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

Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry

International audienceThe paper deals with the quantitative characterization of small-scale random roughness on agricultural bare soils which controls many of the hydraulic and erosion processes on the field scale. More precisely, our aim is to analyse the adequacy of a stereo photogrammetry system to obtain accurate estimation of this random roughness by means of statistical parameters and to detect soil surface roughness changes due to rainfall.The work presented in this paper is based on a set of digital elevation models (DEMs) of actual agricultural bare soils obtained by stereo photogrammetry. The considered field surfaces correspond to various tillage practices (conventional seedbed, chisel and conventional ploughing) and are watered by simulated rainfalls in order to get various patterns.The stereo photogrammetry process is carefully analysed; the effects of the correlation window size are taken into consideration in order to propose optimized DEM reconstructions.Classical roughness parameters such as root mean square of the heights, correlation length and tortuosity are estimated on the DEMs of the database and results concerning the effect of the DEM size on the obtained accuracy are presented for each roughness parameter. The tortuosity comes out to be a relevant roughness estimator able to quantify the roughness evolution during rain, even with important degradation of the soil.Finally to study the evolution of roughness with rainfall thoroughly, we introduced two positional tortuosity values computed independently over the areas of rigdes and interrows of the DEM. The obtained values clearly show that the rainfalls do not decrease homogeneously the soil small-scale roughness: the interrows areas are much more smoothed by the rain than the ridges areas do.The study presented shows that stereo photogrammetry provide DEMs that enable accurate studies of the geometrical properties of soils that can definitely be of use for hydraulic and erosion studies

Validation of a Rough Surface Model Based on Fractional Brownian Geometry with SIRC and ERASME Radar Data over Orgeval

International audienceDuring the 1994 SIRC/XSAR mission, part of the scientific activities were devoted to hydrology applications. To complement the shuttle radar data, the helicopter scatterometer ERASME was used on the Orgeval site. As a result, incidence angles ranging from 25° to 57° are available in the C band. Terrain measurements of soil moisture and roughness were also taken. The objective of this study was to take advantage of this data set to improve the geometrical characterization of local soil structure using a fractional Brownian model in order to simulate radar backscattering over agricultural fields. The experimental soil profiles are proved to be locally fractal over a spatial range of a few centimeters (clod structure). The relation between the shape of the correlation function and the fractal dimension is brought to light. A new empirical correlation function is used to adjust the experimental one. It leads to an improvement in the analytic backscattering model Integral Equation Model. Soil profiles, generated with combination between fractional Brownian local structure and global structure with respect to the surface root mean square (rms height) and the correlation length, are used in Moment Method backscattering simulations, which provide results in excellent agreement with radar data

Using digital elevation models and image processing to follow clod evolution under rainfall

Soil surface roughness plays an important role in determining how the soil will interact with its environment. Analysis of soil roughness at small scale matters both for preparation of soil in order to allow for plant emergence, and for decisions to favor soil conservation. Indeed, soil roughness may be shaped by tillage operations and then changes with time, under rainfall impact.[br/] Soil surface roughness is usually estimated by various indices, computed on measured profiles or images of elevations. Another approach is focusing on soil cloddiness, either by sieving or by image segmentation. The objective of this study is to monitor the evolution of clods under rainfall with Digital Elevation Model (DEM) recording and image processing.[br/] We prepared two trays of artificial soil surfaces in the laboratory with silt loam soil topped by pre-sieved clods. They were designed to look like a seedbed. Soil surface evolution was achieved by subjecting the trays to controlled artificial rainfalls, and DEM were recorded at each stage. We performed automatic clod segmentation and measurement of the volume of individual clods. Under rainfall impact, we could see smoothing and leveling of clods until disappearance of the smaller ones. We focused on the larger clods greater than 12 mm in diameter that remained till the last rainfall. They showed swelling (volume increase) followed by erosion (volume decrease), these two phenomena being size dependent. Clod volume decrease was modeled by an exponential function.[br/] Now, the slope and the amplitude parameters decreased according to a power law, as a function of mean volume of clods. Monitoring of clod volume with cumulated precipitation with the help of DEM measurements is able to differentiate the dynamics of clod depending on their size. This technique improves the usual roughness description and allows for a better understanding of the processes

Using digital elevation models and image processing to follow clod evolution under rainfall

