Dr. Giuliana Pennucci Profile

Dr. Giuliana Pennucci

Engineer at STO-CMRE

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Publications (2)

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Proceedings Article | 19 May 2015 Paper

Identification of sea surface temperature (SST) variability areas through a statistical approach using remote sensing and numerical ocean model data

Jesus Loeches, Raul Vicen-Bueno, Giuliana Pennucci, Aniello Russo

Proceedings Volume 9459, 945910 (2015) https://doi.org/10.1117/12.2177044

KEYWORDS: Remote sensing, Nano opto mechanical systems, Data modeling, In situ metrology, Statistical analysis, Temperature metrology, Clouds, Statistical modeling, Satellites, Environmental sensing

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An understanding of environmental variability (stability/instability) is important to support operational planning of expeditionary warfare and littoral operations, as well as for preparing the Recognized Environmental Picture (REP). Specifically, the identification of environmentally stable/unstable areas helps the planning of maritime operations, increasing their likelihood of success. The purpose of the paper is to describe a methodology to form and interpret an initial spatial-temporal variability characterization of maritime areas from Remote Sensing (RS) and Numerical Ocean Model (NOM) data. As a case study, the analysis of the sea surface tem- perature (SST) in the Black Sea from historical time-series of RS imagery and NOM data is considered. The results of the analysis are validated with in situ measurements from moorings. Identification of gaps of geospatial information is also done in this study. The analysis is focused on monthly spatial-temporal variability of the SST, generating stability maps displaying the geospatial distribution of environmentally stable/unstable areas along a year. The results show how the proposed methodology captures the temporal variability of the SST in the Black Sea, being compared with in situ measurements, and provides useful information for the identification of environmentally stable/unstable areas. The results show a general agreement in the variability with both RS and NOM data, when RS imagery may be used for the present analysis, i.e. when low cloud coverage is given. This paper demonstrates that when RS imagery gaps are not negligible (e.g. due to high cloud occurrence in winter season), these gaps could be filled with NOM data.

Proceedings Article | 12 June 2012 Paper

A methodology for calibration of hyperspectral and multispectral satellite data in coastal areas

Giuliana Pennucci, Giulietta Fargion, Alberto Alvarez, Charles Trees, Robert Arnone

Proceedings Volume 8372, 83720K (2012) https://doi.org/10.1117/12.920464

KEYWORDS: Satellites, In situ metrology, Calibration, Ocean optics, MODIS, Satellite imaging, Sensors, Aerosols, Earth observing sensors, Water

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The objective of this work is to determine the location(s) in any given oceanic area during different temporal periods where in situ sampling for Calibration/Validation (Cal/Val) provides the best capability to retrieve accurate radiometric and derived product data (lowest uncertainties). We present a method to merge satellite imagery with in situ measurements, to determine the best in situ sampling strategy suitable for satellite Cal/Val and to evaluate the present in situ locations through uncertainty indices. This analysis is required to determine if the present in situ sites are adequate for assessing uncertainty and where additional sites and ship programs should be located to improve Calibration/Validation (Cal/Val) procedures. Our methodology uses satellite acquisitions to build a covariance matrix encoding the spatial-temporal variability of the area of interest. The covariance matrix is used in a Bayesian framework to merge satellite and in situ data providing a product with lower uncertainty. The best in situ location for Cal/Val is then identified by using a design principle (A-optimum design) that looks for minimizing the estimated variance of the merged products. Satellite products investigated in this study include Ocean Color water leaving radiance, chlorophyll, and inherent and apparent optical properties (retrieved from MODIS and VIIRS). In situ measurements are obtained from systems operated on fixed deployment platforms (e.g., sites of the Ocean Color component of the AErosol RObotic NETwork- AERONET-OC), moorings (e.g, Marine Optical Buoy-MOBY), ships or autonomous vehicles (such as Autonomous Underwater Vehicles and/or Gliders).

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