Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy

Abstract

In this study, the soil moisture content (SMC) derived from the AMSR-E acquisitions by using the “HydroAlgo” algorithm, which is based on artificial neural networks (ANN), is compared with simulated data obtained from the application of a soil water balance model (SWBM) in central Italy. All the overpasses available for the 9-year lifetime of AMSR-E have been considered for this 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. HydroAlgo includes a disaggregation technique (smoothing filter-based intensity modulation), which allowed obtaining an SMC product with enhanced spatial resolution (0.1°) that is expected to be more suitable for hydrological applications. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular for SMC monitoring on a regional scale in heterogeneous landscapes typical of Mediterranean environment. Slightly different results were obtained using ascending or descending overpasses; however, the overall correlation coefficient between the SMC retrieved by HydroAlgo and the SMC simulated by SWBM was higher than 0.8 and the corresponding root mean square error was less than 0.055 m3/m3. Based on these successful results, HydroAlgo is going to be implemented for current multifrequency microwave radiometers (AMSR2) in order to obtain a high-resolution SMC product suitable to be assimilated into flood- and landslide-related modeling in central Italy.