Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model.

The objective of this paper was to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within this context, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH) polarizations is examined as a function of soil moisture and vegetation properties using statistical correlations. SAR signals scattered by pepper-covered fields are simulated with a modified version of the water cloud model using L-HH and C-VV data.

Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images.

In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed.

Valuing scarce observation of rainfall variability with flexible semi-distributed hydrological modelling - Mountainous Mediterranean context.

To represent spatial and temporal variability in rainfall adequately, rainfall-runoff models must compromise among modelling objectives, data availability, conceptualization options, and the actual variability in rainfall. This is of utmost importance for challenges of integrated water management in the rapidly changing Mediterranean context. We evaluated the sensitivity of the SWAT model to combinations of spatial rainfall variability and catchment subdivision in a data-scarce mesoscale mountainous Mediterranean context.

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters.

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI.

Modeling water quality to improve agricultural practices and land management in a tunisian catchment using the soil and water assessment tool.

Agriculture intensification has impaired water quality. In this study, the risk of pollution by nitrates was assessed by experimental monitoring, spatial integration of farm census, and modeling of water quality using the Soil and Water Assessment Tool (SWAT), version 2009, over the period of 1990 to 2006 for a catchment located northern Tunisia. Under a semiarid climate, the water quality is influenced by the predominating agriculture activities.