Guido Pasquariello

Multitemporal C-band radar measurements on wheat fields

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

On current limits of soil moisture retrieval from ERS-SAR data

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.

Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.

Comparison of SAR amplitude vs. coherence flood detection methods - a GIS application.

Flood area detection from multipass Synthetic Aperture Radar (SAR) data can be performed via amplitude change detection techniques. These methods allow flooded zones to be discriminated only when they are flooded at the time of the second passage, and not at the time of the first one. Coherence derived from multipass SAR interferometry can be used instead, as an indicator of changes in the electromagnetic scattering behaviour of the surface, thus potentially revealing all the areas affected by the flood event at any time between the two passes. The paper presents a prototype application of such techniques, that is, a flood map obtained from ERS-1/2 data taken over Beziers (Southern France), through proper thresholding of a combination of amplitude and coherence information. Produced in the framework of an ESA project, the map consists of a DXF vector file which can be imported directly into most commercial GIS software.

SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data

We apply high-resolution, X-band, stripmap COSMO-SkyMed data to the monitoring of flood events in the Basilicata region (Southern Italy), where multitemporal datasets are available with short spatial and temporal baselines, allowing interferometric (InSAR) processing. We show how the use of the interferometric coherence information can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which affect algorithms based on SAR intensity alone. The effectiveness of using the additional InSAR information layer is illustrated by RGB composites of various combinations of intensity and coherence data. Analysis of multitemporal SAR intensity and coherence trends reveals complex behavior of various field types, which we interpret through a Bayesian inference approach, based on a manual identification of representative scattering and coherence signatures of selected homogeneous fields. The approach allows to integrate external, ancillary information to derive a posteriori probabilistic maps of flood inundation accounting for different scattering responses to the presence of water. First results of this semiautomated methodology, using simple assumptions for the SAR signatures and a priori information based on the distance from river courses, show encouraging results, and open a path to improvement through use of more complex hydrologic and topo-hydrographic information.

NEW REGULARIZATION SCHEME FOR PHASE UNWRAPPING

A new, to our knowledge, algorithm for the phase unwrapping (PU) problem that is based on stochastic relaxation is proposed and analyzed. Unlike regularization schemes previously proposed to handle this problem, our approach dispells the following two assumptions about the solution: a Gaussian model for noise and the magnitude of the true phase-field gradients being less than pi everywhere. We formulate PU as a constrained optimization problem for the field of integer multiples of 2pi, which must be added to the wrapped phase gradient to recover the true phase gradient. By solving the optimization problem using simulated annealing with constraints, one can obtain a consistent solution under difficult conditions resulting from noise and undersampling. Results from synthetic test images are reported.

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

Detection and tracking of oil slicks on sun-glittered visible and near infrared satellite imagery

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.

On the Estimation of Thickness of Marine Oil Slicks From Sun-Glittered, Near-Infrared MERIS and MODIS Imagery: The Lebanon Oil Spill Case Study

The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil-water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MEdium Resolution Imaging Spectrometer Instrument (MERIS) and MODerate Resolution Imaging Spectroradiometer (MODIS) images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, co-located SAR imagery was also considered to corroborate NIR-detected oil slicks.

Three different unsupervised methods for change detection: an application

In this work, unsupervised change detection techniques, based on three different way to compare images, are presented. Two Landsat TM registered and corrected multi-spectral images, acquired on the same geographical area on 18 May 1996 and 21 May 1997, have been used. In the first comparison technique, for each pair of corresponding pixels, the spectral change vector has been computed as the squared difference in the features vectors at the two times. In the second method, the difference image has been computed using, pixel by pixel, a chi square transformation. The third technique is based on the application of a Self-Organizing Map (SOM) neural network to clusterize the two images before comparison. The three obtained difference images has been then analyzed by using a fully automatic thresholding method exploiting the expectation-maximization (EM) algorithm. The experimental results obtained for the three difference images are comparable, showing a reliable robustness of the unsupervised approach, and only few change are detected on the analyzed scene. Moreover, the experimental results have been compared with a change detection map computed by using a supervised technique, obtaining a good agreement between unsupervised and supervised results that confirms the reliability of the considered approach. The encouraging obtained results allow to use the so-computed percentage value of changes as probability of class transitions in input to a Bayesian supervised change detection method, as presented in a companion paper by the same authors. In this framework, the unsupervised approach may be used to support supervised techniques, providing land cover transitions that can be used as guess values