Monitoring of soil moisture using a microwave based variational wetness index

Lacava, Teodosio; Faruolo, Mariapia; Pergola, Nicola; Temimi, Marouane; Khanbilvardi, R.; Coviello, I.; Tramutoli, Valerio; Wang, D.

doi:10.1109/microrad.2012.6185233

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…When implemented on AMSR-E data, RST uses as signal to be investigated for monitoring soil moisture variations the Polarization Ratio (PR = Tbv − Tbh/Tbv + Tbh, where Tb stands for brightness temperature and the subscripts v and h indicate vertical and horizontal polarization, respectively) in order to compute a suitable Polarization Ratio Variation Index (PRVI) (Temimi et al 2011;Lacava et al 2012), defined as: PRVI 6;9 x, y, t ð Þ¼ PR 6;9 x, y, t ð ÞÀl PR 6;9 x, y ð Þ r PR 6;9 x;y ð Þ ð126:3Þ…”

Section: The Robust Satellites Techniques Approachmentioning

confidence: 99%

Integration of Optical and Passive Microwave Satellite Data for Flooded Area Detection and Monitoring

Lacava

Brocca

Coviello

et al. 2014

Engineering Geology for Society and Territory - Volume 3

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Flooding represents a serious threat to millions of people around the world and its hazard is rising as a result of climate changes. From this perspective, flood risk management is a key focus of many governments, whose priority is to have frequently updated and accurate information about the flood state and evolution to promptly react to the disaster and to put in place effective countermeasures devoted to limit damages and human lives losses. Remote sensing technology allows for flood monitoring at different spatial and temporal resolutions with an adequate level of accuracy. In particular, for emergency response purposes, an integrated use of satellite data, acquired by both optical and passive or active microwave instruments, has to be preferred to have more complete and frequently updated information on soil conditions and to better support decision makers. In this framework, multi-year time series of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data were processed and analyzed. In detail, the Robust Satellite Techniques (RST), a multi-sensor approach for satellite data analysis, has been implemented for studying the August 2002 Elbe river flood occurred in Germany, trying to assess the potential of such an integrated system for the determination of soil status and conditions (i.e. moisture variation, water presence) as well as for a timely detection and a near real time monitoring of critical soil conditions.

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Section: The Robust Satellites Techniques Approachmentioning

confidence: 99%