This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

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.

Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. Remote sensing has long been used as an input for crop water balance monitoring. The increasing availability of high resolution high repetitivity remote sensing (forthcoming Sentinel-2 mission) offers an unprecedented opportunity to improve this monitoring. In this study, regional crop water consumption was estimated with the SAMIR software (SAtellite Monitoring of IRrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient and the vegetation fraction cover. Three time series of SPOT5 images have been acquired over an irrigated area in central Tunisia along with a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment, which occurred during the first half of 2013. Using invariant objects located in the scene, normalization of the SPOT5 time series was realized based on the SPOT4-Take5 time series. Hence, a NDVI time profile was generated for each pixel. The operationality and accuracy of the SAMIR tool was assessed at both plot scale (calibration based on evapotranspiration ground measurements) and perimeter scale (irrigation volumes) when several land use types, irrigation and agricultural practices are intertwined in a given landscape. Results at plot scale gave after calibration an average Nash efficiency of 0.57 between observed and modeled evapotranspiration for two plots (barley and wheat). When aggregated for the whole season, modeled irrigation volumes at perimeter scale for all campaigns were close to observed ones (resp. 135 and 121 mm, overestimation of 11.5%). However, spatialized evapotranspiration and irrigation volumes need to be improved at finer timescales.

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

In Lebanon, the seasonal snowpack is poorly monitored despite its importance for water resource supply. The snow accumulates on Mount Lebanon in karstic depressions named “sinkholes.” It is important to monitor the evolution of the snow height inside those “sinkholes”, because of their key role as “containers” for seasonal snow. UAV photogrammetry is a major technological breakthrough which allows an accurate monitoring of the snow height. Because the impact of flight parameters on snow height retrievals is not well documented yet, this research aims to evaluate the impact of UAV flight altitude on the resolution and accuracy of the resulting orthomosaic and DSM. The flight missions were done using the Phantom DJI which generated five DSMs. These are validated using total station measurements.The results indicate that the snow DSMs can be retrieved by adopting a resolution of 8 to 84 cm, a point density between 1.43 and 153 points/sqm and a RMSE of 13 to 41 cm. The testing was done using an elevation varying between 50 and 500 m. The results will be compared to total station observations. These results allow the user to choose the suitable flight altitude for required resolution and points density. We suggest that a flight altitude of 100 m is sufficient for the survey of the snow cover elevation.