Soil surface roughness measurement—methods, applicability, and surface representation

Jester, Werner; Klik, Andreas

doi:10.1016/j.catena.2005.08.005

Cited by 197 publications

(135 citation statements)

References 15 publications

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“…Terrestrial laser scanning has seen rapid developments in the past 10 years [Lichti et al, 2008], with vertical measurement errors in the order of 0.25 mm at small separation distances [Jester and Klik, 2005]. This close-range approach allows aggregate-scale surface roughness measurements at fine sample spacing across relatively small spatial extents [Perez-Gutierrez et al, 2007;Anderson and Croft, 2009].…”

Section: Importance Of Length Scalementioning

confidence: 99%

Modeling fine‐scale soil surface structure using geostatistics

Croft

Anderson

Brazier

et al. 2013

Water Resources Research

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[1] There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm Â 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.Citation: Croft, H., K. Anderson, R. E. Brazier, and N. J. Kuhn (2013), Modeling fine-scale soil surface structure using geostatistic,

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Section: Importance Of Length Scalementioning

confidence: 99%

Modeling fine‐scale soil surface structure using geostatistics

Croft

Anderson

Brazier

et al. 2013

Water Resources Research

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“…Such techniques make it possible to generate digital elevation models that accurately reproduce topographic surfaces (Vericat et al 2014, Pierzchala et al 2014, Nadal-Romero et al 2015. Various methods have been proposed to measure soil surface microtopography (Heng et al 2010), and their relative strengths and weaknesses have been discussed in recent comparative studies (Jester & Klik 2005, Aguilar et al 2009). Although the use of close-range photogrammetry in mapping soil surface structure was demonstrated more than 20 years ago (Warner 1995), the advent of structure-from-motion (SfM) photogrammetry (James & Robson 2012) has generated an improvement in topographic methods, due to its better accessibility to a wider variety of users, low cost, and increased automatization of routines and workflow (Nadal-Romero et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

Cambi¹,

Giannetti²,

Bottalico³

et al. 2018

iForest

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Several studies have been carried out to investigate soil compaction and rutting after logging vehicle traffic, based on time consuming and punctual field measurements. The objective of this study was to measure soil disturbances with two methods: (i) a new, image-based models derived by a structure-frommotion (SfM) photogrammetry approach; and (ii) a traditional soil sampling (bulk density and shear strength). Two trails were selected in a logging area (central Italy), one trafficked by a forwarder (FT) and one trafficked by a skidder (ST). Data collection was conducted before, during and after timber extraction. Image-based models derived by SfM photogrammetry was used to highlight the differences in the shape and distribution of the disturbances along ST and FT. Results showed that the physical parameters of soil significantly changed due to both FT and ST traffic. Machine passes increased bulk density (111% and 31% for FT and ST, respectively), penetration resistance (29% and 24% for FT and ST, respectively) and shear resistance (14% and 6% for FT and ST, respectively), whereas porosity decreased (46% and 9% for FT and ST, respectively). Significant differences between FT and ST were found when comparing ruts removal and bulges with SfM photogrammetry. After logging, FT clearly showed ruts and bulges, whereas in ST ruts and bulges were not visible, but soil displacement in the direction of extraction was evident and measurable. Nevertheless, although our result shows a larger soil disturbance caused by forwarders than skidders, it is not possible to draw any general conclusions about differences between the two machines. Data about the machine passes, or the wood volumes transported over each trial area were not available; therefore, any general conclusion is misleading. SfM photogrammetry give information not available via traditional methods, thus improving impact assessment.

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“…Despite the continuing advances in surface roughness parameterisation from field measurements (e.g. Oh and Kay, 1998;Davidson et al, 2000;Jester and Klik, 2005;Callens et al, 2006) and SAR observations (e.g. Borgeaud and Noll, 1994;Mattia et al, 1997;Srivastava et al, 2008;Marzahn and Ludwig, 2009), the use of these roughness parameters for soil moisture retrieval from SAR often remains unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of fieldmeasured roughness. This paper analyses effective roughness parameters derived from C-and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at Cand L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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Soil surface roughness measurement—methods, applicability, and surface representation

Cited by 197 publications

References 15 publications

Modeling fine‐scale soil surface structure using geostatistics

Modeling fine‐scale soil surface structure using geostatistics

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

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