Relevant bibliographies by topics / Atmospheric remote sensing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Atmospheric remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Atmospheric remote sensing"

1

Bates, B. "Atmospheric Ultraviolet Remote Sensing." Journal of Modern Optics 40, no. 6 (1993): 1191. http://dx.doi.org/10.1080/09500349314551271.

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Rees, M. H. "Atmospheric Ultraviolet Remote Sensing." Journal of Atmospheric and Terrestrial Physics 56, no. 11 (1994): 1530–31. http://dx.doi.org/10.1016/0021-9169(94)90122-8.

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Liang, Tianquan, Xiaobing Sun, Han Wang, Rufang Ti, and Cunming Shu. "Airborne Polarimetric Remote Sensing for Atmospheric Correction." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3569272.

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The problem, whose targets can not be effectively identified for airborne remote sensing images, is mainly due to the atmospheric scattering effect. This problem is necessary to be overcome. According to the statistical evaluations method and the different characteristics of polarization between the objects radiance and atmospheric path radiation, a new atmospheric correction method for airborne remote sensing images was proposed. Using this new method on the airborne remote sensing images which acquired on the north coast areas of China during the haze weather, we achieved a high quality corr
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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (2021): 2432. http://dx.doi.org/10.3390/rs13132432.

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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the correspondi
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Qiao, Feng, Jianyu Chen, Zhihua Mao, et al. "A Novel Framework of Integrating UV and NIR Atmospheric Correction Algorithms for Coastal Ocean Color Remote Sensing." Remote Sensing 13, no. 21 (2021): 4206. http://dx.doi.org/10.3390/rs13214206.

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Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV
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SUGIMOTO, Nobuo. "Atmospheric Remote Sensing by Lidars." Review of Laser Engineering 19, no. 8 (1991): 787–96. http://dx.doi.org/10.2184/lsj.19.8_787.

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Hawat, Toufic. "Suntracker for atmospheric remote sensing." Optical Engineering 37, no. 5 (1998): 1633. http://dx.doi.org/10.1117/1.601676.

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Korchemkina, Elena N., and Daria V. Kalinskaya. "Algorithm of Additional Correction of Level 2 Remote Sensing Reflectance Data Using Modelling of the Optical Properties of the Black Sea Waters." Remote Sensing 14, no. 4 (2022): 831. http://dx.doi.org/10.3390/rs14040831.

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Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical depth of the atmosphere and the spectral slope of its spectrum, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of errors in the retrieval of the remote sensing reflectance spectra. In this work, the obtained large array of field and satellite data for the northeastern part of the Black Sea is used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aq
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SINGH, D., I. HERLIN, J. P. BERROIR, S. BOUZIDI, and F. LAHOCHE. "Evapotranspiration estimation using remote sensing data." MAUSAM 54, no. 1 (2022): 247–52. http://dx.doi.org/10.54302/mausam.v54i1.1509.

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Evapotranspiration (ET) is a critical hydrological link between the earth surface and the atmosphere. It is therefore important point of issues involving many aspects of climate, climate change, and ecosystem response. It is well known that ET is the process responsible for the transfer of the moisture from soil and vegetated surface to the atmosphere. Changes in ET are likely to have large impacts on terrestrial vegetation. Since the distribution and abundance of plant communities are controlled to a large extent by the quantity and seasonality of moisture. If the changes in water balance are
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Yin, Qiu, Zhaoxian Zhang, and Dingbo Kuang. "Channel selection of atmospheric remote sensing." Applied Optics 35, no. 36 (1996): 7136. http://dx.doi.org/10.1364/ao.35.007136.

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Dissertations / Theses on the topic "Atmospheric remote sensing"

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Goudar, Balsubramani. "Signals of opportunity for atmospheric remote sensing." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723334.

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To obtain the crucial information about the boundary layer(Troposphere), there is a need for measurement of large areas( > 25,000 km2) with ne scale measurements of less than 1-2 km area. Over the past years, several methods have been developed to measure atmospheric water vapour fields, but none of them provide information on such small scales( < 1-2 km). With the recent development of high resolution numerical weather model, the need to provide high temporal and spatial data is ever so significant for proper utilization of the model. This thesis presents a novel approach to estimate the wate
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Morris, Paul. "Remote sensing of the Earth's atmosphere." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317735.

