A newsoil roughness parameter for themodelling of radar backscattering over bare soil

International audienceThe characterisation of soil surface roughness is a key requirement for the correct analysis of radar backscattering behaviour. It is noteworthy that an increase in the number of surface roughness parameters in a model also increases the difficulty with which data can be inverted for the purposes of estimating soil parameters. In this paper, a new description of soil surface roughness is proposed for microwave applications. This is based on an original roughness parameter, Zg, which combines the three most commonly used soil parameters: root mean surface height, correlation length, and correlation function shape, into just one parameter. Numerical modelling, based on the moment method and integral equations, is used to evaluate the relevance of this approach. It is applied over a broad dataset of numerically generated surfaces characterised by a large range of surface roughness parameters. A strong correlation is observed between this new parameter and the radar backscattering simulations, for the HH and VV polarisations in the C and X bands. It is proposed to validate this approach using data acquired in the C and X bands, at several agricultural sites in France. It was found that the parameter Zg has a high potential for the analysis of surface roughness using radar measurements. An empirical model is proposed for the simulation of backscattered radar signals over bare soil

Characterization of Martian Surfaces using Mechanical and Spectrophotometric Models

Two recent in situ Mars missions, the Phoenix Mars Lander and the Mars Exploration Rover Opportunity, have explored two quite different locations on the surface of Mars. The Phoenix lander investigated the polygonal terrain and associated soil and icy soil deposits of a high northern latitude site: 68.22° N, 234.25° E). The Opportunity rover, the only currently operational spacecraft on the surface of Mars, is located much closer to the equator: 1.95° S, 354.47° E), and has been exploring the plains and sedimentary rocks in Meridiani Planum. Concurrent with in situ Opportunity and Phoenix observations, the Compact Reconnaissance Imaging Spectrometer for Mars: CRISM) was in orbit around Mars collecting hyperspectral data. In this dissertation, surface and orbital data are used to explore and characterize surface material properties at the Phoenix and Opportunity sites. The Phoenix soil physical properties experiments involved the analysis of forces determined from motor currents from the Robotic Arm: RA)’s trenching activities. Using this information and images of the landing site, soil cohesion and angle of internal friction were determined. Soil dump pile slopes were used to determine the angle of internal friction of loose soil: 38° ± 5°. Additionally, an excavation model that treated walls and edges of the RA’s scoop as retaining walls was used to calculate mean in situ soil cohesion values for several trenches in the Phoenix landing site workspace. These cohesions were found to be consistent with the stability of steep trench slopes. Cohesions varied from 0.20.4−0.2 kPa to 1.21.8−1.2 kPa, with the exception of a subsurface platy horizon unique to a shallow trough for which cohesion will have to be determined using other methods. Soil on a nearby polygon mound had the greatest cohesion: 1.21.8−1.2 kPa). This high cohesion value was most likely due to the presence of adsorbed water or pore ice above the shallow icy soil surface. Further evidence for enhanced soil cohesion above the ice table includes lateral increase in excavation force, by over 30 N, as the RA approached ice. The behavior of soil near the ice table interface is of particular interest considering that many of the high-latitude and mid–latitude regions of Mars are underlain by ice. For the region traversed by Opportunity in the vicinity of Victoria crater, normalized spectral radiances from the Compact Reconnaissance Imaging Spectrometer for Mars: CRISM) were used to retrieve surface scattering properties. Estimates agree with those retrieved in previous photometric studies which used Opportunity–s Panoramic Camera: Pancam) data, and I was able to extend estimates of the Hapke single particle scattering albedo and asymmetry parameter: from the one–term Henyey Greenstein single particle phase function) to a greater spatial and spectral range. Results are useful for determining the boundaries between surface units that otherwise look relatively uniform spectrally. This work also provides photometric functions essential for converting spectra to a single viewing geometry which will yield more accurate spectral comparisons. Results were obtained through simultaneous modeling of surface and atmospheric contributions, iterating through surface scattering parameters until a Levenberg–Marquardt least squares best fit was achieved. Retrieved single scattering albedos range from 0.42 to 0.57: 0.5663 − 2.2715 micrometers), and retrieved asymmetry parameters range from −0.27 to −0.17: moderately backscattering). All surfaces become more backscattering with increasing wavelength. The majority of Victoria crater’s ejecta apron is more backscattering than surrounding regions, indicating a change in physical properties. Images taken when the rover traversed this unit show a cover of basaltic soil with superposed millimeter–scale hematitic spherules, providing agreement with previous analyses of lab experiments showing increased backscattering with the addition of hematitic spherules. Dark wind streaks on the apron appear smooth: low backscatter) because basaltic sands have partly buried spherules, lessening millimeter–scale roughness: in agreement with previous near–surface wind streak analyses). The CRISM–derived scattering parameters also show that bedrock–dominated surfaces are less backscattering than soil–covered surfaces, largely due to lower areal abundance of spherules. The ability to analyze surface unit spherule cover is important because it relates to a wetter period during which spherules formed in Meridiani

