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

Deadline for manuscript submissions: closed (31 October 2010)

Special Issue Editor

Guest Editor
Dr. Alexander A. Kokhanovsky

EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany
Website | E-Mail
Interests: remote sensing; light scattering; radiative transfer; inverse problems; atmospheric optics; retrieval of aerosol and cloud properties from spaceborne observations

Special Issue Information

Dear Colleagues,

Terrestrial atmosphere contains various gases and also condensed matter in forms of clouds and aerosol particles. Its chemical composition varies with time and highly dependent on a particular location. Also gases, atmospheric aerosol, and clouds are not independent entities but they highly interact. In particular, clouds can not form in atmosphere containing no aerosol and a large fraction of atmospheric aerosol is created in atmosphere from a gaseous phase.

An important scientific question is related to the quantification of the spatial distribution of various pollutants (e.g., particulate matter, NO2, SO2, organic compounds, soot, etc.) and also influence of changes in atmospheric composition on climate. The correspondent issues can be addressed only on the basis of observations, preferably using global satellite measurements.

The focus of this special issue is on the modern methods of atmospheric remote sensing, including remote sensing of

  • trace gases;
  • clouds;
  • atmospheric aerosol.

Dr. Alexander A. Kokhanovsky
Guest Editor

Keywords

  • atmospheric aerosol
  • clouds
  • trace gases
  • remote sensing
  • radiative transfer
  • inverse theory
  • light scattering and absorption
  • spectroscopy
  • environmental optics
  • lidar
  • radar

Published Papers (9 papers)

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Research

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Open AccessArticle Global Distribution of Cloud Top Height as Retrieved from SCIAMACHY Onboard ENVISAT Spaceborne Observations
Remote Sens. 2011, 3(5), 836-844; doi:10.3390/rs3050836
Received: 21 February 2011 / Revised: 13 April 2011 / Accepted: 19 April 2011 / Published: 27 April 2011
Cited by 8 | PDF Full-text (839 KB) | HTML Full-text | XML Full-text
Abstract
The spatial and temporal analysis of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT global cloud top height data for 2003–2006 is presented. The cloud top height is derived using a semi-analytical cloud top height retrieval algorithm based on an
[...] Read more.
The spatial and temporal analysis of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT global cloud top height data for 2003–2006 is presented. The cloud top height is derived using a semi-analytical cloud top height retrieval algorithm based on an asymptotic solution of the radiative transfer equation in the oxygen A-band. The analysis is valid for thick clouds only. As expected, clouds are higher in the equatorial region. The cloud altitudes decrease towards the Poles due to the general decrease of the troposphere height. The global average cloud top height as derived from SCIAMACHY measurements is 7.3 km. We also studied the planetary reflectivity R at 443 nm and found that the annual average is R = 0.49 ± 0.08 for the years analyzed. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission
Remote Sens. 2011, 3(2), 270-304; doi:10.3390/rs3020270
Received: 7 December 2010 / Revised: 8 February 2011 / Accepted: 9 February 2011 / Published: 14 February 2011
Cited by 79 | PDF Full-text (1753 KB) | HTML Full-text | XML Full-text
Abstract
The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2
[...] Read more.
The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for XCO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and XCO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved XCO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve XCO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the XCO2 average product for ingestion into data assimilation/inverse models. If biases in the XCO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 XCO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Seven Years’ Observation of Mid-Upper Tropospheric Methane from Atmospheric Infrared Sounder
Remote Sens. 2010, 2(11), 2509-2530; doi:10.3390/rs2112509
Received: 20 September 2010 / Revised: 28 October 2010 / Accepted: 5 November 2010 / Published: 9 November 2010
Cited by 19 | PDF Full-text (439 KB) | HTML Full-text | XML Full-text
Abstract
The Atmospheric Infrared Sounder (AIRS) on EOS/Aqua platform provides a measurement of global methane (CH4)in the mid-upper troposphere since September, 2002. As a thermal infrared sounder, the most sensitivity of AIRS to atmospheric CH4 is in the mid-upper troposphere
[...] Read more.
The Atmospheric Infrared Sounder (AIRS) on EOS/Aqua platform provides a measurement of global methane (CH4) in the mid-upper troposphere since September, 2002. As a thermal infrared sounder, the most sensitivity of AIRS to atmospheric CH4 is in the mid-upper troposphere with the degree of freedom of ~1.0. Validation of AIRS CH4 product versus thousands of aircraft profiles (convolved using the AIRS averaging kernels) demonstrates that its RMS error (RMSE) is mostly less than 1.5%, and its quality is pretty stable from 2003 to 2009. For scientific analysis of the spatial and temporal variation of mid-upper tropospheric CH4 (MUT-CH4) in the High Northern Hemisphere (HNH), it is more valuable to use the AIRS retrieved CH4 in a layer of about 100 hPa below tropopause (“Representative Layer”) than in a fixed pressure layer. Further analysis of deseasonalized time-series of AIRS CH4 in both a fixed pressure layer and the “Representative Layer” of AIRS (only for the HNH) from 2003 to 2009 indicates that, similar to the CH4 in the marine boundary layer (MBL) that was found to increase in 2007–2008, MUT-CH4 was also observed to have a recent increase but the most significant increase occurred in 2008. MUT-CH4 continued to increase in 2009, especially in the HNH. Moreover, the trend of MUT-CH4 from 2006 to 2008 is lower than the trend of CH4 in the MBL by 30–40% in both the southern hemisphere and HNH. This delay for the MUT-CH4 increase of about one year than CH4 in the MBL as well as the smaller increase trend for MUT-CH4 suggest that surface emission is likely a major driver for the recent CH4 increase. It is also found that the seasonal cycle of MUT-CH4 is different from CH4 in the MBL due to the impact of transport, in addition to the surface emission and the photochemical loss. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Dust and Smoke Detection for Multi-Channel Imagers
Remote Sens. 2010, 2(10), 2347-2368; doi:10.3390/rs2102347
Received: 27 August 2010 / Revised: 28 September 2010 / Accepted: 30 September 2010 / Published: 11 October 2010
Cited by 28 | PDF Full-text (2321 KB) | HTML Full-text | XML Full-text
Abstract
A detection algorithm of dust and smoke for application to satellite multi-channel imagers is introduced in this paper. The algorithm is simple and solely based on spectral and spatial threshold tests along with some uniformity texture. Detailed examinations of the threshold tests are
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A detection algorithm of dust and smoke for application to satellite multi-channel imagers is introduced in this paper. The algorithm is simple and solely based on spectral and spatial threshold tests along with some uniformity texture. Detailed examinations of the threshold tests are performed along with explanations of the physical basis. The detection is performed efficiently at the pixel level and output is in the form of an index (or flag): 0 (no dust/smoke) and 1 (dust/smoke). The detection algorithm is implemented sequentially and designed to run on segments of data instead of pixel by pixel for efficient processing. MODIS observations are used to test the operation and performance of the algorithm. The algorithm can capture heavy dust and smoke plumes very well over both land and ocean and therefore is used as a global detection algorithm. The method can be applied to any multi-channel imagers with channels at (or close to) 0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 μm (such as current EOS/MODIS and future JPSS/VIIRS and GOES-R/ABI) for the detection of dust and smoke. It can be used to operationally monitor the outbreak and dispersion of dust storms and smoke plumes that are potentially hazardous to our environment and impact climate. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Figures