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Dobbs, Brian M. "The incorporation of atmospheric variability into DIRSIG /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/3014.

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Lopatin, Anton. "Enhanced remote sensing of atmospheric aerosol by joint inversion of active and passive remote sensing observations." Thesis, Lille 1, 2013. http://www.theses.fr/2013LIL10141/document.

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Ce travail présente l’algorithme GARRLiC (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data). Le but de cet algorithme est d’inverser simultanément les mesures co-localisées d’un LiDAR et d’un photomètre solaire. Cet algorithme original permet de déduire un ensemble très complet de paramètres descriptifs de l’aérosol atmosphérique, paramètres à la fois intégrés sur la colonne atmosphérique et résolus verticalement. GARRLiC est basée sur la recherche du meilleur ajustement de données multi-sources avec contraintes a priori. Il est basée sur la recherche de la meilleure solut
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Xu, Luyao. "Multi-frequency Atmospheric Refractivity InversionDissertation." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574550558934232.

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Buehler, Stefan. "Remote sensing of atmospheric composition for climate applications : Habilitationschrift." Doctoral thesis, Bremen : University of Bremen, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-18528.

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Wright, Jonathan C. "Evaluation of LOWTRAN and MODTRAN for use over high zenith angle/long path length viewing /." Online version of thesis, 1991. http://hdl.handle.net/1850/11352.

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Hanley, Thomas Ryan. "The microwave opacity of ammonia and water vapor: application to remote sensing of the atmosphere of Jupiter." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24673.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Dr. Paul G. Steffes; Committee Member: Dr. Gregory D. Durgin; Committee Member: Dr. Robert D. Braun; Committee Member: Dr. Thomas K. Gaylord; Committee Member: Dr. Waymond R. Scott
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Albert, Peter. "Remote sensing of atmospheric water vapour for numerical weather prediction." [S.l.] : [s.n.], 2005. http://www.diss.fu-berlin.de/2005/113/index.html.

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Querel, Richard Robert, and University of Lethbridge Faculty of Arts and Science. "Remote sensing of atmospheric water vapour above the Chilean Andes." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Physics and Astronomy, 2010, 2010. http://hdl.handle.net/10133/2586.

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Water vapour is the principle source of opacity at infrared wavelengths in the Earth’s atmosphere. In support of site testing for the European Extremely Large Telescope (E-ELT), we have used La Silla and Paranal as calibration sites to verify satellite measurements of precipitable water vapour (PWV). We reconstructed the PWV history over both sites by analysing thousands of archived high-resolution echelle calibration spectra and compared that to satellite estimates for the same period. Three PWV measurement campaigns were conducted over both sites using several independent measurement techniq
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Books on the topic "Atmospheric remote sensing"

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Bradley, Stuart. Atmospheric acoustic remote sensing. CRC Press, 2008.

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Atmospheric ultraviolet remote sensing. Academic Press, 1992.

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Takashi, Fujii, and Tetsuo Fukuchi. Laser remote sensing. Taylor & Francis, 2005.

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Efremenko, Dmitry, and Alexander Kokhanovsky. Foundations of Atmospheric Remote Sensing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66745-0.

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Cracknell, Arthur Philip, and Costas Varotsos. Remote Sensing and Atmospheric Ozone. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-10334-6.

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Rodolfo, Guzzi, ed. Exploring the atmosphere by remote sensing techniques. Springer, 2003.

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1937-, Janssen Michael A., ed. Atmospheric remote sensing by microwave radiometry. Wiley, 1993.

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Remote sensing of the lower atmosphere: An introduction. Oxford University Press, 1994.

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Dr, Fujii Takashi, and Fukuchi Tetsuo, eds. Laser remote sensing. Taylor & Francis, 2005.

