Search Results (37)

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM_2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3. Full article

Pollutants information can be retrieved from visible (VIS) and ultraviolet (UV) diffuse solar spectra exploiting Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. In May 2021, the Italian research institute CNR-ISAC acquired and deployed a MAX-DOAS system SkySpec-2D. It is located in the “Giorgio Fea” observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, where it has constantly acquired zenith and off-axis diffuse solar spectra since the 1st October 2021. This work presents the retrieved tropospheric NO₂ and aerosol extinction profiles (and their columns) derived from the MAX-DOAS measurements using the newly developed DEAP retrieval code. The code has been validated both using synthetic differential Slant Column Densities (dSCDs) from the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project and real measured data. For this purpose, DEAP results are compared with the ones obtained with three state-of-the-art retrieval codes. In addition, an inter-comparison with satellite products from Sentinel-5P TROPOMI, for the tropospheric NO₂ Vertical Column Densities (VCDs), and MODIS-MAIAC for the tropospheric Aerosol Optical Depth (AOD), is performed. We find a bias of −0.6 × 10¹⁵ molec/cm² with a standard deviation of 1.8 × 10¹⁵ molec/cm² with respect to Sentinel-5P TROPOMI for NO₂ tropospheric VCDs and of 0.04 ± 0.08 for AOD with respect to MODIS-MAIAC data. The retrieved data show that the SPC measurement site is representative of the background pollution conditions of the Po Valley. For this reason, it is a good candidate for satellite validation and scientific studies over the Po Valley. Full article

A short comparison campaign took place at the Racibórz measurement site in May 2024 to assess the consistency of the Integrated Aerosol Monitoring Unit (IAMU), which houses three PM aerosol sensors (SPS30, OPC-N3, and OPS 3330) within a single enclosure. This assessment was supported by simultaneous measurements from two reference instruments (APS 3321 and SMP S3082), along with auxiliary observations from a ceilometer and meteorological station. To enhance particle transmission efficiency to the IAMU sensors, aerodynamic modeling of the inlet pipes was performed, accounting for particle density and diameter. The primary objective of this study was to evaluate the feasibility of using the IAMU, in conjunction with optimized inlet designs, for PM monitoring under varying ambient relative humidity and sensor temperature conditions. IAMU measurements have shown large absolute differences in PM values (exceeding one order of magnitude) with moderate (>0.54 for PM₁₀) to high (>0.82 for PM_2.5 and PM₁) temporal correlations. A calibration method was proposed, using reference instrument data and incorporating sensor temperature and air sample humidity information. The IAMU, combined with the developed calibration methodology, enabled the estimation of the aerosol growth factor, deliquescence point (RH ≈ 65%), and PM₁ hygroscopic parameter κ (0.27–0.56) for an industrial region in Poland. Full article

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument’s spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which has a distribution similar to the flat-topped multi-Gaussian function, is employed to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes resembling a Gaussian curve with a flat top could be derived, which contains a full width at half maximum (FWHM) of 1.78–1.82 nm for the AAS. The results show that the correlation is about 0.99, which indicates the usefulness of the fitting function that could better characterize the SRF of the instrument. Full article

The missing cross-channel of the lidar system aboard Aeolus (Atmospheric Laser Doppler Instrument; ALADIN) makes it impossible to obtain realistic optical products when the depolarizing atmospheric layers are probed (non-spherical particles). Additionally, it cannot provide retrievals separately for aerosol and cloud targets. To overcome these inherent deficiencies, this study aims to deliver an enhanced Aeolus aerosol product (focusing on dust), which will be utilized on aerosol data assimilation schemes coupled with dust transport models to improve Numerical Weather Prediction (NWP). For the derivation of the improved aerosol product, a series of processing steps were designed, involving the use of spaceborne retrievals/products from multi-sensors in conjunction with reanalysis numerical outputs and reference ground-based measurements. Full article

The wintertime outbreaks of Saharan dust, increasing in intensity and frequency over the last decade, have become an important component of the global dust cycle and a challenging issue in elucidating its feedback to the ongoing climate change. For their adequate monitoring and characterization, systematic multi-instrument observations and multi-aspect analyses of the distribution and properties of desert aerosols are required, covering the full duration of dust events. In this paper, we present observations of Saharan dust in the atmosphere above Sofia, Bulgaria, during a strong dust episode over the whole of Europe in February 2021, conditioned by a persistent blocking weather pattern over the Mediterranean basin, providing clear skies and constant measurement conditions. This study was accomplished using different remote sensing (lidar, satellite, and radiometric), in situ (particle analyzing), and modeling/forecasting methods and resources, using real measurements and data (re)analysis. A wide range of columnar and range/time-resolved optical, microphysical, physical, topological, and dynamical characteristics of the detected aerosols dominated by desert dust are obtained and profiled with increased accuracy and reliability by combining the applied approaches and instruments in terms of complementarity, calibration, and normalization. Vertical profiles of the aerosol/dust total and mode volume concentrations are presented and analyzed using the LIRIC-2 inversion code joining lidar and sun-photometer data. The results show that interactive combining and use of various relevant approaches, instruments, and data have a significant synergistic effect and potential for verifying and improving theoretical models aimed at complete aerosol/dust characterization. Full article

