Spatially remotely sensed evapotranspiration estimates in Sahel regions using an ensemble contextual model : structural uncertainty estimation and reduction 

Abstract

The Sahel region, identified as a "hot spot" for climate change, is characterized by a water scarcity and an inter-annual variability of water resources. Indeed, ongoing climate changes intensify the evaporative demand which could lead to more frequent period of droughts. Therefore, an important issue in these countries is to provide accurate estimation of evapotranspiration (ET) in a spatially distributed manner. The growing number of spatial ET products, including simple empirical equations (e.g., Penman-Monteith), land surface models (LSM), energy balance models, interpolated in-situ measurements, neural network approaches, or data fusion, form an interesting alternative in these areas scarcely gauged. However, until recently, there is no product combining simultaneously good spatiotemporal resolution (i.e.,