scholarly journals Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

2017 ◽  
Vol 9 (3) ◽  
pp. 217 ◽  
Author(s):  
Hang Yang ◽  
Lifu Zhang ◽  
Cindy Ong ◽  
Andrew Rodger ◽  
Jia Liu ◽  
...  
Keyword(s):  
Water Vapor ◽  
Optical Thickness ◽  
Aerosol Optical Thickness ◽  
Surface Reflectance ◽  
Hyperspectral Sensors ◽  
Columnar Water Vapor

Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance

2021 ◽  
Author(s):  
Marta Luffarelli ◽  
Yves Govaerts
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Solution Space ◽  
Single Scattering ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Earth System ◽  
System Components

<p>The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.</p><p> </p><p>Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.</p><p> </p><p>The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.  </p><p> </p><p>Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.</p>

scholarly journals Variations of aerosol optical thickness, water vapor and Ångström parameter over the semi-arid zone of Tajikistan

2019 ◽  
Vol 99 ◽  
pp. 03004
Author(s):  
Sabur F. Abdullaev ◽  
Vladimir A. Maslov ◽  
Bahron I. Nazarov ◽  
Nasridin Kh. Minikulov ◽  
Abdugani M. Djuraev
Keyword(s):  
Water Vapor ◽  
Moisture Content ◽  
Optical Thickness ◽  
Arid Zone ◽  
Aerosol Optical Thickness ◽  
Statistical Characteristics ◽  
Atmospheric Parameters ◽  
Annual Variations ◽  
Semi Arid

The article describes the results of measurements that were carried out systematically during 2010-2017 at the AERONET station in Dushanbe. The data on the changes of aerosol optical thickness (AOT), moisture content and Ångström parameter are described. The seasonal and annual variations of these quantities were analyzed. The regularities of repeatability histograms as statistical characteristics of the atmospheric parameters were studied.

Improved retrieval of aerosol optical thickness from MODIS measurements through derived surface reflectance over Nanjing, China

Tellus B ◽  
2011 ◽  
Vol 63 (5) ◽  
pp. 952-958 ◽  
Author(s):  
Yong Zha ◽  
Qiao Wang ◽  
Jie Yuan ◽  
Jay Gao ◽  
Jianjun Jiang ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Aerosol Optical Thickness ◽  
Surface Reflectance

scholarly journals Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS

2010 ◽  
Vol 3 (3) ◽  
pp. 2107-2164 ◽  
Author(s):  
W. von Hoyningen-Huene ◽  
J. Yoon ◽  
M. Vountas ◽  
L. G. Istomina ◽  
G. Rohen ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Vegetation Index ◽  
Single Scattering ◽  
Aerosol Optical Thickness ◽  
Visible Range ◽  
Surface Reflectance ◽  
Spectral Determination ◽  
Aerosol Retrieval ◽  
Phase Functions

Abstract. For the determination of aerosol optical thickness (AOT) Bremen AErosol Retrieval (BAER) has been developed. Method and main influences on the aerosol retrieval are described together with validation and results. The retrieval separates the spectral aerosol reflectance from surface and Rayleigh path reflectance for the shortwave range of the measured spectrum of top-of-atmosphere reflectance less than 0.670 μm. The advantage of MERIS (Medium Resolution Imaging Spectrometer on ENVISAT) and SeaWiFS (Sea viewing Wide Fiels Sensor on OrbView-2) observations are the existence of several spectral channels in the blue and visible range enabling the spectral determination of AOT in 7 (or 6) channels (0.412–0.670 μm) and additionally channels in the NIR, which can be used to characterize the surface properties. A dynamical spectral surface reflectance model for different surface types is used to obtain the spectral surface reflectance for this separation. Normalized differential vegetation index (NDVI), taken from the satellite observations, is the model input. Further surface BRDF is considered by the Raman-Pinty-Verstraete (RPV) model. Spectral AOT is obtained from aerosol reflectance using look-up-tables, obtained from radiative transfer calculations with given aerosol phase functions and single scattering albedos either from aerosol models, given by OPAC or from experimental campaigns. Validations of the obtained AOT retrieval results with AERONET data over Europe gave a preference for experimental phase functions derived from almucantar measurements. Finally long-term observations of SeaWiFS have been investigated for trends in AOT.

Deriving Optimum Visibility Range and Aerosol Loading from Urban Atmospheric Model in Malaysia

2012 ◽  
Vol 518-523 ◽  
pp. 5784-5787
Author(s):  
Wan Noni Afida Ab Manan ◽  
Arnis Asmat ◽  
Noordin Ahmad
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Atmospheric Model ◽  
Surface Reflectance ◽  
Atmospheric Parameters ◽  
Kuala Lumpur ◽  
Optimum Range ◽  
Aerosol Loading ◽  
Visibility Range

Visibility, aerosol optical thickness and water vapor are important atmospheric parameters that vary in space and time. Using radiative transfer algorithm to derive surface reflectance from imaging these values would be critical to be assigned. This study will investigate the optimum range of visibility and aerosol loading in Malaysia deriving from atmospheric model. Urban atmospheric model was performed into two major cities in Malaysia to represent for ideal tropical climate. The study found that the farthest visibility range at 50km,the aerosol loading was low and the shortest range at 10 km was contain high aerosol loading. Relatively, aerosol loading estimation is higher at close-shore city (Penang) than inland city (Kuala Lumpur).

scholarly journals Evaluation of BAER surface model for aerosol optical thickness retrieval over land surface

2012 ◽  
Vol 5 (2) ◽  
pp. 2645-2679
Author(s):  
Y. S. Chiang ◽  
W. von Hoyningen-Huene ◽  
K. S. Chen ◽  
A. Ladstätter-Weißenmayer ◽  
J. P. Burrows
Keyword(s):  
Land Cover ◽  
Linear Model ◽  
Optical Thickness ◽  
Linear Method ◽  
Aerosol Optical Thickness ◽  
Surface Model ◽  
Surface Reflectance ◽  
Land Cover Type ◽  
Cover Type ◽  
Vegetation Fraction

Abstract. Estimation of surface reflectance is essential for an accurate retrieval of aerosol optical thickness (AOT) by satellite remote sensing approach. Due to the variability of surface reflectance over land surfaces, a surface model is required to take into account the crucial factor controlling this variability. In the present study, we attempted to simulate surface reflectance in the short-wave channels with two methods, namely the land cover type dependent method and a two-source linear model. In the two-source linear model, we assumed that the spectral property can be described by a mixture of vegetated and non-vegetated area, and both the normalized difference vegetation index (NDVI), and the vegetation continuous field (VCF) was applied to summarize this surface characteristic. By comparing our estimation with surface reflectance data derived from Moderate Resolution Imaging Spectroradiometer (MODIS), it indicated that the land cover type approach did not provide a better estimation because of inhomogeneous land cover pattern and the mixing pixel properties. For the two-source linear method, the study suggested that the use of NDVI as parameterization for vegetation fraction can reflect the spectral behavior of shortwave surface reflectance, despite of some deviation due to the averaging characteristics in our linear combination process. A channel-dependent offset and scalar factor could enhance reflectance estimation and further improve AOT retrieval by the current Bremen AErosol Retrieval (BAER) approach.

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