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Aerosol and Cloud Remote Sensing
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Aerosol and Cloud Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 September 2015) | Viewed by 126574

Special Issue Editor

Dr. Alexander Kokhanovsky

E-Mail Website
Guest Editor
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleauges,

Remote sensing aerosols, clouds, and aerosol–cloud interactions is a hot topic of modern atmospheric remote sensing studies. Both aerosols and clouds influence climate and weather. Their properties could change with time, leading to planetary energy imbalance on a global scale. For instance, recent studies show that there is a change in aerosol load (decrease/increase) on a decadal scale in several places worldwide. The same is true for cloud properties including cloud altitudes.

Optical and thermal infrared remote sensing of aerosols and clouds is a mature research field with a long history. Great progress has been achieved (especially in the last 40 years) using both ground-based and satellite instrumentation. The main parameters of interest are aerosol/cloud optical and microphysical properties, concentration, and aerosol/cloud geometrical characteristics (e.g., the altitudes, thickness and spatial extent). Aerosol–cloud interactions have been heavily studied as well; however, more research is needed in this area. In particular, new fast codes for the solution of the inverse problem, based on the multi-angular light intensity and polarization measurements, must be developed and applied to the satellite measurements on a global scale including real-time operational retrievals. This is of special importance for studies of natural hazards such as dust storms, hurricanes, wild fires, volcanic explosions and technological catastrophes with ejection of aerosol particles (e.g., soot) in the atmosphere at various heights.

This special issue is aimed at the presentation of recent results in ground-based and satellite remote sensing of aerosols and clouds, including validation of retrievals based on independent measurements.

Dr. Alexander A. Kokhanovsky
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aerosol remote sensing
  • cloud remote sensing
  • radiative transfer
  • aerosol optical thickness
  • cloud optical thickness
  • atmospheric pollution
  • aerosol–cloud interactions
  • climate change
  • remote sensing
  • precipitation
  • light scattering
  • aerosol microphysics
  • cloud microphysics
  • aerosol vertical profile
  • cloud top height

Published Papers (15 papers)