Open AccessArticle Interferometric vs Spectral IASI Radiances: Effective Data-Reduction Approaches for the Satellite Sounding of Atmospheric Thermodynamical Parameters
Remote Sens. 2010, 2(10), 2323-2346; doi:10.3390/rs2102323
Received: 13 August 2010 / Revised: 29 August 2010 / Accepted: 25 September 2010 / Published: 30 September 2010
Cited by 11 | PDF Full-text (477 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: Two data-reduction approaches for the Infrared Atmospheric Sounder Interferometer satellite instrument are discussed and compared. The approaches are intended for the purpose of devising and implementing fast near real time retrievals of atmospheric thermodynamical parameters. One approach is based on the usual
[...] Read more.
Abstract: Two data-reduction approaches for the Infrared Atmospheric Sounder Interferometer satellite instrument are discussed and compared. The approaches are intended for the purpose of devising and implementing fast near real time retrievals of atmospheric thermodynamical parameters. One approach is based on the usual selection of sparse channels or portions of the spectrum. This approach may preserve the spectral resolution, but at the expense of the spectral coverage. The second approach considers a suitable truncation of the interferogram (the Fourier transform of the spectrum) at points below the nominal maximum optical path difference. This second approach is consistent with the Shannon-Whittaker sampling theorem, preserves the full spectral coverage, but at the expense of the spectral resolution. While the first data-reduction acts within the spectraldomain, the second can be performed within the interferogram domain and without any specific need to go back to the spectral domain for the purpose of retrieval. To assess the impact of these two different data-reduction strategies on retrieval of atmospheric parameters, we have used a statistical retrieval algorithm for skin temperature, temperature, water vapour and ozone profiles. The use of this retrieval algorithm is mostly intended for illustrative purposes and the user could choose a different inverse strategy. In fact, the interferogram-based data-reduction strategy is generic and independent of any inverse algorithm. It will be also shown that this strategy yields subset of interferometric radiances, which are less sensitive to potential interfering effects such as those possibly introduced by the day-night cycle (e.g., the solar component, and spectroscopic effect induced by sun energy) and unknown trace gases variability. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China
Remote Sens. 2010, 2(9), 2136-2147; doi:10.3390/rs2092136
Received: 20 July 2010 / Revised: 30 August 2010 / Accepted: 5 September 2010 / Published: 13 September 2010
Cited by 7 | PDF Full-text (187 KB) | HTML Full-text | XML Full-text
Abstract
Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, temperature,
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Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, temperature, relative humidity, and wind velocity) on predicting air pollution from MODIS AOT data for the city of Nanjing, China. It is found that PM10 (particulate matter with a diameter Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessCommunication Determination of Backscatter-Extinction Coefficient Ratio for LIDAR-Retrieved Aerosol Optical Depth Based on Sunphotometer Data
Remote Sens. 2010, 2(9), 2127-2135; doi:10.3390/rs2092127
Received: 5 August 2010 / Revised: 6 September 2010 / Accepted: 6 September 2010 / Published: 9 September 2010
Cited by 2 | PDF Full-text (622 KB) | HTML Full-text | XML Full-text
Abstract
Backscattered power data from the Doppler LIght Detection And Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA) could be used to obtain the extinction coefficient of the troposphere by combining with the meteorological optical range (MOR) data from the nearby forward
[...] Read more.
Backscattered power data from the Doppler LIght Detection And Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA) could be used to obtain the extinction coefficient of the troposphere by combining with the meteorological optical range (MOR) data from the nearby forward scatter sensor. The Range-height Indicator (RHI) scan of the LIDAR is then utilized to derive the vertical profile of extinction coefficient, which is integrated with height to obtain the aerosol optical depth (AOD). In the retrieval of extinction coefficient profile, there is a power exponent of unknown value relating the backscattered power and the extinction coefficient. This exponent (called the backscatter-extinction coefficient ratio) depends on the optical properties of the aerosol in the air, and is normally assumed to be 1. In the present study, the value of this ratio is established by comparing the AOD measurements by a hand-held sunphotometer and the LIDAR-based AOD estimate in one winter (October 2008 to January 2009), which is the season with the largest number of haze episodes, and one summer-winter-spring period of the following year (July 2009 to May 2010) at HKIA. It is found to be about 1.4. The sensitivity of extinction coefficient profile to the value of the ratio is also examined for two cases in the study period, one good visibility day and one hazy day. