1,721,034 research outputs found
Hotter cities, hazier skies: exploring aerosol feedback to urban heat using satellite data
No full textAtmospheric aerosols play a key role in the radiative balance of the Earth system, modulating the amount of solar radiation that reaches the surface and altering longwave heat retention. In urban areas, these processes can influence the development and intensity of the urban heat island (UHI), potentially amplifying or reducing surface temperature anomalies. This work investigates the relationship between aerosols and surface heating using multi-source satellite data. We employ sharpened Landsat land surface temperature (LST) maps and MODIS aerosol optical depth (AOD) products to explore aerosol heat feedbacks at very high spatial resolution. A case study is presented on Poznań, Poland, to show how differences in aerosol loading are associated with distinct UHI signatures. The results highlight the importance of integrating aerosol information into urban climate studies to better understand the complex interactions driving heat extremes
Building a High‐Resolution Climate Gridded Dataset in Complex Terrain: Validating Different Methods in the Abruzzo Region in Italy
No full textEnhancing spatial resolution of OLI 100m thermal bands through hypersharpening of 30m spectral bands
No full textWe propose a two-step spatial enhancement procedure for the two 100m thermal infra red (TIR) bands of LandSat 8/9, captured by its TIR spectrometer (TIRS), approached as a problem of fusion of heterogeneous data, or multimodal fusion. The fusion algorithm is guided by the statistical similarity between the TIR and visible and near infra red (VNIR) and short wave infra red (SWIR) bands provided at 30m by the operational land imager (OLI). In the first step, hyper-sharpening is applied from 100m scale to 30m scale (3:10 scale ratio): the two TIRS bands are spatially enhanced by means of two linear combination of the 30m VNIR+SWIR bands, devised to maximize the correlation with each thermal band at its native 100m scale. In the second step, the thermal bands, previously hyper-sharpened at 30m, are pansharpened through the 15m Panchromatic (PAN) band of OLI. The proposed approach is compared to plain 100m-to-15m pansharpening carried out uniquely by means of the Pan image of OLI. Both visual evaluations and statistical indexes measuring the radiometric and spatial consistency at the three scales are provided and discussed. The superiority of the two-step approach is undoubtedly highlighted
Fusion of Sentinel-2 and Sentinel-3 data for high-resolution daily aerosol advection mapping in urban areas
No full textWe present a real-time unsupervised procedure for generating maps of advected aerosols, mainly dust and smoke from biomass burning, that combines the 10m scale, suitable for urban environments, allowed by Sentinel-2 imagery, and the almost daily revisit capabilities of Sentinel-3. The map is generated at 10m scale from the bands 2, 3, 4, and 5 of Sentinel-2, L1C, and L2A formats, every five days, in the absence of cloud cover. Then it is extended daily via spatial modulation by an analogous map, calculated from the visible and near-infrared (VNIR) observations of the OLCI instrument onboard Sentinel-3, available at 300 scale. The bands of the two satellites are separately processed and fusion occurs at feature level, by combining the maps of Sentinel-3 with the maps of Sentinel-2. So, the average aerosol content in a 300m×300m cell is updated almost daily; the spatial distribution of the aerosols (spatial modulation) within the 300m×300m cell is inferred for the missing dates of the Sentinel-2 map. The spatial index is an upgrade of a preliminary study aimed at discriminating dust and smoke based on their scattering properties. The overall chain is validated through correlations with the point measurements of fine- and coarse-mode AOD performed by an AERONET station available at the test site
Full-scale assessment of pansharpening: why literature indexes may give contradictory results and how to avoid such an inconvenience
No full textIn this paper, we wish to explain the contradiction of quality assessments of pansharpening carried out at full and reduced spatial scales. It seems that at full scale, methods based on Component Substitution (CS) are quantitatively poorer than the other methods, but this depends on the intrinsic space varying misregistration between the two datasets. At reduced scale, the local shifts are divided by the MS-to-Pan scale ratio and thus they tend to vanish. The problem of full-scale quality indexes is that they were originally validated on aerial Multispectral (MS) data, with synthetic panchromatic (Pan) and thus total absence of misregistration. In the presence of local misregistration due to inaccurate information of the height of the imaged surface, CS methods locally align the lowpass MS components towards the sharpening Pan, thereby preserving the geometry of the scene; all the other methods produce fading contours because of shifts. The favorable property of CS, however, impacts against the (spectral) consistency property of Wald’s protocol, developed when the misalignments between MS and Pan was a small fraction of the pixel size, and hence negligible. In this perspective, methods that do not shift the original MS information are better, even though the visual quality of fading contours is worse. After exposing and explaining the contradiction between full- and reduced-scale assessments, we perform an in-depth analysis of the spectral and spatial consistency indexes of three widespread full-scale protocols: QNR, KQNR and HQNR. We investigate the robustness to shifts of all consistency indexes and propose to couple the spectral index and the spatial index that are least sensitive to shifts. In this way, the ranking of methods of reduced-scale assessments is preserved in full-scale assessments
