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Journal articles on the topic 'Atmospheric remote sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Taylor, F. W. "Remote sensing of Venus atmospheric dynamics." Advances in Space Research 12, no. 9 (1992): 57–71. http://dx.doi.org/10.1016/0273-1177(92)90321-n.

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12

Fischer, H. "Remote Sensing of Atmospheric Trace Gases." Interdisciplinary Science Reviews 18, no. 3 (1993): 185–91. http://dx.doi.org/10.1179/isr.1993.18.3.185.

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13

Bühl, J., S. Alexander, S. Crewell, et al. "Remote Sensing." Meteorological Monographs 58 (January 1, 2017): 10.1–10.21. http://dx.doi.org/10.1175/amsmonographs-d-16-0015.1.

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Abstract State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on air

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14

Liu, Yun, Yuqin Jing, and Yinan Lu. "Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence." Journal of Chemistry 2020 (March 4, 2020): 1–7. http://dx.doi.org/10.1155/2020/7390545.

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When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer

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15

Bhavsar, P. D. "Acoustic Remote Sensing." MAUSAM 42, no. 4 (2022): 431–32. http://dx.doi.org/10.54302/mausam.v42i4.4963.

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16

Cachorro, Victoria E., and Manuel Antón. "Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”." Remote Sensing 12, no. 13 (2020): 2074. http://dx.doi.org/10.3390/rs12132074.

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The observation/monitoring of atmospheric components and water vapor in the atmosphere is today open to very different remote sensing techniques, most of them based on the radiation-matter interaction covering the full electromagnetic spectrum. This SI collects some papers regarding the retrieval, calibration, validation, analysis of data and uncertainties, as well as comparative studies on atmospheric gases and water vapor by remote sensing techniques, where different types of sensors, instruments, and algorithms are used or developed.

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17

Xu, Lingling, Wei Xiong, Weining Yi, et al. "Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite." Remote Sensing 14, no. 17 (2022): 4427. http://dx.doi.org/10.3390/rs14174427.

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Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric p

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18

Wu, Dong L., Donald E. Jennings, Kwong-Kit Choi, et al. "Compact Thermal Imager (CTI) for Atmospheric Remote Sensing." Remote Sensing 13, no. 22 (2021): 4578. http://dx.doi.org/10.3390/rs13224578.

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The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations

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19

Nina, Aleksandra, Milan Radovanović, and Luka Popović. "Extraterrestrial Influences on Remote Sensing in the Earth’s Atmosphere." Remote Sensing 13, no. 5 (2021): 890. http://dx.doi.org/10.3390/rs13050890.

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Atmospheric properties have a significant influence on electromagnetic (EM) waves, including the propagation of EM signals used for remote sensing. For this reason, changes in the received amplitudes and phases of these signals can be used for the detection of the atmospheric disturbances and, consequently, for their investigation. Some of the most important sources of the temporal and space variations in the atmospheric parameters come from the outer space. Although the solar radiation dominates in these processes, radiation coming out of the solar system also can induces enough intensive dis

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20

USPENSKII, A. B., YU M. TIMOFEEV, D. A. KOZLOV, and I. V. CHERNYI. "DEVELOPMENT OF METHODS AND INSTRUMENTS FOR REMOTE TEMPERATURE AND HUMIDITY SENSING OF THE EARTH’S ATMOSPHERE." Meteorologiya i Gidrologiya, no. 12 (December 2021): 33–44. http://dx.doi.org/10.52002/0130-2906-2021-12-33-44.

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The paper provides an overview of methods and technical devices for remote temperature and humidity sensing of the Earth’s atmosphere from satellites developed in Russia. A brief description of modern and forthcoming infrared and microwave atmospheric sounders installed on Meteor-M operational polar-orbiting weather satellites is given. The physical and mathematical base of interpreting measurements of atmospheric sounders (IKFS-2 infrared Fourier spectrometer and MTVZA-GYa microwave radiometer) is presented. The technologies for retrieving atmospheric temperature and humidity profiles are des

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21

Watzlawick, Luciano Farinha, Pedro Roberto de Azambuja Madruga, and Rudiney Soares Pereira. "CCD (Charge Coupled Device) funcionamento e sua aplicação em sensoriamento remoto." Ciência e Natura 24, no. 24 (2002): 63. http://dx.doi.org/10.5902/2179460x27225.

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The present article concerns about concepts, comments on the composition, working method, advantages and disadvantages of using the CCD, and its application in remote sensing as well, and to do so, we discuss the physical principies of the remote sensing, electromagnetic spectrum, propagation of the electromagnetic radiation, interactions between energy and atmosphere, atmospheric windows and the information acquisition system (detection, record and storage).

