In this work, the surface soil moisture (SMC) derived from the AMSR-E acquisitions by using Artificial Neural Networks (ANN) is compared with simulated data obtained from the application of a soil water balance model in central Italy. All the overpasses available for the 9-years lifetime of AMSR-E have been considered for the comparison, which was carried out point by point over a grid of 91 nodes spaced at 0.1×0.1°, roughly corresponding to the Umbria region. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular, for SMC monitoring at regional scale in heterogeneous environments.
Robust assessment of an operational algorithm for the retrieval of soil moisture from AMSR-E data in central Italy
Brocca L;Ciabatta L
2015
Abstract
In this work, the surface soil moisture (SMC) derived from the AMSR-E acquisitions by using Artificial Neural Networks (ANN) is compared with simulated data obtained from the application of a soil water balance model in central Italy. All the overpasses available for the 9-years lifetime of AMSR-E have been considered for the comparison, which was carried out point by point over a grid of 91 nodes spaced at 0.1×0.1°, roughly corresponding to the Umbria region. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular, for SMC monitoring at regional scale in heterogeneous environments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