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3601 KiB  
Article
National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD
by Wei You, Zengliang Zang, Lifeng Zhang, Yi Li, Xiaobin Pan and Weiqi Wang
Remote Sens. 2016, 8(3), 184; https://doi.org/10.3390/rs8030184 - 26 Feb 2016
Cited by 133 | Viewed by 10222
Abstract
High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. [...] Read more.
High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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5597 KiB  
Article
Vertical Profiling of Volcanic Ash from the 2011 Puyehue Cordón Caulle Eruption Using IASI
by Kwinten Maes, Sophie Vandenbussche, Lars Klüser, Nicolas Kumps and Martine De Mazière
Remote Sens. 2016, 8(2), 103; https://doi.org/10.3390/rs8020103 - 29 Jan 2016
Cited by 8 | Viewed by 6715
Abstract
Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while [...] Read more.
Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while of high importance for both applications mentioned. Here, we study the sensitivity of the thermal infrared spectral range to the altitude distribution of volcanic ash, based on similar work that was undertaken on mineral dust. We use measurements by the Infrared Atmospheric Sounding Interferometer (IASI) instruments onboard the MetOp satellite series. The retrieval method that we develop for the ash vertical profile is based on the optimal estimation formalism. This method is applied to study the eruption of the Chilean volcano Puyehue, which started on the 4th of June 2011. The retrieved profiles agree reasonably well with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) measurements, and our results generally agree with literature studies of the same eruption. The retrieval strategy presented here therefore is very promising for improving our knowledge of the vertical distribution of volcanic ash and obtaining a global 3D ash distribution twice a day. Future improvements of our retrieval strategy are also discussed. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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3689 KiB  
Article
Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection
by Li Yi, Boris Thies, Suping Zhang, Xiaomeng Shi and Jörg Bendix
Remote Sens. 2016, 8(1), 8; https://doi.org/10.3390/rs8010008 - 22 Dec 2015
Cited by 15 | Viewed by 6688
Abstract
Operational nowcasting techniques for sea fog over the Yellow Sea rely on data from weather satellites because ground-based observations are hardly available. While there are several algorithms for detecting low stratus (LST) that are applicable to geostationary weather satellite data, sea fog retrieval [...] Read more.
Operational nowcasting techniques for sea fog over the Yellow Sea rely on data from weather satellites because ground-based observations are hardly available. While there are several algorithms for detecting low stratus (LST) that are applicable to geostationary weather satellite data, sea fog retrieval is more complicated. These schemes mostly need ancillary data such as Cloud Optical Thickness (COT) and Droplet Effective Radius (DER). To retrieve the necessary parameters for sea fog detection over the Yellow Sea, the Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM) scheme developed by Kawamoto et al. (2001) was adapted to the Japanese Multifunctional Transport Satellites (MTSAT) system-Japanese Advanced Meteorological Imager (JAMI). COT and DER values were then retrieved for 64 cases over the Yellow Sea (= 85,000 LST pixels) and compared with the COT and DER products from the MYD06/MOD06, CAPCOM-MODIS (Moderate Resolution Imaging Spectroradiometer) and CloudSat (cloud radar). Results showed that the COT and DER values retrieved from JAMI were satisfactory. The MTSAT-2 JAMI data delivered better COT values than the MTSAT-1R JAMI data, due to the re-calibration of MTSAT-2 JAMI’s visible (VIS) band in 2011. Similarly, improvements were seen in DER retrieval, even though the VIS re-calibration primarily affects COT retrieval. By comparing the difference in stratus thickness calculated by MTSAT-1R and MTSAT-2, the COT and DER retrieved from MTSAT-2 JAMI can be used in ground fog retrieval schemes. These values exhibit less bias, especially in cases involving high cloud top and thin cloud thickness. Both the COT and DER retrievals from MTSAT-2 JAMI offer potential as reliable parameters for Yellow Sea fog detection. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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417 KiB  
Article
Validation of Long-Term Global Aerosol Climatology Project Optical Thickness Retrievals Using AERONET and MODIS Data
by Igor V. Geogdzhayev and Michael I. Mishchenko
Remote Sens. 2015, 7(10), 12588-12605; https://doi.org/10.3390/rs71012588 - 24 Sep 2015
Cited by 6 | Viewed by 5441
Abstract
A comprehensive set of monthly mean aerosol optical thickness (AOT) data from coastal and island AErosol RObotic NETwork (AERONET) stations is used to evaluate Global Aerosol Climatology Project (GACP) retrievals for the period 1995–2009 during which contemporaneous GACP and AERONET data were available. [...] Read more.