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22

Li, Z., Y. Zhang, and J. Hong. "POLARIMETRIC REMOTE SENSING OF ATMOSPHERIC PARTICULATE POLLUTANTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 981–84. http://dx.doi.org/10.5194/isprs-archives-xlii-3-981-2018.

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Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM<sub>2.5</sub> mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies.

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23

Qiang, Xiwen. "Remote Sensing of Atmospheric Turbulence Profiles by Laser Guide Stars." EPJ Web of Conferences 237 (2020): 06014. http://dx.doi.org/10.1051/epjconf/202023706014.

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Ranged-resolved profiles of atmospheric turbulence are necessary and important for many applications in astronomical and adaptive optics communities. In order to characterize the vertical atmospheric structure in field, a technique is put forward to remote sensing ranged-resolved profiles of atmospheric turbulence by combined with laser guide stars and differential image motion method. Laser guide stars are formed at several successive altitudes by projecting pulsed laser, returned signals of images are received by a optical system with two receiving telescopes, and variance of centroids′ dist

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24

Edner, Hans, Kent Fredriksson, Anders Sunesson, Sune Svanberg, Leif Unéus, and Wilhelm Wendt. "Mobile remote sensing system for atmospheric monitoring." Applied Optics 26, no. 19 (1987): 4330. http://dx.doi.org/10.1364/ao.26.004330.

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25

Peckham, G. E. "Instrumentation and measurement in atmospheric remote sensing." Reports on Progress in Physics 54, no. 4 (1991): 531–77. http://dx.doi.org/10.1088/0034-4885/54/4/001.

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26

DALU, G. "Satellite remote sensing of atmospheric water vapour." International Journal of Remote Sensing 7, no. 9 (1986): 1089–97. http://dx.doi.org/10.1080/01431168608948911.

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27

Noerdlinger, Peter D. "Atmospheric refraction effects in Earth remote sensing." ISPRS Journal of Photogrammetry and Remote Sensing 54, no. 5-6 (1999): 360–73. http://dx.doi.org/10.1016/s0924-2716(99)00030-1.

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28

Doicu, Adrian, Franz Schreier, and Michael Hess. "Iterative regularization methods for atmospheric remote sensing." Journal of Quantitative Spectroscopy and Radiative Transfer 83, no. 1 (2004): 47–61. http://dx.doi.org/10.1016/s0022-4073(02)00292-3.

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29

Doicu, A., and T. Trautmann. "Two linearization methods for atmospheric remote sensing." Journal of Quantitative Spectroscopy and Radiative Transfer 110, no. 8 (2009): 477–90. http://dx.doi.org/10.1016/j.jqsrt.2009.02.001.

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30

Dicaire, I., V. Jukna, C. Praz, C. Milián, L. Summerer, and A. Couairon. "Spaceborne laser filamentation for atmospheric remote sensing." Laser & Photonics Reviews 10, no. 3 (2016): 481–93. http://dx.doi.org/10.1002/lpor.201500283.

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31

Zhu, Weining, and Wei Xia. "Effects of Atmospheric Correction on Remote Sensing Statistical Inference in an Aquatic Environment." Remote Sensing 15, no. 7 (2023): 1907. http://dx.doi.org/10.3390/rs15071907.

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Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (Rrs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validati

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32

Yang, Xiao Feng, and Xing Ping Wen. "Atmospheric Correction of Landsat ETM+ Remote Sensing Data Using 6S Code and its Validation." Applied Mechanics and Materials 29-32 (August 2010): 2365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2365.

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Atmospheric correction is one of the most important pre-processing steps in quantitative remote sensing. To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. Furthermore, multi-temporal images after atmospheric correction can be compared to each other quantitatively. The Second simulation of satellite signal in the solar spectrum (6S) radiative code can process many types of satellite data and provide several standard atmosphere and aerosol models for atmospheric correction. This paper demonstrates atm

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33

Di, Huige, Yun Yuan, Qing Yan, et al. "Determination of atmospheric column condensate using active and passive remote sensing technology." Atmospheric Measurement Techniques 15, no. 11 (2022): 3555–67. http://dx.doi.org/10.5194/amt-15-3555-2022.

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Abstract. To further exploit atmospheric cloud water resources (CWRs), it is necessary to correctly evaluate the number of CWRs in an area. The CWRs are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited and maximise possible precipitation in the atmosphere (Zhou et al., 2020). Three items are included in CWRs: the existing hydrometeors at a certain time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapour through condensation or

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34

Romano, Filomena. "Editorial for the Special Issue “Remote Sensing of Clouds”." Remote Sensing 12, no. 24 (2020): 4085. http://dx.doi.org/10.3390/rs12244085.