International audienceStatement of the Problem: Analysis of soil roughness is a key point in soil sciences for soil preparation as well as for impact studies on overland flow and erosion. Soil roughness changes with time, due to the effect of tillage and rainfall. Roughness is usually estimated by various indices, computed on measured profiles or images of elevations. Another approach is focusing on soil cloddiness, either by sieving or by image segmentation. The purpose of this study is to follow the evolution of clods under rainfall with Digital Elevation Model (DEM) recording and image processing. Methodology & Theoretical Orientation: artificial soil surfaces were made in the laboratory with silt loam soil topped by pre-sieved clods. They were then subjected to controlled artificial rainfalls, and DEM were recorded at each stage of the surfaces. We performed automatic clod segmentation and measurement of the volume of individual clod. Findings: The clods of diameter superior than 12 mm show swelling (volume increase) and erosion (volume decrease), with cumulated rainfall. These two phenomena seem to be size dependent. When modeling volume decrease by an exponential function, the slope parameter shows a hyperbolic behavior, as a function of mean volume of the clod subset. Conclusion & Significance: Monitoring of clod volume with cumulated precipitation with the help of DEM measurements is able to differentiate the dynamics of clod depending on their size. This techniques improves the usual roughness description and allows for a better understanding of the processes

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

International audienceMany 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 circularit

Estimation of heat and mass fluxes from IR brightness temperature

International audienceSoil-vegetation-atmosphere transfer models have been developed to simulate mass and energy exchanges between vegetation canopies, the soil, and the atmosphere. They may be used in conjunction with remote sensing data through inversion procedures. In this study, the inversions of two soil-vegetation-atmosphere transfer models are compared on the same data set. Hourly evolutions of stomatal conductance and evapotranspiration are retrieved from the midday measurement of thermal infrared brightness temperature. Seasonal evolution of evapotranspiration and midday stomatal conductance are monitored with a good accuracy with both models. However, the simpler model underestimates evapotranspiration because it does not include a realistic description of hourly evolution of stomatal conductance, and then underestimates morning and afternoon evapotranspiration. The other model gives a better description of hourly evolutions of stomatal conductance and evapotranspiration. This model gives also better estimates of hourly canopy photosynthesis. However, it requires more parameters and computer time than the simpler model, two unfavorable factors for inversion

Taking into account vegetation effects to estimate soil moisture from C-band radar measurements

International audienceEstimation of surface soil moisture is one of the major potential applications of radar remote sensing. The European Remote Sensing Satellites (ERS1 and 2) are equipped with a synthetic aperture radar working at C-band (5 Ghz) using a rather low incidence angle (23°). For this fequency and angle, the effect of soil roughness and vegetation attenuation are not negligible. It is shown that, for wheat canopy, it is possible to apply an empirical relation for correcting for the effect of vegetation. The proposed algorithm is derived from a data set acquired over several years using an airborne radar. It uses a simple cloud model to describe the vegetation attenuation. This algorithm does not need very precise information on vegetation density and yields a final precision for the moisture content on the order of 0.05 cm3/cm3

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

A contour-based approach for clods identification and characterization on a soil surface

International audienceBecause the surface micro-topography has a large impact on soil properties, numerous studies have focused on surface roughness, soil height changes, and soil cloddiness characterization. Usually, feature parameters are estimated from soil measurement samples, based on statistics characterizing the surface as a whole. Now, the shortcoming of such a global approach is that it fails to detect local soil height changes and non-stationarities. The present study introduces a new method to identify and characterize the clods on a seedbed surface Digital Elevation Model (DEM). It is based on an a priori model of the clods, namely objects presenting closed elevation contours with high gradient values. Our clod segmentation method was assessed with the help of a soil scientist on the two kinds of tilled soil surfaces which were considered in this study: an artificial surface made in the laboratory to have a controlled roughness, and an actual seedbed surface made in an experimental field. In both cases results were evaluated in terms of sensitivity and specificity, and showed the performance of the method. We also study the impacts of the main parameters of the method and the computer time. Its main limitations are that it fails to identify the small clods (diameter smaller than 7 mm in this study) and the clods embedded within another piece of relief, such as a greater clod or a hollow border. Then, we show how results can be used to compute clod parameters: mapping of clods provided as output of our algorithm and clod shape measurements. Finally, an application to study soil heights changes with rainfall is provided