Influence of Radar Frequency on the Relationship Between Bare Surface Soil Moisture Vertical Profile and Radar Backscatter

International audienceThe aim of this letter is to discuss the influence of radar frequency on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to address this issue, the advanced integral equation model was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands, namely, L, C, and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The influence of radar frequency is clearly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, which was acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the utility of taking the soil moisture heterogeneity into account in the backscatter model

Soil surface parameters retrieving from TerraSAR-X SAR data

International audienceThe goal of this study is to analyze the sensitivity of X-band SAR (TerraSAR-X) signals as a function of different physical bare soil parameters (soil moisture, soil roughness), and to demonstrate that it is possible to estimate of both soil moisture, roughness and texture from the same experimental campaign, using a single radar signal configuration (one incidence angle, one polarization). Discussions are based on experimental campaigns acquired on North Africa (Merguellil site, Tunisia) with ground measurements over more than fifteen test fields, simultaneously to seven TerraSAR-X images acquisitions at HH polarization and 36° incidence angle. Firstly, we analyzed statistically the relationships between X-band SAR (TerraSAR-X) backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 36°, over a semi-arid site in Tunisia (North Africa). Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter. Then, we proposed to retrieve of both soil moisture and texture using these multi-temporal X-band SAR images. Our approach is based on the change detection method and combines the seven radar images with different continuous thetaprobe measurements. To estimate soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our approaches are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. Finally, by considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved

Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band SAR data from TerraSAR-X sensor

International audienceWe propose in this study, an approach based on the change detection method for the retrieval of surface soil moisture at a higher spatial resolution. The proposal algorithm combines multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements. More than five thetaprobe stations are installed at different depths over the central semi arid region of Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N). They cover approximately the entire of our study site and provide regional scale information. Ground data were collected over agricultural bare soil fields simultaneously to various TerraSAR-X data acquired during 2013-2014 and 2014-2015. More than fourteen test fields were selected for each spatial acquisition campaigns, with variations in soil texture and in surface soil roughness. For each date, we considered the volumetric water content with thetaprobe instrument and gravimetric sampling; we measured also the roughness parameters with pin profilor

Mapping of surface soil parameters (roughness, moisture and texture) using one radar X-band SAR configuration over bare agricultural semi-arid region

International audienceThe aim of this paper is to estimate geometric, water and physical surface soil parameters from typical semi-arid regions made over bare study area (North Africa) using multi-temporal X-band SAR images (TerraSAR-X)

Multi-frequency analysis of soil moisture vertical heterogeneity effect on radar backscatter

International audienceThe goal of this study is to discuss the effect of multi-frequency radar configurations on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to answer this question, the Advanced Integral Equation Model (AIEM) was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands: L, C and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The effect of radar frequency is distinctly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the usefulness of taking the soil moisture heterogeneity into account in the backscattering model

Irrigation mapping using products derived from Sentinel-1 and Sentinel-2 time series over a Mediterranean semi-arid region

International audienceIn order to ensure food security, semi-arid Mediterranean regions are largely dependent on irrigated agriculture. Irrigated agriculture in such areas can be highly productive and can also provide congenial living conditions. Because of the high contribution of irrigation, monitoring of it actual state is the major issue in these regions and knowing the spatial distribution and year-to-year variability in irrigated areas could be imperative for water resources management. With the arrival of Sentinel-1 and Sentinel-2 satellite, operational approaches are developed for monitoring surface states at the field scale with high spatial and temporal resolution. This present paper develops a methodology based on high spatial resolution remote sensing data for irrigation mapping. The inputs of the approach are the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data every 10 days, and soil moisture time series produced by the inversion of the Water Cloud Model (WCM) using a synergy of Sentinel-1, radar data in VV polarization and Sentinel-2 optical data every 6 days, over the Kairouan plain, in Central of Tunisia, North Africa. The first step was to divide an NDVI image into segments to delineate the agricultural fields. Then, a Support Vector Machine (SVM) classification is performed to distinguish between irrigated and non-irrigated areas, using the mean and variance values of soil moisture computed over the training cereal fields. Three cases were used to classify the fields, using a Decision Tree classification. The resulting irrigation maps were validated using ground truth measurements. The first case computed the mean value of NDVI on each segment, using an empirical threshold to delineate between the irrigated and rainfed fields. The overall accuracy of the classification was about 58%, due to the confusion between the two classes. Then, we combined, the mean value of NDVI and the mean and variance of soil moisture to obtain an overall accuracy of approximately 71 %. Finally, we used only the mean and variance values of soil moisture to produce the irrigation map. The best estimation was obtained using only soil moisture parameters with an accuracy of 77 %. This study demonstrates the high potential of combining radar and optical data for soil moisture estimation, which allows the monitoring of irrigation at the field scale

Potential of X-band SAR data from TerraSAR-X and COSMO-SkyMed sensors to retrieve physical soil properties

