Assessing the potential of <i>SWVI</i> (Soil Wetness Variation Index) for hydrological risk monitoring by means of satellite microwave observations

Abstract

Abstract. In the last years satellite remote sensing applications in hydrology have considerably progressed. A new multi-temporal satellite data-analysis approach has been recently suggested in order to estimate space-time changes of geophysical parameters possibly related to the increase of environmental and hydro-geological hazards. Such an approach has been already used both for flooded area mapping (using AVHRR data) and for soil wetness index estimation (using AMSU data).In this work, a preliminary sensitivity analysis of the proposed Soil Wetness Variation Index (SWVI) is made in the case of low intensity meteorological events by the comparison with hydrological (precipitation) data. This analysis, as a first step of a more complex work in progress, is targeted to a first evaluation of the reliability of the SWVI in describing soil response to precipitations of different duration and intensity.

A multi‐temporal analysis of AMSR‐E data for flood and discharge monitoring during the 2008 flood in Iowa

“…RST has been successfully tested in monitoring hydro‐meteorological processes. Lacava et al (2005a) have developed an algorithm to monitor space‐time soil wetness dynamics during major flood events (Lacava et al , 2005b, 2005c, 2006, 2007). Their analysis was expanded also to optical Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (Faruolo et al , 2009; Lacava et al , 2009a).…”

“…The same index was used by Paloscia et al (2001) to assess the vegetation effect and to retrieve soil moisture using observations from the Special Sensor Microwave Imager (SSM/I). In order to distinguish the soil moisture contribution from vegetation and soil roughness contributions to the measured signal, Lacava et al (2005a) successfully applied the Robust Satellite Techniques (RST) approach (Tramutoli et al , 2005, 2007) to Advanced Microwave Sounding Unit (AMSU) data. More recently, Temimi et al (2007) used PR with other ancillary data to account for vegetation heterogeneity and to estimate soil moisture in a large northern watershed.…”

Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique

“…The technique proposed in this paper, christened RST-cover, is based on the more general Robust Satellite Technique (RST, [35]). This approach is a general multitemporal satellite data analysis method, already used with excellent results to investigate different natural hazards [36][37][38][39][40][41][42]. The RST philosophy needs the preliminary characterization of the signal observed (in single bands or band combinations) in terms of expected value (temporal mean) and natural variability (standard deviation) by investigating long-term satellite data series acquired in homogenous conditions.…”

Space–time soil wetness variations monitoring by a multi-temporal microwave satellite records analysis

“…the effects of vegetation cover and/or surface roughness, can be considered negligible. Such an approach has recently been used by Lacava et al (2005a) who proposed a satellite-based Soil Wetness Variation Index (SWVI) to monitor soil wetness variations in the space-time domain during some flooding events which occurred in Europe in the past few years (Lacava, 2004;Lacava et al, 2004Lacava et al, , 2005a, besides, a sensitivity analysis targeted to a first evaluation of the reliability of the SWVI in describing soil response to precipitation of different duration and intensity, is also in progress (Lacava et al, 2005b).…”