Alterações nas reservas de sementes de Dalbergia nigra ((Vell.) Fr. All. ex Benth.) durante a hidratação

Seed imbibitions is the first stage of the germination process and is characterized by the hydration of tissues and cells and the activation and/or induction of the enzymes responsible for mobilizing reserves for respiration and the construction of new cell structures. The objective of this study was to investigate the alterations in reserve substances during slow hydration of Bahia Rosewood (Dalbergia nigra) seeds in water. Seeds from two different lots (Lot I and II) were placed in saturated desiccators (95-99% RH) to hydrate at 15 and 25 °C until water contents of 10, 15, 20 and 25% were reached. At each level of hydration, changes in lipid reserves, soluble carbohydrates, starch and soluble proteins were evaluated. The mobilization of reserves was similarly assessed in both lots, with no differences being observed between the two hydration temperatures. Lipid contents showed little variation during hydration, while the contents of soluble carbohydrates and starch decreased after the 15% water content level. Soluble proteins showed a gradual tendency to decrease between the control (dry seeds) up to 25% water content

Surface Moisture and Irrigation Mapping at Agricultural Field Scale Using the Synergy SENTINEL-1/SENTINEL-2 Data

International audienceSoil moisture plays a key role in various processes at the soil-vegetation-atmosphere interface, such as evapotranspiration, infiltration and runoff. In this study, we firstly propose a global analysis of Sentinel-1 (S1) & Sentinel-2 (S2) data potential to retrieve soil moisture. Two approaches are tested. The first one is based on neural network approach; it uses Integral Equation Model (IEM) coupled to Water Cloud Model for vegetation cover backscattering simulation (El Hajj et al., 2017). The second approach considers change detection methodology. It estimates change of soil moisture with the driest and highest moisture levels, and also change of moisture between successive radar acquisitions (Gao et al., 2017). The proposed approaches are validated over three agricultural regions, south of France, Urgell (Spain) and Merguellil (Tunisia). In these different sites, important ground campaigns have been realized over reference fields with different types of measurements (soil moisture and roughness, Leaf area Index of vegetation cover). The retrieved accuracy of estimated volumetric soil moisture is about 5 vol.%. Based on estimated moisture products, two methodologies are considered to map irrigated areas (Gao et al., 2018, Bousbih et al., 2018). An analysis of different metrics (mean, variance, correlation length, etc.) of radar signal time series and surface parameters (moisture and NDVI) are tested. The proposed classification of irrigated areas is based on a combination of Support Vector Machine (SVM) and decision tree methodologies. For Urgell and Merguellil sites, a mapping of irrigated fields is proposed. The accuracy of mapping is higher than 75% for the two studied sites

Soil surface moisture estimation using the synergy S1/S2 data

International audienceThe main objective of this study is to analyze the potential use of Sentinel-l (S1) radar data for the estimation of soil moisture in agricultural areas. Simultaneously to several S1 acquisitions made between 2015 and 2017 over different sites, ground measurements of soil roughness, soil water content, LAI and crop height were recorded. The sensitivity of S1 signal to variations in soil moisture is discussed. A modelling based on Water Cloud Model was proposed to simulate radar signal backscattered over covered vegetation surfaces. Three inversion approaches were proposed to retrieve surface soil moisture at field scale

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

International audienc