In this work we present a new simple index to estimate water stress (WS) for different types of surfaces, from remotely sensed data. We derive a WS index, named WSIEw, modifying the Water Deficit Index (WDI) proposed ...In this work we present a new simple index to estimate water stress (WS) for different types of surfaces, from remotely sensed data. We derive a WS index, named WSIEw, modifying the Water Deficit Index (WDI) proposed by Moran et al. by using the wet environment evapotranspiration (Ew) instead of the potential evapotranspiration (Epot) concept. Jiang and Islam model was used to simulate actual evapotranspiration (ET) and Priestley and Taylor equation to estimate Ew. The WSIEw results were compared to ground observations of ET, precipitation (PP), soil temperature (Tsoil) and soil moisture (SM) in the Southern Great Plains-EEUU. Preliminary results suggest the method is sensitive to the water status of different surfaces. However, the WSIEw would range from 0 to 0.7, having a value of 0.4 for a dry surface with 5% of SM. The methodology is operationally simple and easy to implement since it requires only information from remote sensors.展开更多
文摘In this work we present a new simple index to estimate water stress (WS) for different types of surfaces, from remotely sensed data. We derive a WS index, named WSIEw, modifying the Water Deficit Index (WDI) proposed by Moran et al. by using the wet environment evapotranspiration (Ew) instead of the potential evapotranspiration (Epot) concept. Jiang and Islam model was used to simulate actual evapotranspiration (ET) and Priestley and Taylor equation to estimate Ew. The WSIEw results were compared to ground observations of ET, precipitation (PP), soil temperature (Tsoil) and soil moisture (SM) in the Southern Great Plains-EEUU. Preliminary results suggest the method is sensitive to the water status of different surfaces. However, the WSIEw would range from 0 to 0.7, having a value of 0.4 for a dry surface with 5% of SM. The methodology is operationally simple and easy to implement since it requires only information from remote sensors.