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United States. National Aeronautics and Space Administration., ed. Modelling atmospheric scatterers using spacecraft observations. National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Atmospheric remote sensing"

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Gupta, Ravi P. "Atmospheric Corrections." In Remote Sensing Geology. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-55876-8_10.

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Khorram, Siamak, Frank H. Koch, Cynthia F. van der Wiele, and Stacy A. C. Nelson. "Using Remote Sensing in Atmospheric Applications." In Remote Sensing. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9_5.

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Jin, Shuanggen, Estel Cardellach, and Feiqin Xie. "Ground GNSS Atmospheric Sensing." In GNSS Remote Sensing. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7482-7_3.

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Jin, Shuanggen, Estel Cardellach, and Feiqin Xie. "GNSS Atmospheric and Multipath Delays." In GNSS Remote Sensing. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7482-7_2.

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Jin, Shuanggen, Estel Cardellach, and Feiqin Xie. "Atmospheric Sensing Using GNSS RO." In GNSS Remote Sensing. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7482-7_6.

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Neff, W. D., and R. L. Coulter. "Acoustic Remote Sensing." In Probing the Atmospheric Boundary Layer. American Meteorological Society, 1986. http://dx.doi.org/10.1007/978-1-944970-14-7_13.

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Livesey, Nathaniel. "Limb Sounding, Atmospheric." In Encyclopedia of Remote Sensing. Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_87.

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Kokhanovsky, Alexander A., Claudio Tomasi, Boyan H. Petkov, et al. "Remote Sensing of Atmospheric Aerosol." In Atmospheric Aerosols. Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527336449.ch7.

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Wiegner, Matthias. "Lidar for Aerosol Remote Sensing." In Atmospheric Physics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30183-4_27.

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Yan, Lei, Bin Yang, Feizhou Zhang, Yun Xiang, and Wei Chen. "Atmospheric Remote Sensing 2: Neutral Point Areas of Atmospheric Polarization and Land-Atmosphere Parameter Separation." In Polarization Remote Sensing Physics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2886-6_8.

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Conference papers on the topic "Atmospheric remote sensing"

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Longo, Francesco, and Giovanni Laneve. "Iterative atmospheric parameters estimation of the tropical atmosphere." In Remote Sensing, edited by Klaus Schaefer, Adolfo Comeron, Michel R. Carleer, and Richard H. Picard. SPIE, 2004. http://dx.doi.org/10.1117/12.511007.

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Hays, Paul B. "Remote Sensing of Atmospheric Winds." In Optical Remote Sensing. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ors.1985.wa3.

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The technique for measuring atmospheric winds from the Upper Atmosphere Research Satellite (UARS) is discussed. Stress is placed on the use of absorption features in the spectrum of scattered sunlight to measure the motion of the atmosphere by doppler techniques. Instrumental detail will be presented for the High Resolution Doppler Imager (HRDI) which will be used to make the observations of winds from the UARS.
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Smith, William L., Daniel K. Zhou, Henry E. Revercomb, Hung L. Huang, Poalo Antonelli, and Steven A. Mango. "Hyperspectral atmospheric sounding." In Remote Sensing, edited by Klaus Schaefer, Adolfo Comeron, Michel R. Carleer, and Richard H. Picard. SPIE, 2004. http://dx.doi.org/10.1117/12.515209.

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Liu, Xu, Daniel K. Zhou, Allen Larar, William L. Smith, and Peter Schluessel. "Atmospheric property retrievals from infrared atmospheric sounding interferometer (IASI)." In SPIE Remote Sensing, edited by Richard H. Picard, Adolfo Comeron, Klaus Schäfer, Aldo Amodeo, and Michiel van Weele. SPIE, 2008. http://dx.doi.org/10.1117/12.800361.

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Burrows, J. P., W. Schneider, J. C. Geary, et al. "Atmospheric Remote Sensing with SCIAMACHY." In Optical Remote Sensing of the Atmosphere. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/orsa.1990.mc4.

