Search

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils. [ABSTRACT FROM AUTHOR]

Abstract-In the framework of the ITALSAT campaign various European institutions carried out propagation measurements at V band. In this frequency band, signal reception in a satellite-to- Earth or Earth-to-satellite path may suffer severely from signal attenuation due to meteorological effects such as rain, snow, hail, fog, clouds, etc., where rain provides the most significant contribution. In this paper, a joint analysis of the ITALSAT propagation measurement campaigns at 39.6 GHz (precisely 39.592 GHz) carried out at both Oberpfaffenhofen, close to Munich, Germany, and Spino d'Adda, close to Milan, Italy, is presented. For both stations long-term attenuation statistics are available and reveal that signal attenuation due to meteorological effects depends very much on the season and on the time of the day. Signal attenuation can be compensated by an appropriate fade margin. In this paper, we show that the required fade margin to guarantee a target link availability has to be chosen very carefully, since it depends very much on the time interval upon which availability is defined. We consider complete years, ITU-R recommendations, all single months of the year, worst months, and spring, summer, fall, and winter. Furthermore, we evaluate diurnal variations for four time intervals: 00:00-06:00, 06:00-12:00, 12:00-18:00, and 18:00-24:00. [ABSTRACT FROM AUTHOR]

This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top–down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework. [ABSTRACT FROM PUBLISHER]

Differential synthetic aperture radar interferometry (DInSAR) is a remote sensing technique that allows monitoring ground deformation with accuracy of the order of fractions of the radiated wavelength, by means of proper combination and processing of repeat-pass data. In contrast to the satellite case, application of such a technique to airborne data is not, today, a well-established task. Several airborne campaigns, involving mainly C/L-band data, have been planned in the last years to exploit the potentialities of these more flexible platforms for deformation monitoring. In this paper, we show the results of an airborne DInSAR X-band experiment carried out over the Perugia area (center of Italy) by using the OrbiSAR system. We discuss the processing chain applied to the acquired data, which allows achieving a satisfactory compromise between accuracy and efficiency. Eleven repeated passes were carried out in two days; two corner reflectors were located on the ground in a hilly region. One corner reflector was vertically moved between the two days to evaluate the system detection capability. Moreover, we carry out an analysis of all possible differential interferograms for a region 2 x 4 km wide. [ABSTRACT FROM AUTHOR]

This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100 x 100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements. [ABSTRACT FROM AUTHOR]