Zohra Lili Chabaane

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. 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. Maps of soil moisture, clay and sand percentages at the studied site are derived.

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

Clay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a OPEN ACCESS Remote Sens. 2015, 7 6060 time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R 2 = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping.

Using a time series of Landsat TM data for digital mapping to fill information gaps in topsoil texture central Tunisia

In arid and semi-arid areas, bare soils occupy a larger area than the vegetation cover. The vegetation covers only 10% to 30% of the soil surface with seasonal chlorophyll activity. The soil surface should thus be directly detectable by remote sensing. Concerning our study site in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for environmental modeling or thematic mapping. The main purpose of our study was to produce topsoil texture map at fine spatial resolution over our area by combination of Landsat Thematic Mapper data. Landsat images were acquired in summer and in the plowing and sowing period in fall. A maximum of one image was selected per year. Vegetation areas were masked using a sill of the normalized difference vegetation index for each image. Relationships between textural indices (MID-Infrared) and particle size analysis were studied and were used to produce clay map at a spatial resolution of 30 m. Ordinary kriging and cokriging, by combining more than one image, were used to fill in the gaps created by the vegetation mask and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that ordinary kriging can identify certain linear structures such as the wadi bed with low estimated clay content. Cokriging using more than one date improved the prediction of the soil fraction over the masked area.

Prediction accuracy of local and regional soil total carbon models, calibrated based on visible-near infrared spectra, in the Djerid arid region

Visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) is recognized as a promising tool for predicting various soil physico-chemical and biological properties. However, models’ applicability, transferability, and scaling are still questionable. Our objective was to study, for total carbon, these aspects in arid context. QuickBird satellite images enabled us to establish parsimonious soil sampling strategies over three different sites selected in Djerid arid region. For each site, a spectral local database was built and merging them allowed us to obtain a regional database. The principal component analysis enabled us to select independent calibration and validation sets. Local spectral models were performing well for two sites and poorly for the site with high salinity. In cross-transfer, local models showed limited geographic robustness. The regional model was less efficient than one of the local models, yet quite satisfactory (r2 = 0.67, bias = 0.18%, RMSEP = 0.93% and REP = 1.72). The choice of local or regional model should depend not only on performance of the model but also on the purpose of the intended application and the required precision.

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

The 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). For spatial and temporal surface roughness estimation, empirical relationships between radar and soil roughness parameters (rms height “Hrms”, and Zg parameter) were proposed. Two roughness classes are identified through radar signal inversion (smooth and ploughed soils). For the retrieval of surface soil moisture at a high spatial resolution, an algorithm combing TerraSAR-X images with continuous thetaprobe measurements was proposed. Two assumptions were studied: (1) roughness variations during the radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. Finally, an empirical relationship was established between the mean moisture values retrieved from the SAR images and the percentage of clay over several test fields. Results showed that highly accurate clay estimations can be achieved.

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

The 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.