D. Vidal-Madjar

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.

Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region

Abstract Hydrological models are generally unable to simulate correctly water exchanges at the soil-atmosphere interface and the time evolution of surface soil humidity. This drawback often leads to poor simulation of water flows after periods of low flows because of the wrong estimation of the surface soil water content. In this paper, we describe how remote sensing can be used to account for the vegetation in the estimation of the actual evapotranspiration, and to estimate soil moisture regularly throughout the year and use it to correct the model simulation. This work has been done on the Adour river basin, in the framework of the HAPEX-MOBILHY experiment. The results presented show the improvements that result from the use of remote sensing data in hydrological modelling: better simulation of the soil moisture and of water flows at the outlets, and more realistic calculation of evaporation.

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).

INTRODUCTION OF NEIGHBORHOOD INFORMATION IN EVIDENCE THEORY AND APPLICATION TO DATA FUSION OF RADAR AND OPTICAL IMAGES WITH PARTIAL CLOUD COVER

Abstract Two ways of introducing spatial information in Dempster–Shafer evidence theory are examined: in the definition of the monosource mass functions, and, during data fusion. In the latter case, a “neighborhood” mass function is derived from the label image and combined with the “radiometric” masses, according to the Dempster orthogonal sum. The main advantage of such a combination law is to adapt the importance of neighborhood information to the level of radiometric missing information. The importance of introducing neighborhood information has been illustrated through the following application: forest area detection using radar and optical images showing a partial cloud cover.

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.

Surface and aerodynamic roughness in arid and semiarid areas and their relation to radar backscatter coefficient

Surface roughness is a key parameter for surface-atmosphere exchanges of mass andenergy. Only a few field measurements have been performed in arid or semiarid areaswhere it is an important control of the aeolian erosion threshold. An intensive fieldcampaign was performed in southern Tunisia to measure the lateral cover, Lc, and theaerodynamic roughness length, Z0, over 10 sites with different surface roughnesses. Lcwas determined by combining field measurements of the geometry of the roughnesselements and simple assumptions on their shapes. Z0was experimentally determined fromhigh-precision wind velocity and air temperature profiles. The resulting data were found tobe in good agreement with the existing relationships linking the geometric and theaerodynamic roughness. This suggests that for natural surfaces, Z0can be estimated onthe basis of the geometric characteristics of the roughness elements. This data set wasthen used to investigate the capabilities of radar backscatter coefficients, s0, to retrieve Lcand/or Z0. Significant relationships were found between s0and both Lcand Z0. TheSAR/ERS data set is in agreement with the SIR-C SLR data set from Greeley et al. (1997).On the basis of these two data sets including data from different arid and semiarid areas(North Africa, South Africa, North America), we propose an empirical relationship toretrieve Z0using radar observations in the C band from operational sensors.

Evaluation of the ERS 1/Synthetic Aperture Radar Capacity to Estimate Surface Soil Moisture: Two-Year Results Over the Naizin Watershed

One of the possible applications of satellite radar remote sensing is to estimate surface soil moisture. To evaluate the capacity of ERS 1/synthetic aperture radar (SAR), a European Space Agency (ESA) pilot project has been set up. The test site is a small agricultural watershed situated in the central part of French Brittany. During 1992 and 1993, almost all possible SAR images were acquired together with two types of ground truths: intensive ground measurements during 14 field campaigns and point automatic measurements over the entire period. From the comparison of those ground truth data with the ERS 1 images, the following results are obtained. On a field scale the relation between the radar signal and the surface soil moisture depends strongly on the type of culture: Correlation is poor for the different cultures except for wheat. On a basin scale, it is shown that during the period of low vegetation density, there is a linear correlation between the mean radar data and the point automatic measurements. This last result is very encouraging and could open the way to hydrological applications.

Estimation of soil and crop parameters for wheat from airborne radar backscattering data in C and X bands

Abstract The study of radar backscattering signatures of wheat fields was investigated, using data collected on the Orgeval agricultural watershed (France) by the airborne scatterometer ERASME in C and X bands, HH and VV polarizations, at incidence angles from 15° to 45°, during two years for different soil moisture conditions with simultaneous ground-based measurements. A simple parameterization as water-cloud model with two driving parameters (the surface soil moisture and the plant water content) gives satisfactory results to estimate radar cross sections of wheat for a wide range of frequencies (C and X bands) and incidence angles (20° and 40°) within 1 dB in CHH and XHH and 2 dB in CVV and XVV. At the lower frequency (C band) the attenuated soil backscattering by the vegetation is dominant. It is shown that simple linear relations in C band between radar cross section and soil moisture are insufficient. A correction term for the vegetation attenuation is needed and is determined. Low contrast between the backscattering of dry and wet soil (around 6 dB) for a given vegetation density leads to a relatively high error in the estimation of soil moisture by radar (0.06 cm 3 / cm 3 ). At the higher frequency (X band), the radar backscattering is negatively correlated to the vegetation water content with a saturation of the radar cross section as the plant grows (about 6 dB of dynamic range between low and fully grown canopy) with no dependence on the soil signal. The achievable accuracy in the estimation of crop water content is the same at 20° and 40° and higher in XHH (about 0.5 kg/m 2 ) than in XVV.

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.

A robust method for picture segmentation based on split-and-merge procedure

The aim of picture segmentation is the extraction of pertinent and stable areas. Pertinence is the agreement of the detected areas with a physical or semantical property of the object; stability is the robustness of the detection to slight transformations such as geometric or photometric distortions. In aerial picture segmentation, the pertinence of an area is often reduced to radiometric homogeneity and spatial connectivity. Unfortunately stability is seldom checked and the deduced segmentation is very sensitive to many parameters introduced by the programmer and thus it is not very reliable. We propose a solution to the stability problem. It will be presented in a theoretical way and then an example of an application is proposed. This method makes use of the well-known split-and-merge algorithm and we will first recall its principle and its main properties.