Mehrez Zribi

Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas

TerraSAR-X data are processed for an “operational” mapping of bare soils moisture in agricultural areas. Empirical relationships between TerraSAR-X signal and soil moisture were established and validated over different North European agricultural study sites. The results show that the mean error on the soil moisture estimation is less than 4% regardless of the TerraSAR-X configuration (incidence angle, polarization) and the soil surface characteristics (soil surface roughness, soil composition). Furthermore, the potential of TerraSAR-X data (signal, texture features) to discriminate bare soils from other land cover classes in an agricultural watershed was evaluated. The mean signal backscattered from bare soils can be easily differentiated from signals from other land cover classes when the neighboring plots are covered by fully developed crops. This was observed regardless of the TerraSAR-X configuration and the soil moisture conditions. When neighboring plots are covered by early growth crops, a TerraSAR-X image acquired under wet conditions can be useful for discriminating bare soils. Bare soil masks were calculated by object-oriented classifications of mono-configuration TerraSAR-X data. The overall accuracies of the bare soils mapping were higher than 84% for validation based on object and pixel. The bare soils mapping method and the soil moisture relationships were applied to TerraSAR-X images to generate soil moisture maps. The results show that TerraSAR-X sensors provide useful data for monitoring the spatial variations of soil moisture at the within-plot scale. The methods of bare soils moisture mapping developed in this paper can be used in operational applications in agriculture, and hydrology.

Estimating Surface Soil Moisture from TerraSAR-X Data over Two Small Catchments in the Sahelian Part of Western Niger

The objective of this study is to validate an approach based on the change detection in multitemporal TerraSAR images (X-band) for mapping soil moisture in the Sahelian area. In situ measurements were carried out simultaneously with TerraSAR-X acquisitions on two study sites in Niger. The results show the need for comparing the difference between the rainy season image and a reference image acquired in the dry season. The use of two images enables a reduction of the roughness effects. The soils of plateaus covered with erosion crusts are dry throughout the year while the fallows show more significant moisture during the rainy season. The accuracy on the estimate of soil moisture is about 2.3% (RMSE) in comparison with in situ moisture contents.

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

The aim of this paper 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). 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. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. 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. nTwo 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. Maps of soil moisture, clay and sand percentages at the studied site are derived.

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°).

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms2/l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l)alpha (where alpha is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. nThe results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in the IEM model performance are observed using the Baghdadi-calibrated version of this model.

Remote Sensing of Soil

Remote sensing has shown a high potential in soil characteristics retrieving in the last three decades. Different methodologies have been proposed for the estimation of soil parameters, based on different remote sensing sensors and techniques (passive and active).

Characterization of Soil Surface Properties Using Radar Remote Sensing

Abstract: Soil surface characteristics (SSC) play a key role in the understanding of different processes taking place at the soil–vegetation–atmosphere interface (runoff, infiltration, soil erosion, exchange of water and energy streams). Until the 1990s, the only observations used for the modeling of this interface were limited and often unrepresentative of the spatial scales modeled. Radar remote sensing now allows spatial parameters to be accessed for the monitoring of the soil surface and the modeling of its functioning. In fact, signals acquired by radar are strongly correlated to some physical variables that are linked to soil surface conditions, such as soil moisture and surface roughness. The assimilation of these data in functional models (hydrologic, erosion, SVAT (Soil–Vegetation–Atmosphere Transfer) etc.) has shown a clear improvement in the simulation of physical processes.

Radio frequency interferences investigation using the airborne L-band full polarimetric radiometer CAROLS

In the present paper, different methods are proposed for the detection and mitigation of the undesirable effects of radio frequency interference (RFI) in microwave radiometry. The first of these makes use of kurtosis to detect the presence of non-Gaussian signals, whereas the second imposes a threshold on the standard deviation of brightness temperatures, in order to distinguish natural emission variations from RFI. Finally, the third approach is based on the use of a threshold applied to the third and fourth Stokes parameters. All of these methods have been applied and tested, with a CAROLS radiometer operating in the L-band, on data acquired during airborne campaigns made in spring 2009 over the South West of France. The performance of each, or of two combined approaches is analyzed with our database. We thus show that the kurtosis method is well adapted to pulsed RFI, whereas the method based on the second moment is well adapted to continuous-wave RFI.

Empirical model for soil salinity mapping from SAR data

Soil salinization is one of the most hazardous phenomenon accelerating the land degradation processes. Mapping and tracking soil salinity changes is fundamental for anticipating natural disaster, such as desertification, in arid and semi-arid regions. In this work, we establish an empirical model for soil salinity mapping based on a gaussian mixture and using field electrical conductivity (EC) measures. The developed model is tested on saline soil samples collected from the semi-arid region of Kairouan located in central Tunisia. It is based on statistical moments derived from multiband (HH and VV) intensity synthetic aperture radar (SAR) data of the Envisat satellite. The resulting salinity map is composed of three classes of salinity (Low, Medium and High) with respect to the EC measurements. The developed model is validated for low salinity distribution, whereas, it needs more samples to be generalized for medium and high soil salinity content.

The potential use of high resolution X-band SAR moisture products for the calibration of a water balance model over bare agricultural soils (Tunisia)

The aim of this paper was to demonstrate the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modele de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and soil moisture content (SMC) at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). The MHYSAN model was calibrated using seven very high-resolution SAR (TerraSAR-X) SM outputs ranging over only two months. Then, the simulated SM was validated using continuous thetaprobe measurements during 15 months. The high performances observed could be explained by the fact that although images were acquired during a short time range, the important soil moisture variation captured allowed a good calibration of the soil parameters. These results highlight the potential of Sentinel-1 images for daily soil moisture monitoring using simple models.