Comparison between backscattered TerraSAR signals and simulations from the radar backscattering models IEM, Oh, and Dubois

The objective of this paper is to evaluate on bare soils the surface backscattering models IEM, Oh, and Dubois in X-band. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture and surface roughness). Oh's model correctly simulates the radar signal for HH and VV polarizations whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR data and model. The backscattering Integral Equation Model (IEM) model simulates correctly the backscattering coefficient only for rms1.5 cm in using Gaussian function. However, the results are not satisfactory for a use of IEM in the inversion of TerraSAR data. A semi-empirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR data and the simulations using the calibrated version of the IEM

Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust

Le comportement du signal radar TerraSAR-X en fonction des paramètres du sol (rugosité, humidité, structure) a été analysé sur des données 2009 et 2010. Les résultats montrent que la sensibilité du signal radar à l'humidité est plus importante pour des faibles incidences (25° en comparaison à 50°). Pour des fortes valeurs d'humidité, le signal TerraSAR-X est plus sensible à la rugosité du sol à forte incidence (50°). La forte résolution spatiale des données TerraSAR-X (1 m) permet de détecter la croûte de battance à l'échelle intra parcellaire. / Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5 GHz) data acquired in 2009 and 2010. We analyzed the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (N25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°). The high spatial resolution of the TerraSAR-X data (1 m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust

Kalideos OSR MiPy : un observatoire pour la recherche et la démonstration des applications de la télédétection à la gestion des territoires

International audienceCes dernières années, le CESBIO a mis en place un Observatoire Spatial Régional 'OSR' : un dispositif d'observation couplant mesures de terrain et télédétection dans le sud-ouest de la France. L'OSR se base sur des acquisitions mensuelles de données satellitaires à résolution décamétrique depuis 2002 et sur des sites expérimentaux lourdement instrumentés (mesures en continu de flux d'eau et de carbone) à partir de 2004. Ce dispositif a été reconnu service d'observation par l'INSU/CNRS en 2007 et site KALIDEOS par le CNES fin 2009 : 'KALIDEOS OSR MiPy'. Le site atelier correspond à une emprise d'image SPOT, soit environ 50x50 km et couvre une grande diversité de milieux (pédologie, topographie), d'occupation et d'utilisation des sols, de pratiques et de modalités de gestion (agricole, forestière...) et de conditions climatiques (fort gradient de déficits hydriques estivaux). Pour la télédétection, ce site a servi la préparation de SMOS, et il soutient maintenant en priorité à la préparation des missions VENμS et Sentinel-2. Les aspects radar, imagerie thermique et les approches multi-capteurs se développent depuis peu. Le traitement du signal, la physique de la mesure et l'amélioration de la qualité des données constituent le premier axe de recherche. Au niveau thématique, le CESBIO a pour priorité les suivis et les modélisations des agrosystèmes de grandes cultures. L'implication récente d'autres partenaires scientifiques ou gestionnaires a permis d'initier des travaux sur d'autres aspects, comme la biodiversité, l'aménagement du territoire, le suivi de l'extension urbaine, les risques environnementaux, la santé des forêts, l'enfrichement, la diversité et la productivité des prairies. La valorisation des 10 années d'archives 2002-2011 débute et semble très pertinente pour la caractérisation en haute et en basse résolution des conséquences d'années climatiques atypiques (2003, 2011) sur les éco-agro-systèmes. L'extrapolation des résultats obtenus sur ce site atelier à toute la région Midi-Pyrénées ou à la chaine des Pyrénées est aussi initiée

Effet des ultrasons basse fréquence sur l’hydrodynamique d’un réacteur annulaire continu : approche expérimentale en Distribution des Temps de Séjour (DTS)

Ultrasound (US) are particularly interesting for their mechanical effects enabling transfers activation, in particular by generating mixing. However, this phenomenon has not yet been quantified in a continuous reactor, which is nevertheless a key point for the intensification of such processes. For this purpose, this work characterized the hydrodynamics within a continuous annular reactor under low frequency ultrasound via a Residence Time Distribution experimental approach (RTD). Reliable and reproducible experimental protocol and data processing method were developed. The experiments under silent conditions showed that,due to its geometry, the studied reactor had dead zones that are not negligible. The comparison of these results with those obtained under US had clearly demonstrated the action of US in the flow rate range investigated (laminar flow). The comparison of the RTD curves, as well as the average residence time values obtained, confirmed the US effect on the mixing within the reactor. By creating micro-mixing, ultrasound also reduced dead zones. The study of US power influence showed a threshold beyond which its contribution on hydrodynamics is less marked. This point is encouraging for the scale up of reactors under ultrasound

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring

Transitions sol-gel de colloïdes anisotropes sous champs de cisaillement, pression et ondes ultrasonores, caractérisées par diffusion de rayons x aux petits angles in-situ

L'objectif de ce travail est de caractériser aux échelles mésoscopiques, l'effet combiné des champs de pression, hydrodynamiques et ultrasonores sur les mécanismes de transition sol-gel de colloïdes anisotropes d'argiles lors de l'ultrafiltration tangentielle. Pour cela, des cellules de filtration ont été développées en intégrant une lame vibrante sollicitée à 20kHz par un générateur ultrasonore. Ces cellules de filtration permettent l'observation in-situ aux échelles nanométriques par diffusion de rayons X aux petits angles (SAXS). Différentes suspensions aqueuses d'argiles ont été étudiées : des argiles naturelles de montmorillonite Wyoming-Na et des argiles synthétiques de Laponite en présence ou non d'un peptisant le tetrasodium diphosphate (Na4P2O7). Par ailleurs l'effet des ultrasons sur le comportement rhéologique de suspensions a aussi été étudié.  L'effet du pré-cisaillement induit par la pompe du circuit de filtration et l'effet des ultrasons, sur les contraintes de cisaillement des suspensions de Laponite ont été mises en évidence. Les deux sollicitations réduisent les niveaux de contrainte et l'effet est plus marqué sur les suspensions avec peptisant (à interaction répulsive dominante) que sur les suspensions sans peptisant (à interaction attractive dominante). Les évolutions temporelles de la structure et de la concentration en colloïdes en fonction de la distance à la membrane ont ainsi été caractérisées sous différentes conditions de filtration et de sollicitations ultrasonores. Deux mécanismes principaux ont été mis en évidence lors de l'application des ultrasons : soit un mécanisme de fracturation ou d'intensification locale de l'écoulement lorsque les colloïdes forment un réseau dense très anisotrope (cas des suspensions de Montmorillonite et de Laponite sans peptisant), soit un mécanisme d'érosion des couches concentrées pour les colloïdes assemblés en structures ouvertes (cas des suspensions de Laponite avec peptisant)

Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2  =  0.74, RMSE  =  0.009 m3 m−3) when the wheat is smaller than one wavelength (∼ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2  =  0.98, RMSE  =  6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])