Développement et validation de méthodologies pour le suivi des états de surface des sols agricoles nus par télédétection radar (bande X)

Le recours à la caractérisation des états hydrique, géométrique et physique de surface du sol est essentiel dans la gestion et la conservation des ressources naturelles dans les régions agricoles semi-aride. Dans ce contexte, les travaux de cette thèse visent à estimer la variabilité spatio-temporelle des paramètres de surfaces agricoles nues (humidité, rugosité et texture) moyennant des données radars multi-temporelles acquises en bande X à haute résolution spatiale. Une nouvelle description de l'état géométrique des sols est d'abord proposée à travers l'estimation d'un nouveau paramètre de rugosité, le paramètre Zg, estimé en fonction de trois paramètres statistiques de rugosité (écart type des hauteurs "s", longueur de corrélation "l" et la forme de la fonction de corrélation). Les simulations des signaux radar montrent une très forte corrélation avec ce paramètre de rugosité. L'apport du paramètre Zg est confirmé à travers une large base de données expérimentale et spatiale acquises sur différents sites en France. Le deuxième volet de cette thèse présente une analyse des sensibilités des signaux radars issus de capteurs (TerraSAR-X et COSMO-SkyMed), aux paramètres de surface (l'humidité et les trois paramètres de rugosité : s, Zs=s2/l et Zg). Une forte corrélation est observée entre les mesures radars acquises à différentes configurations (polarisations HH et VV, et à 26° et 36°d'incidences) et tous les paramètres du sol. Cette analyse est suivie par des comparaisons des coefficients de rétrodiffusion réels et simulés à partir des modèles physique et semi empirique couramment utilisés : Modèle d'équation intégrale " IEM " de Fung et al., 1992, Modèle de Dubois (Dubois et al., 1995) et le Modèle IEM empiriquement calibré par Baghdadi et al., 2011. Le dernier modèle a montré une forte cohérence avec les mesures radar. Dans le troisième volet, une méthode empirique de détection de changement est développée, en combinant les images radars TerraSAR-X avec des données d'humidités ponctuelles dérivées du réseau des 7 capteurs repartis sur la zone d'étude en continue, pour spatialiser l'état hydrique du sol. La performance de l'algorithme proposé, est évaluée et validée sur de nombreuses parcelles de référence. La spatialisation de la teneur en argile des sols est déduite à partir du calcul de la moyenne des cartes de l'état hydrique du sol (une erreur quadratique moyenne équivalent à 108 g/kg). Pour cartographier la rugosité des sols, des relations empiriques reliant le signal radar aux paramètres de rugosité (Ecart type des hauteurs et le paramètre Zg) étaient élaborées. En inversant les mesures radars, les cartes de rugosité qui en résultent, ont permis de distinguer différents états de surface des sols (labourés, dégradés ou en jachère). Dans le dernier volet, un modèle d'estimation du bilan hydrique des sols agricoles nus " MHYSAN " qui simule l'évaporation et l'état hydrique surfacique est développé. Cette dernière partie souligne le potentiel de calibrer un modèle hydrologique des sols en assimilant les produits d'humidité radars.The characterization of geometric, water and physical surface soil parameters for semi-arid regions is a key requirement for sustainable agricultural management and natural resources conservation. In this context, the current study aims to estimate the spatio-temporal variability of soil properties (soil moisture, roughness and texture) using multi-temporal X-band radar images acquired at high spatial resolution over bare agricultural site in Tunisia. In the first section of this work, a new roughness parameter was proposed; it was the Zg parameter which combines the three most commonly used soil parameters: root mean surface height "s", correlation length "l", and correlation function shape, into just one parameter. A strong correlation was observed between this new parameter and the radar backscattering simulations. The parameter Zg was validated using large database acquired at several agricultural sites in France. Secondly, the sensitivity of X-band TerraSAR-X and COSMO-SkyMed sensors to soil moisture and different roughness parameters (s, Zs=s2/l and Zg parameters) was analyzed. The radar measurements acquired at different configurations (HH and VV polarizations, incidence angles of 26° and 36°) were found to be highly sensitive to the various soil parameters of interest. After that, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. Considerable improvements in the IEM model performance were observed using the Baghdadi-calibrated version of this model. Thirdly, an empirical change detection approach was developed using TerraSAR-X data and ground auxiliary thetaprobe network measurements for the retrieval of surface soil moisture at a high spatial resolution. The accuracy of the soil moisture retrieval algorithm was determined, and validated successfully over numerous test fields. Maps of soil clay percentages at the studied site were derived from the mean of the seven soil moisture radar outputs (a root mean square error equal to 108 g/kg). To retrieve surface soil roughness, empirical expressions were established between backscattering TerraSAR-X coefficients data and the roughness parameters (s and Zg). By inversing radar signals, resulting surface roughness maps have revealed that is possible to use spatial roughness variability observations at plot scale to identify soil surface changes between multi-temporal images. Finally, a Bare Soil HYdrological balance Model "MHYSAN" was developed to estimate surface evaporation fluxes and soil moisture time series over our study site. The present section of this work highlighted the feasibility of calibrating our proposed MHYSAN model through the use of multi-temporal TerraSAR-X moisture products

