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

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.

Download Full-text

On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-3383-2008 ◽

2008 ◽

Vol 5 (6) ◽

pp. 3383-3418

Author(s):

P. Marzahn ◽

R. Ludwig

Keyword(s):

Surface Roughness ◽

Hydrological Modeling ◽

Soil Surface ◽

Test Site ◽

Bare Soil ◽

North East ◽

The North ◽

Soil Surface Roughness ◽

Bulk Parameters ◽

Matching Techniques

Abstract. 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 test-site in the north-east of Germany during the AgriSAR 2006 campaign using fully polarimetric L-Band E-SAR data. In addition to various measurements of soil physical properties, soil surface roughness was measured extensively using photogrammetric image matching techniques for ground truthing. The resulting micro-DEMs are analyzed to correlate soil surface roughness indices to three well established polarimetric roughness estimators. Good results are obtained for Re[ρRRLL] vs. RMS Height, which is thus used to produce multi-temporal roughness maps of the test site. The spatial quality of maps is limited due to the fact that the presence and growth of particular plants is affecting the derivation process significantly. However, roughness derivation for bare soil surfaces is sufficiently accurate to allow for an first order assessment of soil-hydrological parameters (soil porosity, void ratio, micro depression storage capacity), which are crucial for the initialization and operation of hydrological surface models. While uncertainties remain, the dependency of soil bulk parameters from surface roughness can be shown and thus highlights the potential of the retrieval approach for hydrological model applications.

Download Full-text

On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling

Hydrology and Earth System Sciences ◽

10.5194/hess-13-381-2009 ◽

2009 ◽

Vol 13 (3) ◽

pp. 381-394 ◽

Cited By ~ 35

Author(s):

P. Marzahn ◽

R. Ludwig

Keyword(s):

Surface Roughness ◽

Hydrological Modeling ◽

Soil Surface ◽

Test Site ◽

Soil Bulk Density ◽

North East ◽

The North ◽

Soil Surface Roughness ◽

Ground Truthing ◽

Matching Techniques

Abstract. 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 test site in the North East of Germany during the AgriSAR 2006 campaign using fully polarimetric L-band E-SAR data. In addition to various measurements of soil physical properties, soil surface roughness was measured extensively using photogrammetric image matching techniques for ground truthing. The resulting micro-DSMs are analyzed to correlate a soil surface roughness index to three well established polarimetric roughness estimators. Good results are obtained for Re[ρRRLL] vs. RMS Height for areas with a polarimetric alpha angel α<40°, which is thus used to produce multi temporal roughness data of the test site. The proposed roughness inversion scheme showed sufficiently accurate results (RMSE=0.1) to allow for a first order assessment of soil-hydrological parameters (soil porosity, void ratio), which are crucial for the initialization and operation of hydrological surface models. While uncertainties remain, the dependency of soil bulk density parameters from surface roughness can be shown and thus highlights the potential of the retrieval approach for hydrological model applications.

Download Full-text

A comparison of multi-polarization and multi-angular approaches for estimating bare soil surface roughness from spaceborne radar data

Canadian Journal of Remote Sensing ◽

10.5589/m02-060 ◽

2002 ◽

Vol 28 (5) ◽

pp. 641-652 ◽

Cited By ~ 30

Author(s):

Mahmod R Sahebi ◽

Joel Angles ◽

Ferdinand Bonn

Keyword(s):

Surface Roughness ◽

Soil Surface ◽

Bare Soil ◽

Radar Data ◽

Soil Surface Roughness ◽

Spaceborne Radar

Download Full-text

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-207-2009 ◽

2009 ◽

Vol 6 (1) ◽

pp. 207-241 ◽

Cited By ~ 2

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.

Download Full-text