A comprehensive set of monthly mean aerosol optical thickness (AOT) data from coastal and island AErosol RObotic NETwork (AERONET) stations is used to evaluate Global Aerosol Climatology Project (GACP) retrievals for the period 1995–2009 during which contemporaneous GACP and AERONET data were available. To put the GACP performance in broader perspective, we also compare AERONET and MODerate resolution Imaging Spectroradiometer (MODIS) Aqua level-2 data for 2003–2009 using the same methodology. We find that a large mismatch in geographic coverage exists between the satellite and ground-based datasets, with very limited AERONET coverage of open-ocean areas. This is especially true of GACP because of the smaller number of AERONET stations at the early stages of the network development. Monthly mean AOTs from the two over-the-ocean satellite datasets are well-correlated with the ground-based values, the correlation coefficients being 0.81–0.85 for GACP and 0.74–0.79 for MODIS. Regression analyses demonstrate that the GACP mean AOTs are approximately 17%–27% lower than the AERONET values on average, while the MODIS mean AOTs are 5%–25% higher. The regression coefficients are highly dependent on the weighting assumptions (e.g., on the measure of aerosol variability) as well as on the set of AERONET stations used for comparison. Comparison of over-the-land and over-the-ocean MODIS monthly mean AOTs in the vicinity of coastal AERONET stations reveals a significant bias. This may indicate that aerosol amounts in coastal locations can differ significantly from those in adjacent open-ocean areas. Furthermore, the color of coastal waters and peculiarities of coastline meteorological conditions may introduce biases in the GACP AOT retrievals. We conclude that the GACP and MODIS over-the-ocean retrieval algorithms show similar ranges of discrepancy when compared to available coastal and island AERONET stations. The factors mentioned above may limit the performance of the validation procedure and cause us to caution against a direct extrapolation of the presented validation results to the entirety of the GACP dataset. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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3248 KiB  
Article
Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis
by Yang Shen, Yong Wang, Haitao Lv and Jiang Qian
Remote Sens. 2015, 7(9), 11481-11500; https://doi.org/10.3390/rs70911481 - 09 Sep 2015
Cited by 39 | Viewed by 10871
Abstract
An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a [...] Read more.
An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a cloud-free image, changes of histogram curves validated the algorithm. Scatterplots were generated and linear regression performed for the reflectance values of each band before and after the algorithm, and compared to those of the reference image. Band-by-band, results in cloud removal were acceptable. The algorithm had little effect on pixels in cloud-free areas after the analyses of histograms, scatterplots, and linear regression equations. Finally, the algorithm was applied to various land use and land cover types and cloud conditions, and to a full Landsat-8 scene yielding satisfactory results efficiently. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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1228 KiB  
Article
A 10-Year Cloud Fraction Climatology of Liquid Water Clouds over Bern Observed by a Ground-Based Microwave Radiometer
by Federico Cossu, Klemens Hocke and Christian Mätzler
Remote Sens. 2015, 7(6), 7768-7784; https://doi.org/10.3390/rs70607768 - 11 Jun 2015
Cited by 14 | Viewed by 5361
Abstract
Cloud fraction (CF) is known as the dominant modulator of Earth’s radiative fluxes. Ground-based CF observations are useful to characterize the cloudiness of a specific site and are valuable for comparison with satellite observations and numerical models. We present for the first time [...] Read more.
Cloud fraction (CF) is known as the dominant modulator of Earth’s radiative fluxes. Ground-based CF observations are useful to characterize the cloudiness of a specific site and are valuable for comparison with satellite observations and numerical models. We present for the first time CF statistics (relative to liquid clouds only) for Bern, Switzerland, derived from the observations of a ground-based microwave radiometer. CF is derived with a new method involving the analysis of the integrated liquid water distribution measured by the radiometer. The 10-year analyzed period (2004–2013) allowed us to compute a CF climatology for Bern, showing a maximum CF of 60.9% in winter and a minimum CF of 42.0% in summer. The CF monthly anomalies are identified with respect to the climatological mean values, and they are confirmed through MeteoSwiss yearly climatological bulletins. The CF monthly mean variations are similar to the observations taken at another Swiss location, Payerne, suggesting a large-scale correlation between different sites on the Swiss Plateau. A CF diurnal cycle is also computed, and large intraseasonal variations are found. The overall mean CF diurnal cycle, however, shows a typical sinusoidal cycle, with higher values in the morning and lower values in the afternoon. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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2811 KiB  
Article
Seasonal Variations of the Relative Optical Air Mass Function for Background Aerosol and Thin Cirrus Clouds at Arctic and Antarctic Sites
by Claudio Tomasi, Boyan H. Petkov, Mauro Mazzola, Christoph Ritter, Alcide G. Di Sarra, Tatiana Di Iorio and Massimo Del Guasta
Remote Sens. 2015, 7(6), 7157-7180; https://doi.org/10.3390/rs70607157 - 01 Jun 2015
Cited by 7 | Viewed by 6148
Abstract
New calculations of the relative optical air mass function are made over the 0°–87° range of apparent solar zenith angle θ, for various vertical profiles of background aerosol, diamond dust and thin cirrus cloud particle extinction coefficient in the Arctic and Antarctic [...] Read more.