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Application of MODIS Products for Air Quality Studies Over Southeastern Italy
Remote Sens. 2010, 2(7), 1767-1796; doi:10.3390/rs2071767
Received: 4 June 2010 / Revised: 24 June 2010 / Accepted: 5 July 2010 / Published: 14 July 2010
Cited by 12 | PDF Full-text (572 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol optical thicknesses (AOTs) by the MODerate Resolution Imaging Spetroradiometer (MODIS) on-board Aqua and Terra satellites, and ground-based measurements of PM10 mass concentrations, collected over three years (2006–2008) at two suburban sites which are 20 km apart, are correlated to assess the use
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Aerosol optical thicknesses (AOTs) by the MODerate Resolution Imaging Spetroradiometer (MODIS) on-board Aqua and Terra satellites, and ground-based measurements of PM10 mass concentrations, collected over three years (2006–2008) at two suburban sites which are 20 km apart, are correlated to assess the use of satellite data for regional air quality studies over Southeastern Italy, in the central Mediterranean. Due to the geographical location, this area is affected by local and long-range transported marine, desert (from Sahara), and anthropogenic (from continental Europe) aerosols. 24-hour averaged PM10 mass concentrations span the 1.6–152 µg/m 3 range. Yearly means of PM10 mass concentrations decrease from 2006 to 2008 and vary within the 26–36 µg/m 3 range. Daily mean values of MODIS AOTs vary up to 0.8 at 550 nm, while yearly means span the 0.15–0.17 range. A first assessment of the regression relationship between daily averaged PM10 mass concentrations and MODIS-AOTs shows that linear correlation coefficients ( R ) vary within the 0.20–0.35 range and are affected by the sampling year and the site location. The PM10-AOT correlation becomes stronger (0.34 ≤ R ≤ 0.57) when the analysis is restricted to clear-sky MODIS measurements. The cloud screening procedure adopted within the AERONET network is used in this study to select clear-sky MODIS measurements, since it allows obtaining larger R values than the ones obtained using the cloud fraction MODIS product to select clear-sky MODIS measurements. Using three years of clear-sky measurements to estimate PM10 mass concentrations from MODIS-AOTs, the empirical relation we have found is: PM10 ( m g/m 3 ) = 25 ( m g/m 3 ) + 65 ( m g/m 3 ) × AOT. Over 80% of the differences between the measured and satellite estimated PM10 mass concentrations over the three years are within ±1 standard deviation of the yearly means. The differences between yearly means of calculated and measured mass concentrations that are close to zero in 2006, increase up to 4 m g/m 3 at one siteand 8 m g/m 3 at the other site in 2008. The PM10 mass concentration decrease from 2006 to 2008 contributes to this last result. Our results demonstrate the potential of MODIS data for deriving indirect estimates of PM10 over Southeastern Italy. It is also shown that a stronger relationship between PM10 and MODIS-AOTs is obtained when the AOT is divided by the product of the mixing layer height with the ground wind speed and the analysis restricted to clear sky MODIS measurements. However, we have found that the stronger correlation (0.52 ≤ R ≤ 0.66) does not allow a significant improvement of MODIS-based-estimates of PM10 mass concentrations. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)

Review

Jump to: Research

Open AccessReview Detection and Measurement of Snowfall from Space
Remote Sens. 2011, 3(1), 145-166; doi:10.3390/rs3010145
Received: 21 November 2010 / Revised: 5 January 2011 / Accepted: 5 January 2011 / Published: 11 January 2011
Cited by 12 | PDF Full-text (708 KB) | HTML Full-text | XML Full-text
Abstract
Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize
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Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)

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