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The Alps represent an extremely complex environment in which snow properties suffer dramatic spatial variations that cannot easily be followed by space-borne microwave radiometers, due to their coarse spatial resolution: some studies demonstrated that the algorithms developed for global scale monitoring of the snow depth (SD) are unable to retrieve this parameter with a satisfactory accuracy on mountainous areas. An improved method for monitoring the Snow Depth (SD) on Alpine areas is presented here. Equivalent Brightness Temperature Tbeq at an enhanced spatial resolution, corrected for the effects of orography and forest coverage, were computed from the AMSR-E measurements by using ancillary information on land use, surface temperature, and a digital elevation model (DEM). These equivalent Tbeq values were used instead of the original AMSR-E measurements as inputs of an algorithm that estimates SD on a global scale basing on and Artificial Neural Network (ANN) techniques from AMSR-E brightness temperatures at X-, Ku- and Ka-bands, V-polarization. The improvement in the retrieval accuracy using these Tbeq equivalent values was evaluated using data collected during the winters between 2002 and 2011 on a test area located in the eastern part of the Italian Alps.

In this paper, the soil moisture content (SMC) estimated from Advanced Microwave Scanning Radiometer 2 (AMSR2) through the ANN-based “HydroAlgo” algorithm is firstly compared with the outputs of the Soil Water Balance hydrological model (SWBM). The comparison is performed over Italy, by considering all the available overpasses of AMSR2, since July 2012. The SMC generated by Hydroalgo is then considered as input for generating a rainfall product through the SM2RAIN algorithm. The comparison between observed and estimated rainfall in central Italy provided satisfactory results with a substantial room for improvement. The aim of this work is to exploit the potential of AMSR2 for hydrological applications on a regional scale and in heterogeneous environments characterised by different surface covers at subpixel resolution.

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.

WOS:000345894600024; International audience; This paper provides a comparative analysis of statistical algorithms for the retrieval of the wet tropospheric correction from microwave radiometers in the context of altimetry missions. The algorithms are based on the algorithms used in Envisat and Jason-1 missions. The objective of this comparison is two-folds:1) To find which regression method is better suited for the retrieval between the neural network algorithm and the log linear regression.2) To tackle the problem of variable selection, i.e. to find the best set of variables to include as inputs in order to reduce the retrieval error. In particular, we want to determine whether the lack of a radiometer third channel at 18GHz can be compensated by the altimeter backscattering coefficient.Several configurations of algorithms, including those used in the operational processing of altimetry missions such as JASON-1 or ENVISAT, are built and compared on the same learning and test databases to determine which retrieval strategy is more appropriate. The database is composed of atmospheric and oceanographic conditions taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and the brightness temperatures are simulated using a radiative transfer model. The importance of each input for the different algorithms is analyzed and the performances of the different algorithms are assessed in terms of error (bias and standard deviation) but also in terms of geographical distribution of the errors and correlation with other environmental variables. The results are then validated on Jason-2 radiometer measurements.Our results show that, in terms of variable selection, better results were obtained when the brightness temperature at 18 GHz was used instead of the backscattering coefficient. Moreover, better estimations of the wet tropospheric path delay were obtained with neural networks.