Fast multispectral pansharpening based on a hyper-ellipsoidal color space
No full textIn this paper, we present a modified version of a popular component-substitution (CS) pansharpening method, namely the hyperspherical color space (HCS) fusion technique. Unlike other improvements of HCS, the proposed method is insensitive to the format of the data, either calibrated spectral radiance values or uncalibrated 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, in order to find out the intensity component peculiar of CS methods. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the color space hyper-ellipsoidal instead of hyper-spherical and the intensity component more similar to the lowpass-filtered Pan, such that the extracted detail, namely Pan minus intensity, is more accurate. Furthermore, before the regression is calculated, the interpolated MS bands are diminished by their minima, in order to build a multiplicative injection model with approximately de-hazed components, thereby benefiting from the haze correction, as for all methods exploiting the multiplicative model. Experiments on true GeoEye-1 images show consistent advantages over the baseline HCS and its improvements achieved over time, and a performance comparable with some of the most advanced methods
Characterizing Dust and Biomass Burning Events from Sentinel-2 Imagery
Full text linkThe detection and evaluation of biomass burning and dust events are critical for understanding their impact on air quality, climate, and human health, particularly in the Mediterranean region. This research pioneers an innovative methodology that uses Sentinel-2 multispectral (MS) imagery to meticulously pinpoint and analyze long-transport dust outbreaks and biomass burning phenomena, originating both locally and transported from remote areas. We developed the dust/biomass burning (DBB) composite normalized differential index, a tool that identifies clear, dusty, and biomass burning scenarios in the selected region. The DBB index jointly employs specific Sentinel-2 bands: B2-B3-B4 for visible light analysis, and B11 and B12 for short-wave infrared (SWIR), exploiting the specificity of each wavelength to assess the presence of different aerosols. A key feature of the DBB index is its normalization by the surface reflectance of the scene, which ensures independence from the underlying texture, such as streets and buildings, for urban areas. The differentiation involves the comparison of the top-of-atmosphere (TOA) reflectance values from aerosol events with those from clear-sky reference images, thereby constituting a sort of calibration. The index is tailored for urban settings, where Sentinel-2 imagery provides a decametric spatial resolution and revisit time of 5 days. The average values of DBB achieve a 96% match with the coarse-mode aerosol optical depths (AOD), measured by a local station of the AERONET network of sun-photometers. In future studies, the map of DBB could be integrated with that achieved from Sentinel-3 images, which offer similar spectral bands, albeit with much less fine spatial resolution, yet benefit from daily coverage
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A dark target Kalman filter algorithm for aerosol property retrievals in urban environment using multispectral images
No full textNatural and anthropogenic aerosol atmospheric emissions play a fundamental role in directly modulating the incoming solar radiation and affecting the air quality, especially in large metropolitan regions. Likewise, aerosols indirectly impact cloud lifetime, atmospheric column thermodynamics and precipitation patterns. For these reasons, it is of particular importance to assess the aerosol spatial and temporal variability in the first instance to reduce the associated global climate models uncertainty to correctly forecasting future scenarios and then to react fast in applying mitigation strategies. In this paper, an aerosol optical depth (AOD) retrieval algorithm for high-spatial resolution images in the blue wavelength range for urban environments is developed for the first time. The proposed approach is completely blind because does not use look-up-tables or complex radiative transfer models, which require the setting/estimation of many parameters. The multi-wavelength (exploiting the coastal and the blue bands) AOD retrieval permits to retrieve also important aerosol micro-physical properties, e.g., the size. The proposed method leverages on the use of Kalman filters to deal with the unavoidable sensor's noise improving the accuracy of the estimation of the AOD. The approach is assessed on four different test cases acquired by Landsat 8 involving two metropolitan areas. A strong agreement to ground-based AERONET measurements is observed on several performance metrics. Clear advantages in comparison with the baseline approach relied upon the simple inversion of the explored model are pointed out
- …
