Aerosol direct radiative effects under cloud-free conditions over highly-polluted areas in europe and mediterranean: A ten-years analysis (2007–2016)

This study investigates changes in aerosol radiative effects on two highly urbanized regions across the Euro-Mediterranean basin with respect to a natural desert region as Sahara over a decade through space-based lidar observations. The research is based on the monthly-averaged vertically-resolved aerosol optical depth (AOD) atmospheric profiles along a 1◦ × 1◦ horizontal grid, obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument measurements aboard the Cloud-Aerosol lidar and Infrared Pathfinder Satellite Observation (CALIPSO). To assess the variability of the anthropogenic aerosols on climate, we compared the aerosol vertical profile observations to a one-dimensional radiative transfer model in two metropolitan climate sensible hot-spots in Europe, namely the Po Valley and Benelux, to investigate the variability of the aerosol radiative effects and heating rate over ten years. The same analysis is carried out as reference on the Sahara desert region, considered subject just to natural local emission. Our findings show the efficacy of emission reduction policies implemented at government level in strongly urbanized regions. The total atmospheric column aerosol load reduction (not observed in Sahara desert region) in Po Valley and Benelux can be associated with: (i) an increase of the energy flux at the surface via direct effects confirmed also by long term surface temperature observations, (ii) a general decrease of the atmospheric column heating rate, and likely (iii) an increase in surface temperatures during a ten-year period. Summarizing, the analysis, based on the decade 2007–2016, clearly show an increase of solar irradiation under cloud-free conditions at the surface of +3.6 % and +16.6% for the Po Valley and Benelux, respectively, and a reduction of −9.0% for the Sahara Desert

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Advantages of nonlinear intensity components for contrast-based multispectral pansharpening

In this study, we investigate whether a nonlinear intensity component can be beneficial for multispectral (MS) pansharpening based on component-substitution (CS). In classical CS methods, the intensity component is a linear combination of the spectral components and lies on a hyperplane in the vector space that contains the MS pixel values. Starting from the hyperspherical color space (HCS) fusion technique, we devise a novel method, in which the intensity component lies on a hyper-ellipsoidal surface instead of on a hyperspherical surface. The proposed method is insensitive to the format of the data, either floating-point spectral radiance values or fixed-point packed digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the intensity component no longer lie on a hypersphere in the vector space of the MS samples, but on a hyperellipsoid. Furthermore, before the fusion is accomplished, the interpolated MS bands are corrected for atmospheric haze, in order to build a multiplicative injection model with approximately de-hazed components. Experiments on GeoEye-1 and WorldView-3 images show consistent advantages over the baseline HCS and a performance slightly superior to those of some of the most advanced methodsPeer ReviewedPostprint (published version

Haze correction for contrast-based multispectral pansharpening

In this letter, we show that pansharpening of visible/near-infrared (VNIR) bands takes advantage from a correction of the path-radiance term introduced by the atmosphere during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is, for methods exploiting a contrast-based injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. Such methods are high-pass modulation (HPM), Brovey transform, synthetic variable ratio (SVR), University of New Brunswick pansharp, smoothing filterbased intensity modulation, and spectral distortion minimization. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished and added back after. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized SVR and HPM algorithms. Simulations carried out on QuickBird and IKONOS data highlight that haze correction of MS before fusion is always beneficial, especially on vegetated areas and in terms of spectral quality. Index Terms-Haze, image fusion, multispectral (MS) pansharpening, path radiance, radiative transfer model, remote sensing