Modelling of GPR Wave Propagation and Scattering in Inhomogeneous Media

Numerical modelling of GPR wave propagation is becoming more and more important. The ability to build complex models to mirror complex subsurface structures can improve our understanding of how electromagnetic waves are effected by it. The main task of this thesis was to develop a model generator which simplifies complex model building by applying statistical processes. Both, the distribution of inhomogeneities and random rough surfaces can be described statistically. The developed model builder \emph{modelGPR} uses Gaussian distribution to create rough surfaces and to embed inhomogeneities into a host medium. The potentials of the software are presented by two examples. The first is related to ground truth measurements for SAR-satellites in polar regions and the second is devoted to water detection in the Martian shallow subsurface

Second International Colloquium on Mars: Abstracts for a colloquium

Abstracts of 110 papers relating to investigations of the planet Mars and intended for consideration at the colloquium are presented. Entries are arranged alphabetically according to the author's name

Significant achievements in the Planetary Geology Program

Developments reported at a meeting of principal investigators for NASA's planetology geology program are summarized. Topics covered include: constraints on solar system formation; asteriods, comets, and satellites; constraints on planetary interiors; volatiles and regoliths; instrument development techniques; planetary cartography; geological and geochemical constraints on planetary evolution; fluvial processes and channel formation; volcanic processes; Eolian processes; radar studies of planetary surfaces; cratering as a process, landform, and dating method; and the Tharsis region of Mars. Activities at a planetary geology field conference on Eolian processes are reported and techniques recommended for the presentation and analysis of crater size-frequency data are included

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

Design Data Collection with Skylab Microwave Radiometer-Scatterometer S-193, Volume 1

The author has identified the following significant results. Observations with S-193 have provided radar design information for systems to be flown on spacecraft, but only at 13.9 GHz and for land areas over the United States and Brazil plus a few other areas of the world for which this kind of analysis was not made. Observations only extended out to about 50 deg angle of incidence. The value of a sensor with such a gross resolution for most overland resource and status monitoring systems seems marginal, with the possible exception of monitoring soil moisture and major vegetation variations. The complementary nature of the scatterometer and radiometer systems was demonstrated by the correlation analysis. Although radiometers must have spatial resolutions dictated by antenna size, radars can use synthetic aperture techniques to achieve much finer resolutions. Multiplicity of modes in the S-193 sensors complicated both the system development and its employment. An attempt was made in the design of the S-193 to arrange optimum integration times for each angle and type of measurement. This unnecessarily complicated the design of the instrument, since the gains in precision achieved in this way were marginal. Either a software-controllable integration time or a set of only two or three integration times would have been better

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction