Characterisation of Soil Profile Roughness

Lu, Zhixiong; Nan, Chia; Perdok, U.D.; Hoogmoed, W.B.

doi:10.1016/j.biosystemseng.2005.04.004

Cited by 44 publications

(33 citation statements)

References 5 publications

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“…This result is aligned with different studies in [6][7][8]16] that found the ACF was well approximated by exponential correlation function. For larger sampling intervals, a mixed behavior is observed, where the exponential type resembled more closely the experimental ACF for lags less than l but the Gaussian type was slightly closer for lags greater than l. Deviation from the exponential ACF especially at higher lags was also found in [20]. Since exponential and Gaussian theoretical descriptions do not always describe the roughness of natural surfaces very well, more complex models for ACF can be found in [31], where two analytical models introducing a varying power for the correlation function are suggested to fit the experimental ACF.…”

Section: Discussionmentioning

confidence: 67%

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Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

et al. 2016

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Abstract:The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values.Large-and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C-or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

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Section: Discussionmentioning

confidence: 67%

“…More advanced profiling methods use non-contact, laser techniques such as triangulation [19,20] or time-of-flight distance meters [7]. In the triangulation laser techniques, each laser-illuminated soil feature is recorded onto a picture by a camera and then transformed to a reference frame using a dedicated calibration procedure.…”

Section: Introductionmentioning

confidence: 99%

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

et al. 2016

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“…The orientation of mechanical tillage, which can be related to roughness measurements, has an influence on backscattering signals (Remond et al, 1999;Smyth et al, 2000;Zhixiong et al, 2005). However, the backscatter models do not enable the simulation of this influence directly.…”

Section: Resultsmentioning

confidence: 99%

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

Sahebi

Angles

2010

Hydrol. Earth Syst. Sci.

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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 (m 3 m −3 ) in volumetric soil moisture, -4.95 -equal to 8.72 (m 3 m −3 ) in volumetric soil moisture -and 3.31 -equal to 6.03 (m 3 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.

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“…Direct ground field measurement of surface roughness can be performed by either contact techniques [44][45][46] or non-contact techniques such as close range photogrammetry [15,26,[47][48][49][50] or laser scanning [32,[51][52][53]. Some authors made comparative studies [30,[54][55][56] and a review of the measurement techniques can be found in [3].…”

Section: Introductionmentioning

confidence: 99%

Statistical Characterization of Bare Soil Surface Microrelief

Vannier¹,

Taconet²,

Dusséaux³

et al. 2014

Advanced Geoscience Remote Sensing

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Because the soil surface occurs at the boundary between the atmosphere and the pedosphere, it plays an important role for geomorphologic processes. Roughness of soil surface is a key parameter to understand soil properties and physical processes related to substrate movement, water infiltration or runoff, and soil erosion. It has been noted by many authors that most of the soil surface and water interaction processes have characteristic lengths in millimeter scales. Soil irregularities at small scale, such as aggregates, clods and interrill depressions, influence water outflow and infiltration rate. They undergo rapid changes caused by farming imple‐ ments, followed by a slow evolution due to rainfall events. Another objective of soil surface roughness study is investigating the effects of different tillage implements on soil physical properties (friability, compaction, fragmentation and water content) to obtain an optimal crop emergence. Seedbed preparation focuses on the creation of fine aggregates and the size distribution of aggregates and clods produced by tillage operations is frequently measured. Active microwave remote sensing allows potential monitoring of soil surface roughness or moisture retrieving at field scale using space-based Synthetic Aperture Radars (SAR) with high spatial resolution (metric or decametric). The scattering of microwaves depends on several surface characteristics as well as on imagery configuration. The SAR signal is very sensitive to soil surface irregularities and structures (clod arrangement, furrows) and moisture content in the first few centimeters of soil (depending on the radar wavelength). In order to link the remote sensing observations to scattering physical models as well as for modelling purpose, key features of the soil microtopography should be characterized. However, this characteri‐ zation is not fully understood and some dispersion of roughness parameters can be observed in the same field according to the methodology used. It seems also, that when describing surface roughness as a whole, some information related to structured elements of the micro‐ topography is lost

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Characterisation of Soil Profile Roughness

Cited by 44 publications

References 5 publications

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

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

Statistical Characterization of Bare Soil Surface Microrelief

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