Evaluation of PERSIANN database in the framework of SMOS Calibration/Validation activities over Valencia Anchor Station

Abstract

Soil moisture is a key parameter for land surface water resource and climate change monitoring. ESA’s Soil Moisture and Ocean Salinity (SMOS) mission will deliver, as one of its main goals, global fields of surface soil moisture with an accuracy better than 0.04m3m−3. SMOS relies on an L-band (1.4GHz) interferometric radiometer. Within the context of the preparation for this mission over land, the Valencia Anchor Station (VAS) experimental site, in Spain, was selected to be one of the main test sites in Europe for the SMOS Calibration/Validation (Cal/Val) activities. This study presents preliminary analysis of PERSIANN in the framework of SMOS Cal/Val activities at the Valencia Anchor Station. The PERSIANN database is an au- tomated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks. The real interest of using the PERSIANN database into the hydrologic applications is mainly with the goal of having access to the spatial and tem- poral distribution of precipitation over a significant area (typically here an area equivalent to a SMOS pixel). The goal of this study is to quantify the gain of using PERSIANN instead of distributing sparse rain gauge measurements. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil was depicted by a comparison between different soil moisture products. Having an accurate estimation of the amount and temporal/spatial distribution of precipitation is a critical issue so as to have a faithful representation of the soil moisture distribution.