Soil salinity is one of the fastest growing threatsthat affects agriculture and food production.Detecting and evaluating the impact of soil salinitywith traditional methods has been a laborious and time consuming job, recently methods for detectingsoil salinity have improved greatly thanks to newtechnology. This book describes methods to detectsoil salinity levels in agricultural lands based oncrop conditions and Evapotranspiration (ET) usingsatellite imagery. Quantifying ET accurately has always been consideredone of the most challenging and expensive tasks, mostof the methods currently used to achieve this taskdepend on point measurements that do not reflect thespatial variability of ETin agriculture fields. This book describes thedevelopment of a surface energy balance-based model(Remove Sensing of ET - ReSET) that handles thespatial variability in ET, the model estimates ETbased on remotely sensed data using satelliteimagery. ET is estimated for each pixel in the imagetherefore displaying the spatial variability of ETeven within fields. Relationships between ET and soilsalinity levels in agriculture fields were developedfor several crops.
Soil salinity is one of the fastest growing threatsthat affects agriculture and food production.Detecting and evaluating the impact of soil salinitywith traditional methods has been a laborious and time consuming job, recently methods for detectingsoil salinity have improved greatly thanks to newtechnology. This book describes methods to detectsoil salinity levels in agricultural lands based oncrop conditions and Evapotranspiration (ET) usingsatellite imagery. Quantifying ET accurately has always been consideredone of the most challenging and expensive tasks, mostof the methods currently used to achieve this taskdepend on point measurements that do not reflect thespatial variability of ETin agriculture fields. This book describes thedevelopment of a surface energy balance-based model(Remove Sensing of ET - ReSET) that handles thespatial variability in ET, the model estimates ETbased on remotely sensed data using satelliteimagery. ET is estimated for each pixel in the imagetherefore displaying the spatial variability of ETeven within fields. Relationships between ET and soilsalinity levels in agriculture fields were developedfor several crops.
Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop...
This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales,...
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