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

2002 ◽  
Vol 28 (5) ◽  
pp. 641-652 ◽  
Author(s):  
Mahmod R Sahebi ◽  
Joel Angles ◽  
Ferdinand Bonn
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Bare Soil ◽  
Radar Data ◽  
Soil Surface Roughness ◽  
Spaceborne Radar

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 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.

Retrieving parameters of bare soil surface roughness and soil water content under arid environment from ERS-1, -2 SAR data

2013 ◽  
Vol 34 (17) ◽  
pp. 6202-6215 ◽  
Author(s):  
Arthur Genis ◽  
Leonid Vulfson ◽  
Dan G. Blumberg ◽  
Michael Sprinstin ◽  
Alexey Kotlyar ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Surface ◽  
Bare Soil ◽  
Arid Environment ◽  
Soil Surface Roughness ◽  
Sar Data

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.

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

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.

scholarly journals Fast computation of bare soil surface roughness on a Fermi GPU

2015 ◽  
Vol 82 ◽  
pp. 38-44 ◽  
Author(s):  
Xiaojie Li ◽  
Changhe Song ◽  
Sebastian López ◽  
Yunsong Li ◽  
José F. López
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Bare Soil ◽  
Fast Computation ◽  
Soil Surface Roughness

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

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