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. Full article

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality. Full article

An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods such as crop variety techniques, crop intensities, crop water production, and crop irrigation, it is necessary to examine how cropland products typically span narrow or expansive farmlands. Some of the existing challenges are processing by constructing precision-high resolution cropland-wide items of training and testing data on diverse geographical locations and safe frontiers, computing capacity, and managing vast volumes of geographical data. This analysis includes eight separate Sentinel-2 multi-spectral instruments data from 2018 to 2019 (Short-wave Infrared Imagery (SWIR 2), SWIR 1, Cirrus, the near infrared, red, green, blue, and aerosols) have been used. Pixel-based classification algorithms have been employed, and their precision is measured and scrutinized in this study. The computations and analyses have been conducted on the cloud-based Google Earth Engine computing network. Training and testing data were obtained from the Google Earth Engine map console at a high spatial 10 m resolution for this analysis. The basis of research information for testing the computer algorithms consists of 855 training samples, culminating in a manufacturing field of 200 individual validation samples measuring product accuracy. The Pakistan cropland extent map produced in this study using four state-of-the-art machine learning (ML) approaches, Random Forest, SVM, Naïve Bayes & CART shows an overall validation accuracy of 82%, 89% manufacturer accuracy, and 77% customer accuracy. Among these four machine learning algorithms, the CART algorithm overperformed the other three, with an impressive classification accuracy of 93%. Pakistan’s average cropland areas were calculated to be 370,200 m², and the cropland’s scale of goods indicated that sub-national croplands could be measured. The research offers a conceptual change in the development of cropland maps utilizing a remote sensing multi-date. Full article

To meet the demand for the aerosol detection of single-angle and multi-band polarization instrument containing short-wave infrared bands, an inversion algorithm that makes full use of multi-band intensity and polarization information is proposed based on optimal estimation theory. This method uses the polarization information in the short-wave infrared band to perform surface and atmosphere decoupling without a prior information on the surface. This obtains the initial value of the aerosol, and then it uses the scalar information to obtain the final result. Moreover, the multi-band information of the instrument is used for decoupling the surface and atmospheric information, which avoids the inversion error caused by the untimely update of the surface reflectance database and the error of spatio-temporal matching. The measured data of the Particulate Observing Scanning Polarimeter (POSP) are used to test the proposed algorithm. Firstly, to verify the effectiveness of the algorithm under different surface conditions, four regions with large geographical differences (Beijing, Hefei, Baotou, and Taiwan) are selected for aerosol optical depth (AOD) inversion, and they are compared with the aerosol robotic network (AERONET) products of the nearby stations. The validation against the AERONET products produces high correlation coefficients of 0.982, 0.986, 0.718, and 0.989, respectively, which verifies the effectiveness of the algorithm in different regions. Further, we analyzed the effectiveness of the proposed algorithm under different pollution conditions. Regions with AOD >0.7 and AOD < 0.7 are screened by using the AOD products of the Moderate-Resolution Imaging Spectroradiomete (MODIS), and the AOD of the corresponding region is inverted using POSP data. It was found to be spatially consistent with the MODIS products. The correlation coefficient and root mean square error (RMSE) in the AOD high region were 0.802 and 0.217, respectively, and 0.944 and 0.022 in the AOD low region, respectively, which verified the effectiveness of the proposed algorithm under different pollution conditions. Full article

The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found. Full article

A novel methodology is formulated and investigated on test cases for the reconstruction of complete vertical aerosol extinction profiles in which a synergy of remote, in-situ, and airborne measurements is utilized. The GRASP Open aerosol retrieval algorithm is supplied with remote LIDAR and sunphotometer data to obtain aerosol extinction profiles within the LIDAR’s operation range for coarse and fine aerosol modes separately. These are supplemented with ground-based in-situ measurements of particle size distribution that are translated to coarse and fine aerosol extinction coefficients with the use of Mie theory. UAV-based observations with optical particle counters are included to add information on vertical aerosol variability in the near-surface region. The profiles are closed with an analytical interpolation that is fine-tuned to produce continuous and smooth extinction profiles throughout the whole troposphere that are in agreement with columnar aerosol optical depth measurements. We present the possibility of reconstructing a complete and calibrated aerosol extinction profile, based on the case studies at a Central European background station. We include data-denial experiments to show that the inclusion of UAV-based measurements improves such reconstructions by providing crucial information on aerosol profiles near the ground. The proposed methodology can prove to be a potent tool for studies of aerosol concentration and evolution, especially when the majority of the pollution resides near the surface. Such conditions are prevalent in many highly industrialized regions, including central and southern Poland. Full article

Atmospheric correction of satellite optical imagery over inland waters is a key remaining challenge in aquatic remote sensing. This is due to numerous confounding factors such as the complexity of water optical properties, the surface glint, the heterogeneous nature of atmospheric aerosols, and the proximity of bright land surfaces. This combination of factors makes it difficult to retrieve accurate information about the system observed. Moreover, the impact of radiance coming from adjacent land (adjacency effects) in complex geometries further adds to this challenge, especially for small lakes. In this study, ten atmospheric correction algorithms were evaluated for high-resolution multispectral imagery of Landsat-8 Operational Land Imager and Sentinel-2 MultiSpectral Instrument using in situ optical measurements from ~300 lakes across Canada. The results of the validation show that the performance of the algorithms varies by spectral band and evaluation metrics. The dark spectrum fitting algorithm had the best performance in terms of similarity angle (spectral shape), while the neural network-based models showed the lowest errors and bias per band. However, none of the tested atmospheric correction algorithms meet a 30% retrieval accuracy target across all the visible bands, likely due to uncorrected adjacency effects. To quantify this process, three-dimensional radiative transfer simulations were performed and compared to satellite observations. These simulations show that up to 60% of the top of atmosphere reflectance in the near-infrared bands over the lake was from the adjacent lands covered with green vegetation. The significance of these adjacency effects on atmospheric correction has been analyzed qualitatively, and potential efforts to improve the atmospheric correction algorithms are discussed. Full article

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score S_tot (16.89) and for the coefficient of correlation R² (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score S_tot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary. Full article

The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land. Full article