International audienceWe propose in this study to analyze the potential of TerraSAR-X and COSMO-SkyMedSAR measurements to retrieve surface parameters over bare soils. We consider a statistical analysis of the relationships between X-band SAR backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 36°. Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal with the strongest correlation observed with gravimetric moisture measurements. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter. We compared, then the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) with SAR measurements at HH and VV polarization. The results show that the IEM model simulates correctly the radar response only for HrmsNous proposons dans cette étude d'analyser le potentiel de TerraSAR-X et des mesures COSMO-SKYMEDSAR pour récupérer des paramètres superficiels sur des sols nus. Nous considérons une analyse statistique des relations entre la X-bande SAR backscattering la fonction de signaux d'humidité de sol et des paramètres de rugosité différents (la racine la hauteur carrée moyenne Hrms, le paramètre Zs et le paramètre Zg) à la polarisation HH et pour un angle d'incidence ENVIRON 36 °. Les résultats ont montré une haute sensibilité de données réelles de radar aux deux paramètres de sol : rugosité et humidité. Une relation linéaire est obtenue entre l'humidité de sol volumétrique et le signal de radar avec la corrélation la plus forte observée avec des mesures d'humidité gravimétriques. Une corrélation logarithmique est observée entre le coefficient backscattering et tous les paramètres de rugosité. La sensibilité dynamique la plus haute est obtenue avec le paramètre Zg. Nous avons comparé, alors la performance de modèles de backscattering physiques et semi-empiriques différents (IEM, Baghdadi-calibré IEM et des modèles de Dubois) avec des mesures SAR À HH et VV polarisation. Les résultats montrent que le modèle d'IEM simule correctement la réponse de radar seulement pour Hrms 1.5cm l'utilisation de la fonction de corrélation exponentielle dans la polarisation HH et pour Hrms 1.5cm l'utilisation de la fonction de corrélation exponentielle et Hrms > 1.5cm avec la fonction gaussienne dans VV polarisation, à 36 °. Des contradictions importantes (ou sous l'évaluation) sont généralement observées entre la X-bande mesurée SAR des signaux et le Dubois a simulé backscatters. L'amélioration considérable de la performance de modèle d'IEM a été observée utilisant l'IEM la version Baghdadi-calibrée

Analyse des sensibilités des mesures radar bande-X aux paramètres des surfaces de sols nus

International audienceLes problèmatiques : la pénurie d'eau, les pertes en sols dues à l'érosion, la faible densité du couvert végétal, l'impact défavorable de l'action anthropique. Les axes de recherche sont l'amélioration de la description des états de surface de sols nus en milieux agricoles : introduction d’un nouveau paramètre de rugosité du sol "Zg" et la validation des modèles de rétrodiffusion (IEM, Dubois et IEM calibré par Baghdadi et al., 2011). Le nouveau paraètre "ZG" qu'on introduit après l'application de l'approche numérique basée sur la méthode des moments, a été réalidée sur une base de données acquises sur trois sites d'étude différents en France ainsi que sur le site d'étude à Kairouan. L'analyse statistique de sensibilité des mesures radars (TerraSAR-X et COSM-skyMED) aux paramètres physiques de surface ont montré de fortes corrélations du signal rétrodiffusé vis-à-vis de ces paramètres. Les comparaisons des données radars réelles et simulées à partir du modèle IEM et de Dbois ont montré des imperfectipns de ces modèles (sur ou sous estimations). L'étalonnage semi-empirique du modèle IEM proposé par Baghdadi et al., 2011, a permis d'obtenir des simulations adéquates avec le 0° réel et une réduction des biais observés avec la version initiale

X-band Terrasar-X and COSMO-SkyMed SAR data for bare soil parameters estimation

International audienceThe goal of this paper is to analyze the potential of COSMO-SkyMed and TerraSAR-X SAR measurements over bare soils in order to estimate correctly soil parameters. We analyzed statistically the relationships between X-SAR backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 35.5°. Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal with the strongest correlation observed with gravimetric moisture measurements. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter

Retrieval of both soil moisture and texture using one configuration TerraSAR-X radar images

International audienceThe aim of this study is to propose a methodology combing multi-temporal X-band SAR images (TerraSAR-X) with continuous ground thetaprobe measurements, for the retrieval of surface soil moisture and texture at a high spatial resolution. Our analysis is based on seven radar images acquired at a 36° incidence angle in the HH polarization, over a semi-arid site in Tunisia (North Africa). All ground measurements of surface soil parameters were carried out over several bare soil reference fields located at the Kairouan site. Between November 2013 and January 2014 (three months), ground campaigns were carried out at the same time as the seven satellite acquisitions. The soil moisture estimations are based on an empirical change detection approach using TerraSAR-X data and ground auxiliary thetaprobe network measurements. Two assumptions were tested: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. For the two considered approaches, the soil moisture estimations were validated using ground measurements acquired over fifteen test fields, under different moisture conditions. These comparisons lead to a volumetric moisture RMSE equal to 3.8% and 3.3%, and a bias equal to 0.5% and 0.3%, respectively