Statistical Characterization of Bare Soil Surface Microrelief

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

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Statistical description of seedbed cloddiness by structuring objects using digital elevation models

International audienceIn this paper the selected approach to analyze seedbed roughness is to study soil surface structural elements, such as aggregates and clods lying on the soil substrate. Recently their identification has been made possible on millimetric resolution digital elevation models (DEMs) by new developed segmentation algorithms relying on contour-based procedure. Here we consider two DEMs of 30 cm and 40 cm by 90 cm recorded on a freshly tilled seedbed of moderate roughness and build up a dataset of several hundreds of clods and large aggregates (sizes greater than 7 mm). We show that these irregular shaped objects can be represented by simple approached forms: an ellipse for the base and a half-cosine function for the height. Values of areal (and volume) overlap rates indicate that half of clods bases are matched with very good rates greater than 0.74 up to 0.89 (respectively 0.70 up to 0.87). The set of detected objects enables to derive the statistical distributions characterizing the ellipse variables (orientation angle, major and minor axis lengths) and the half-cosine amplitude. Because of interdependence of lengths of major and minor axes, we introduce the horizontal compression factor which measures the ellipse flattening. We show plausible independence of the major axis length with the horizontal compression factor and we find that the major axis length minus its minimum is well fitted by the Gamma distribution and the normalized horizontal compression factor by the Beta distribution. We propose to infer the value of the minor axis length from the values of the two preceding variables knowing their statistical occurrences. Same reasoning is handled for inference of the half-cosine amplitude from the major axis length and the normalized vertical compression factor, which is also well fitted by the Beta distribution