New calculations of the relative optical air mass function are made over the 0°–87° range of apparent solar zenith angle θ, for various vertical profiles of background aerosol, diamond dust and thin cirrus cloud particle extinction coefficient in the Arctic and Antarctic atmospheres. The calculations were carried out by following the Tomasi and Petkov (2014) procedure, in which the above-mentioned vertical profiles derived from lidar observations were used as weighting functions. Different sets of lidar measurements were examined, recorded using: (i) the Koldewey-Aerosol-Raman Lidar (KARL) system (AWI, Germany) at Ny-Ålesund (Spitsbergen, Svalbard) in January, April, July and October 2013; (ii) the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite-based sensor over Barrow (Alaska), Eureka (Nunavut, Canada) and Sodankylä (northern Finland), and Neumayer III, Mario Zucchelli and Mirny coastal stations in Antarctica in the local summer months of the last two years; (iii) the National Institute of Optics (INO), National Council of Research (CNR) Antarctic lidar at Dome C on the Antarctic Plateau for a typical “diamond dust” case; and (iv) the KARL lidar at Ny-Ålesund and the University of Rome/National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) lidar at Thule (northwestern Greenland) for some cirrus cloud layers in the middle and upper troposphere. The relative optical air mass calculations are compared with those obtained by Tomasi and Petkov (2014) to define the seasonal changes produced by aerosol particles, diamond dust and cirrus clouds. The results indicate that the corresponding air mass functions generally decrease as angle θ increases with rates that are proportional to the increase in the pure aerosol, diamond dust and cirrus cloud particle optical thickness. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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632 KiB  
Article
Application of the Ultraviolet Scanning Elastic Backscatter LiDAR for the Investigation of Aerosol Variability
by Fei Gao, Samo Stanič, Klemen Bergant, Ying Li, Songhui Li, Dengxin Hua and Longlong Wang
Remote Sens. 2015, 7(5), 6320-6335; https://doi.org/10.3390/rs70506320 - 20 May 2015
Cited by 8 | Viewed by 6435
Abstract
In order to investigate the aerosol variability over the southwest region of Slovenia, an ultraviolet scanning elastic backscatter LiDAR was utilized to make the vertical scan for atmospheric probing. With the assumption of horizontal atmospheric homogeneity, aerosol optical variables were retrieved from the [...] Read more.
In order to investigate the aerosol variability over the southwest region of Slovenia, an ultraviolet scanning elastic backscatter LiDAR was utilized to make the vertical scan for atmospheric probing. With the assumption of horizontal atmospheric homogeneity, aerosol optical variables were retrieved from the horizontal pixel data points of two-dimensional range-height-indicator (RHI) diagrams by using a multiangle retrieval method, in which optical depth is defined as the slope of the resulting linear function when height is kept constant. To make the data retrieval feasible and precise, a series of key procedures complemented the data processing, including construction of the RHI diagram, correction of Rayleigh scattering, assessment of horizontal atmospheric homogeneity and retrieval of aerosol optical variables. The measurement example demonstrated the feasibility of the ultraviolet scanning elastic backscatter LiDAR in the applications of the retrieval of aerosol extinction and determination of the atmospheric boundary layer height. Three months’ data combined with the modeling of air flow trajectories using Hybrid Single Particle Lagrangian Integrated Trajectory Model were analyzed to investigate aerosol variability. The average value of aerosol extinction with the presence of land-based air masses from the European continent was found to be two-times larger than that influenced by marine aerosols from the Mediterranean or Adriatic Sea. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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15104 KiB  
Article
Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product
by Tamás Várnai and Alexander Marshak
Remote Sens. 2015, 7(5), 5283-5299; https://doi.org/10.3390/rs70505283 - 27 Apr 2015
Cited by 21 | Viewed by 6217
Abstract
Characterizing the way satellite-based aerosol statistics change near clouds is important for better understanding both aerosol-cloud interactions and aerosol direct radiative forcing. This study focuses on the question of whether the observed near-cloud increases in aerosol optical thickness and particle size may be [...] Read more.
Characterizing the way satellite-based aerosol statistics change near clouds is important for better understanding both aerosol-cloud interactions and aerosol direct radiative forcing. This study focuses on the question of whether the observed near-cloud increases in aerosol optical thickness and particle size may be explained by a combination of two factors: (i) Near-cloud data coming from areas with higher cloud fractions than far-from-cloud data and (ii) Cloud fraction being correlated with aerosol optical thickness and particle size. This question is addressed through a statistical analysis of aerosol parameters included in the MODIS (MODerate resolution Imaging Spectroradiometer) ocean color product. Results from ten Septembers (2002–2011) over part of the northeast Atlantic Ocean confirm that the combination of these two factors working together explains a significant but not dominant part (in our case, 15%–30%) of mean optical thickness changes near clouds. Overall, the findings show that cloud fraction plays a large role in shaping the way aerosol statistics change with distance to clouds. This implies that both cloud fraction and distance to clouds are important to consider when aerosol-cloud interactions or aerosol direct radiative effects are examined in satellite or modeling studies. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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3347 KiB  
