O. Taconet

Estimation of watershed soil moisture index from ERS/SAR data.

Abstract The aim of this article is to show that a watershed hydrological index could be derived from ERS/SAR measurements. Indeed, it is well known that, over bare soil, the SAR signal is a function of the geometric and dielectric surface properties. The problem to estimate soil moisture is to free from the effects of the space and time fluctuations of soil roughness and from the vegetation cover attenuation and scattering. The methodology presented here is based on the selection of land cover types or “targets,” for which the SAR signal is mainly sensitive to soil water content variations, and for which the vegetation and the roughness effects (in SAR signal) can be estimated and removed if needed. This method has been validated over an agricultural watershed in France. We show that the accuracy of the retrieved soil moisture is ±0.04–0.05 cm 3 /cm 3 , except during May and June, when vegetation cover is too dense to get reliable soil information.

Backscattering behavior and simulation comparison over bare soils using SIR-C/X-SAR and ERASME 1994 data over Orgeval☆

Abstract During April 1994, the three-frequency radar system flew on the Space Shuttle Endeavour, known as SIR-C/X-SAR mission (Shuttle Imaging Radar C/X-Synthetic Aperture Radar). Over the Orgeval watershed (France), the ground condition stayed very wet throughout the 5-day SAR mission. The SAR imagery allows a data collection over a range of roughness conditions on bare soils. Three classes were identified: very smooth sowings with crusted top layer, cloddy surfaces, and different ploughed fields for future crops. To complement the Shuttle Radar data (three frequencies L, C, X, incidence range from 44° to 57°), the helicopter-borne scatterometer ERASME (C- and X-bands, copolorized configurations) was used. Merging of the two databases was possible. As a result, incidence angles ranging from 25° to 50° are available in C- and X-bands for the copolarized cross sections. Then the major objective of the article is, over this available data collection, to begin the validation of current surface backscattering models to natural surfaces, the theoretical integral equation model (IEM) of Fung et al. (1992) and the empirical model of Oh et al. (1994). It shows adequacies and limits. The IEM model reproduces well radar scatter over smooth surfaces, but fails over rough surfaces, predicting a flatter response with incidence angle than the observed signals in C- and X-bands. Difference in backscatter response due to direction angles (perpendicular and parallel to the row direction) is difficult to reproduce over smooth surfaces by this model integrating anisotropic surface but may be due to the unadequacy of the surface representation. The Oh algorithm agrees well with the backscatter response over rough surfaces at medium incidence angle, but fails with a systematic underestimation over smooth conditions. As a conclusion, further developments are necessary on derivation of theoritical solutions over rough surfaces and on validation of semiempirical algorithms over data sets of various training sources (radar and natural conditions).

Land cover discrimination from multitemporal ERS images and multispectral Landsat images: A study case in an agricultural area in France

More and more remote sensing data corresponding to various wavelength domains is becoming available. Visible/infrared data were first used for land cover classification. However, radar data are becoming more widely used for hydrological and agricultural applications. This paper discusses the performance, for land cover type discrimination, of an optical image acquisition and a multitemporal radar series. For the majority of land cover types existing within the test site (representative of northern European agricultural areas), both ERS multitemporal SAR and Landsat multispectral visible/infrared classifications lead to good results, with the latter being more robust. For better identification of cultures that are less represented, the complementarity of the two datasets may be exploited using an efficient data fusion algorithm based on the Dempster-Shafer evidence theory. The performance of this combination was verified on two successive vegetation cycles.

Characterisation of the Soil Structure and Microwave Backscattering Based on Numerical Three-Dimensional Surface Representation: Analysis with a Fractional Brownian Model

Abstract In this paper, local structure of bare soil is analyzed from the fractal point of view. Soil surface profiles were created from three dimensional (3-D) stereoscopic images of soil surface leading to 3-D numerical reconstruction of the soil topography with very fine resolution. Investigations are done with a database (four soils) that includes three main soil classes according to the way of tillage: smoothed field from rainfalls, ploughed, and sowed fields. The fractional Brownian model developed by Mandelbrot is used to describe local structure of soil roughness. For the database soils, fractal nature of the profiles is demonstrated over a finite range of scales showing a good stability of fractal dimension for each one. This model also provides an excellent analytic fit to the experimental correlation function of soil. Therefore, a new method to calculate its shape at the origin and a more stable correlation length is presented. To study the influence of the band-limited fractal nature of the soil on radar signal, the Moment Method is used to evaluate the backscattered field and to obtain the radar cross-section by statistical averaging. Surfaces used for this electromagnetic simulation are cylindrical and perfectly conducting. A method is developed to generate soil surface profiles that have the same statistical properties and the same roughness parameters values (rms height, correlation length, and fractal dimension) as what has been found on our database soils. The generation method is based on an initially Gaussian correlated random profile, modified by the random midpoint displacement method to introduce the short-range disorder that depends on fractal dimension. The radar signal level computed on these surfaces by the Moment Method shows the dependence of backscattering on fractal dimension and new aspects in electromagnetic scattering behavior over soils.

