The radar measurements were performed in the frequency domain in the range of 1–3 GHz and the roughness amplitude standard deviation was varied from 0 to 1 cm. Two inversion strategies were investigated: (1) Full-waveform inversion using the correct model configuration, and (2) inversion focused on the surface reflection only. The roughness model provided a good description of the frequency-dependent roughness effect. For the full-waveform analysis, accounting for roughness permitted us to simultaneously retrieve water content and roughness amplitude. However, in this approach, information on soil layering was assumed to be known. For the surface reflection analysis, which is applicable under field conditions, accounting for roughness only enabled water content to be reconstructed, but with a root mean square error (RMS) in terms of water content of [Formula: see text] compared to an RMS of [Formula: see text] for an analysis where roughness is neglected. However, this inversion strategy required a priori information on soil surface roughness, estimated, e.g., from laser profiler measurements.

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