Soil surface roughness modeling: limit of global characterization in remote sensing

Olivier Chimi Chiadjeu 1 Richard Dusséaux 1 Odile Taconet 1
1 ESTER - LATMOS
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
Abstract : Many scientists use a global characterization of bare soil surface random roughness. Surface roughness is often characterized by statistical parameters deduced from its autocorrelation function. Assuming an autocorrelation model and a Gaussian height distribution, some authors have developed algorithms for numerical generation of soil surfaces that have the same statistical properties. This approach is widespread and does not take into account morphological aspects of the soil surface micro-topography. Now a detail surface roughness analysis reveals that the micro-topography is structured by holes, aggregates and clods. In the present study, we clearly show that when describing surface roughness as a whole, some information related to morphological aspects is lost. Two Digital Elevation Model (DEM) of a same natural seedbed surface were recorded by stereo photogrammetry. After estimating global parameters of these natural surfaces, we generated numerical surfaces of the same average characteristics by linear filtering. Big aggregates and clods were then captured by a contour-based approach. We show that the two-dimensional autocorrelation functions of generated surfaces and of the two agricultural surfaces are close together. Nevertheless, the number and shape of segmented object contours change from generated surfaces to the natural surfaces. Generated surfaces show fewer and bigger segmented objects than in the natural case. Moreover, the shape of some segmented objects is unrealistic in comparison to real clods, which have to be convex and of low circularity
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Submitted on : Thursday, December 26, 2013 - 8:31:47 AM
Last modification on : Wednesday, May 15, 2019 - 3:42:01 AM

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Olivier Chimi Chiadjeu, Richard Dusséaux, Odile Taconet. Soil surface roughness modeling: limit of global characterization in remote sensing. Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, Sep 2013, Dresden, Germany. pp.88920B, ⟨10.1117/12.2028861⟩. ⟨hal-00922347⟩

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