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

Abstract Estimation of surface soil moisture is one of the major potential applications of radar remote sensing. The European Remote Sensing Satellites (ERS 1 and 2) are equipped with a Synthetic Aperture Radar working at C-Band (5 Ghz) using a rather low incidence angle (23°). For this frequency and angle, the effect of soil roughness and vegetation attenuation are not negligible. Therefore, it is difficult to estimate the surface soil moisture using an algorithm that could be valid for the entire year. In this article 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 (from 1988 to 1994) 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.

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

During 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.

Application of Shannon information theory to a comparison between L- and C-band SIR-C polarimetric data versus incidence angle

Abstract The aim of this article is to present a quantitative measurement of the redundancy, or mutual information, between two different images of the same site. It is based on Shannon and Wiener information theory. In the case of polarimetric synthetic aperture radar images, we propose to compute redundancy either at the radiometric level, according to the error bar on measurement, or at the class level, according to supervised or unsupervised classification results. The advantage of the comparison at the class level is that both Polarimetric information and spatial neighborhood information are considered, and therefore the comparison is performed at a higher level of information. This measurement has been applied to the study of redundancy between L and C bands from spaceborne imaging radar C images of the Orgeval test site, located in the cast of Paris (France), versus incidence angle. It is shown that the redundancy between the two bands increases from about 20% to 30% when incidence angle increases from 44° to 57°. Based on the analysis of classification results in terms of redundancy with ground truth, an interpretation of this result is proposed. Finally, the great complementarity between the two bands is used to improve classification results, by performing data fusion between the two bands.

Effect of row structures on radar microwave measurements over soil surface

The purpose of this paper is to study the effect of row structures on backscattering by soil surface. A new modelling of periodic row structures is proposed. The Moment method is used to evaluate the effect of rows on backscattering. To check theoretical results, a campaign carried out in the north of France in 1994 is used. Series of radar measurements were made by two radars, ERASME and RENE in S band (3.25 GHz), C band (5.6 GHz), and X band (9.25 GHz). It is shown, that the effect of row structure on radar measurements is sensitive to radar frequency and decreases with increasing incidence angle. Finally, the slope of the graph of radar measurements versus incidence angles seems to be a useful tool to retrieve the radar look direction perpendicular or parallel to rows.

Derivation of wild vegetation cover density in semi-arid regions: ERS2/SAR evaluation

In this paper, a simple model is proposed for measuring the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In such regions, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations over Tunisian test areas, where ground parameters are controlled, are performed and compared with actual ERS2 radar measurements. A very good agreement is found. The model is then used to derive a map of the vegetation cover density for the whole studied site (in Tunisia). The approach used here is based upon supervised classification with classes defined by inverting the model and taking into account ERS calibration error. Each of the four classes thus defined exhibits a good classification rate, greater than 85%. Finally, two important applications for natural resources management are presented: vegetation monitoring and soil moisture monitoring.

Study of Backscatter Signature for Seedbed Surface Evolution Under Rainfall - Influence of Radar Precision

We propose a 3D-approach of the soil surface height variations, either for the roughness characterization by the mean of the bidimensional correlation function, or as input of a backscattering model. We consider plots of 50cm by 50cm and two states of roughness of seedbed surfaces: an initial state just after tillage and a second state corresponding to the soil roughness evolution under a rainfall event. We show from stereovision data that the studied surfaces can be modelled as isotropic Gaussian processes. We study the change of roughness parameters between the two states. To discuss the relevance of their difierences, we flnd from Monte-Carlo simulations the bias and variance of estimator for each roughness parameters. We study the roughness and moisture combined in∞uences upon the direct backscattering coe-cients by means of an exact method based on Maxwells equations written in a nonorthogonal coordinate system and by averaging the scattering amplitudes over several realizations. We discuss results taking into account the numerical errors and the precision of radar. We show that the ability of the radar to discriminate the difierent states of seedbed surfaces is clearly linked to its precision.