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Baronti, S.
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Baronti, S.
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- PublicationOpen AccessNoise modelling and estimation of hyperspectral data from airborne imaging spectrometers(2006-02)
; ; ; ; ; ; ; ;Aiazzi, B.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy ;Alparone, L.; Dipartimento di Elettronica e Telecomunicazioni, Università degli Studi di Firenze, Italy ;Barducci, A.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy ;Baronti, S.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy ;Marcoionni, P.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy ;Pippi, I.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy ;Selva, M.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy; ; ; ; ; ; The definition of noise models suitable for hyperspectral data is slightly different depending on whether whiskbroom or push-broom are dealt with. Focussing on the latter type (e.g., VIRS-200) the noise is intrinsically non-stationary in the raw digital counts. After calibration, i.e. removing the variability effects due to different gains and offsets of detectors, the noise will exhibit stationary statistics, at least spatially. Hence, separable 3D processes correlated across track (x), along track (y) and in the wavelength (λ), modelled as auto-regressive with GG statistics have been found to be adequate. Estimation of model parameters from the true data is accomplished through robust techniques relying on linear regressions calculated on scatter-plots of local statistics. An original procedure was devised to detect areas within the scatter-plot corresponding to statistically homogeneous pixels. Results on VIRS-200 data show that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and somewhat correlated along and across track by slightly different extents. Spectral correlation has been investigated as well and found to depend both on the sparseness (spectral sampling) and on the wavelength values of the bands that have been selected.350 1255 - PublicationRestrictedSeasonal and diurnal variations of greenhouse gases in Florence (Italy): Inferring sources and sinks from carbon isotopic ratios(2020-01-01)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; In this study, the results of a continuous monitoring of (i) CO2 fluxes, and (ii) CO2 and CH4 concentrations and carbon isotopic ratios (δ13C-CO2 and δ13C-CH4) in air, carried out from 7 to 21 July 2017 and from October 10 to December 15, 2017 in the city centre of Florence, are presented. The measurements were performed from the roof of the historical building of the Ximenes Observatory. CO2 flux data revealed that the metropolitan area acted as a net source of CO2 during the whole observation period. According to the Keeling plot analysis, anthropogenic contributions to atmospheric CO2 were mainly represented by vehicular traffic (about 30%) and natural gas combustion (about 70%), the latter contributing 7 times more in December than in July. Moreover, the measured CO2 fluxes were about 80% higher in fall than in summer, confirming that domestic heating based on natural gas is the dominant CO2 emitting source in the municipality of Florence. Even though the continuous monitoring revealed a shift in the δ13C-CO2 values related to photosynthetic uptake of atmospheric CO2, the isotopic effect induced by plant activity was restricted to few hours in October and, to a lesser extent, in November. This suggests that urban planning policies should be devoted to massively increase green infrastructures in the metropolitan area in order to counterbalance anthropogenic emissions. During fall, the atmospheric CH4 concentrations were sensibly higher with respect to those recorded in summer, whilst the δ13C-CH4 values shifted towards heavier values. The Keeling plot analysis suggested that urban CH4 emissions were largely related to fugitive emissions from the natural gas distribution pipeline network. On the other hand, δ13C-CH4 monitoring allowed to recognize vehicular traffic as a minor CH4 emitting source.64 5