Article
Where Aerosols Become Clouds—Potential for Global Analysis Based on CALIPSO Data
by Julia Fuchs and Jan Cermak
Remote Sens. 2015, 7(4), 4178-4190; https://doi.org/10.3390/rs70404178 - 08 Apr 2015
Cited by 13 | Viewed by 6497
Abstract
This study evaluates the potential to determine the global distribution of hydrated aerosols based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data products. Knowledge of hydrated aerosol global distribution is of high relevance in the study of the radiative impact of [...] Read more.
This study evaluates the potential to determine the global distribution of hydrated aerosols based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data products. Knowledge of hydrated aerosol global distribution is of high relevance in the study of the radiative impact of aerosol-cloud interactions on Earth’s climate. The cloud-aerosol discrimination (CAD) score of the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) instrument on the CALIPSO satellite separates aerosols and clouds according to the probability density functions (PDFs) of attenuated backscatter, total color ratio, volume depolarization ratio, altitude and latitude. The pixels that CAD fails to identify as either cloud or aerosol are used here to pinpoint the occurrence of hydrated aerosols and to globally quantify their relative frequency using data of August from 2006 to 2013. Atmospheric features in this no-confidence range mostly match with aerosol PDFs and imply an early hydration state of aerosols. Their strong occurrence during August above the South-East Atlantic and below an altitude of 4 km coincides with the biomass burning season in southern Africa and South America. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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3330 KiB  
Article
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images
by Olivier Hagolle, Mireille Huc, David Villa Pascual and Gerard Dedieu
Remote Sens. 2015, 7(3), 2668-2691; https://doi.org/10.3390/rs70302668 - 09 Mar 2015
Cited by 222 | Viewed by 14851
Abstract
The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of [...] Read more.
The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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781 KiB  
Article
Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking
by Alireza Taravat, Simon Proud, Simone Peronaci, Fabio Del Frate and Natascha Oppelt
Remote Sens. 2015, 7(2), 1529-1539; https://doi.org/10.3390/rs70201529 - 02 Feb 2015
Cited by 51 | Viewed by 11336
Abstract
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one [...] Read more.
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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3273 KiB  
Article
The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics
by Jędrzej S. Bojanowski, Reto Stöckli, Anke Tetzlaff and Heike Kunz
Remote Sens. 2014, 6(12), 12866-12884; https://doi.org/10.3390/rs61212866 - 22 Dec 2014
Cited by 11 | Viewed by 8211
Abstract
Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy [...] Read more.
Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK) by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true) HK from HK affected by the collocation differences, which significantly (t-test p < 0.01) improves the validation results. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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9126 KiB  
Article
Inversion of Aerosol Optical Depth Based on the CCD and IRS Sensors on the HJ-1 Satellites
by Yang Zhang, Zhihong Liu, Yongqian Wang, Zhixiang Ye and Lu Leng
Remote Sens. 2014, 6(9), 8760-8778; https://doi.org/10.3390/rs6098760 - 19 Sep 2014
Cited by 10 | Viewed by 6387
Abstract
To perform a high-resolution aerosol optical depth (AOD) inversion from the HJ-1 satellites, a dark pixel algorithm utilizing the HJ-1 satellite data was developed based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) algorithm. By analyzing the relationship between the apparent reflectance from the 1.65 [...] Read more.
To perform a high-resolution aerosol optical depth (AOD) inversion from the HJ-1 satellites, a dark pixel algorithm utilizing the HJ-1 satellite data was developed based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) algorithm. By analyzing the relationship between the apparent reflectance from the 1.65 μm and 2.1 μm channels of MODIS, a method for estimating albedo using the 1.65 μm channel data of the HJ-1 satellites was established, and a high-resolution AOD inversion in the Chengdu region based on the HJ-1 satellite was completed. A comparison of the inversion results with CE318 measured data produced a correlation of 0.957, respectively, with an absolute error of 0.106. An analysis of the AOD inversion results from different aerosol models showed that the rural aerosol model was suitable as a general model for establishing an aerosol inversion look-up table for the Chengdu region. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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7039 KiB  
Technical Note
Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images
by Lin Sun, Jing Wei, Muhammad Bilal, Xinpeng Tian, Chen Jia, Yamin Guo and Xueting Mi
Remote Sens. 2016, 8(1), 23; https://doi.org/10.3390/rs8010023 - 31 Dec 2015
Cited by 100 | Viewed by 13808
Abstract
Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as [...] Read more.
Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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