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SCIAMACHY is an atmospheric remote sensing satellite instrument. It is a passive instrument whose primary measurement objective is to observe the absorptions of tropospheric and stratospheric trace gases in the solar spectrum. Two identical telescope-spectrographs (TS1 and TS2) will observe transmitted, reflected, and scattered light from the atmosphere in the UV, visible and near-infrared wavelength regions (200 - 2400 nm). There are two primary modes of data collection: simultaneous 1 limb and nadir viewing, and solar/lunar occultation. The limb scan yields a vertical profile of atmospheric
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Diner, David J., and John V. Martonchik. "Remote Sensing of Atmospheric Opacity Using Multiple View Angle Imaging." In Optical Remote Sensing. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ors.1985.wc20.

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The Earth’s atmosphere influences spacecraft measurements of surface reflectance in a variety of ways. The dominant effects are (1) extinction, which results in attenuation of both incident and emergent radiation, and (2) the introduction of an additive diffuse path radiance field as a result of atmospheric scattering. Since the magnitude of both of these effects is strongly influenced by the atmospheric opacity, determination of this quantity will be an important component of atmospheric correction algorithms.
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Lukin, Vladimir P. "Outer scale of atmospheric turbulence." In Remote Sensing, edited by Karin Stein and Anton Kohnle. SPIE, 2005. http://dx.doi.org/10.1117/12.649809.

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Christensen, Jacob, Anders Carlström, Anders Emrich, and Peter de Maagt. "The Geostationary Atmospheric Sounder (GAS)." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2006. http://dx.doi.org/10.1117/12.689577.

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David, Florian. "Atmospheric turbulence monitoring at DLR." In Remote Sensing, edited by John D. Gonglewski and Karin Stein. SPIE, 2004. http://dx.doi.org/10.1117/12.565478.

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Vidot, Jerome, and Richard P. Santer. "Atmospheric correction for inland waters." In Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.511439.

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Reports on the topic "Atmospheric remote sensing"

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Ramani, Suchitra. Microwave remote sensing for atmospheric chemistry. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1471300.

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Killinger, Dennis K., Norman Menyuk, and Aram Mooradian. Laser Remote Sensing of Atmospheric Pollutants. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada183014.

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Aikin, Arthur C. Satellite Remote Sensing of Atmospheric Meteoric Ions and Neutral Species. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada464989.

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Sokolik, Irina N. Characterization of Atmospheric Mineral Dust from Radiometric and Polarimetric Remote Sensing. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada611941.

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Sokolik, Irina N. Characterization of Atmospheric Mineral Dust from Radiometric and Polarimetric Remote Sensing. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada541431.

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Acharya, P., L. Bernstein, A. Berk, and S. Adler-Golden. Optimization of Atmospheric Radiance Algorithms for Remote Sensing: MODTRAN upgrades and Applications. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada351102.

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Tooman, T. P. Strategic Environmental Research and Development Program: Atmospheric Remote Sensing and Assessment Program -- Final Report. Part 1: The lower atmosphere. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/481513.

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Tsay, Si-Chee, Q. J. Ji, Santiago Gasso, and Jeffrey S. Reid. Characterization of Dust Aerosols and Atmospheric Parameters from Space-borne and Surface-based Remote Sensing: Application of Community Radiative Transfer Algorithms to Navy Electro-Optical Models. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada628826.

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Tsay, Si-Chee, Q. J. Ji, Santiago Gasso, and Jeffrey S. Reid. Characterization of Dust Aerosols and Atmospheric Parameters from Space-borne and Surface-based Remote Sensing: Application of Community Radiative Transfer Algorithms to Navy Electro-Optical Models. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada633993.

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Sparrow, Kent, and Sandra LeGrand. Establishing a series of dust event case studies for North Africa. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46445.

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Dust aerosols often create hazardous air quality conditions that affect human health, visibility, agriculture, and communication in various parts of the world. While substantial progress has been made in dust-event simulation and hazard mitigation over the last several decades, accurately forecasting the spatial and temporal variability of dust emissions continues to be a challenge. This report documents an analysis of atmospheric conditions for a series of dust events in North Africa. The researchers highlight four analyzed events that occurred between January 2016 to present in the following
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