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35

Ha, Tran, Arnaud Cuisset, Sébastien Payan, et al. "The first Vietnam School of Earth Observation: Atmospheric Remote Sensing and Molecular Spectroscopy." VIETNAM JOURNAL OF EARTH SCIENCES 41, no. 2 (2019): 138–55. http://dx.doi.org/10.15625/0866-7187/41/2/13724.

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In this review paper, we give an introduction to molecular spectroscopy and its relation to atmospheric remote sensing and examples of recent developments in spectroscopic experimental techniques and modelling. Atmospheric retrieval techniques, based on radiative transfer theories and molecular spectroscopy as well as some atmospheric remote sensing missions using spectroscopic techniques are presented.

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36

Ferguson, Craig R., and Eric F. Wood. "Observed Land–Atmosphere Coupling from Satellite Remote Sensing and Reanalysis." Journal of Hydrometeorology 12, no. 6 (2011): 1221–54. http://dx.doi.org/10.1175/2011jhm1380.1.

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Abstract The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moist

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37

Lubin, Dan, Gabrielle Ayres, and Steven Hart. "Remote Sensing of Polar Regions." Bulletin of the American Meteorological Society 90, no. 6 (2009): 825–35. http://dx.doi.org/10.1175/2008bams2596.1.

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38

Yin, Xue Mei, Qiu Yang Ma, Xue Hong Wu, Yi Gong, and Yan Li Lu. "Calculation of Radiation Signal of Rocket Plume after Atmospheric Attenuation Using Wide Band K-Distribution Model." Applied Mechanics and Materials 295-298 (February 2013): 2437–41. http://dx.doi.org/10.4028/www.scientific.net/amm.295-298.2437.

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The calculation of the gas radiation process plays an important role in the study of atmospheric remote sensing and climatic effects of greenhouse gas. The remote sensing of rocket plume has important significance for early warning, interception, detection, identification and tracking of flight vehicle. A model was established to calculate the remote sensing signal of rocket plume by wide band k-distribution, the liquid rocket plume remote sensing signals in atmospheric window region and the detectors’ working spectrum are calculated, and the results were compared with the results calculated b

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39

Khutorova, O. G., and V. N. Khutorov. "Some regularities of atmospheric mesoscale variations obtained from satellite navigation system remote sensing." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, no. 6 (2020): 76–81. http://dx.doi.org/10.21046/2070-7401-2020-17-6-76-81.

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40

He, Yufeng, Cuili Li, and Tiecheng Bai. "Remote Sensing Image Haze Removal Based on Superpixel." Remote Sensing 15, no. 19 (2023): 4680. http://dx.doi.org/10.3390/rs15194680.

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The presence of haze significantly degrades the quality of remote sensing images, resulting in issues such as color distortion, reduced contrast, loss of texture, and blurred image edges, which can ultimately lead to the failure of remote sensing application systems. In this paper, we propose a superpixel-based visible remote sensing image dehazing algorithm, namely SRD. To begin, the remote sensing haze images are divided into content-aware patches using superpixels, which cluster adjacent pixels considering their similarities in color and brightness. We assume that each superpixel region sha

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41

Langerock, B., M. De Mazière, F. Hendrick, et al. "Description of algorithms for co-locating and comparing gridded model data with remote-sensing observations." Geoscientific Model Development 8, no. 3 (2015): 911–21. http://dx.doi.org/10.5194/gmd-8-911-2015.

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Abstract. MACC-II,III, Monitoring Atmospheric Composition and Climate, is the current pre-operational Copernicus Atmosphere Monitoring Service (CAMS). It provides data records on atmospheric composition for recent years, present conditions and forecasts (a few days ahead). To support the quality assessment of the CAMS products, the EU FP7 project Network Of ground-based Remote-Sensing Observations (NORS) created a server to validate the gridded MACC-II,III/CAMS model data against remote-sensing observations from the Network for the Detection of Atmospheric Composition Change (NDACC), for a sel

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42

De Vis, Pieter, Frédéric Mélin, Samuel E. Hunt, Rosalinda Morrone, Morven Sinclair, and Bill Bell. "Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals." Remote Sensing 14, no. 3 (2022): 497. http://dx.doi.org/10.3390/rs14030497.

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Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the

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43

Qiang, Xi Wen, Jun Wei Zhao, Shuang Lian Feng, et al. "Review on Remote Sensing of Atmospheric Turbulence Profiles by Laser Guide Stars." Applied Mechanics and Materials 303-306 (February 2013): 823–26. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.823.

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Ranged-resolved profiles of atmospheric turbulence are necessary and important for many applications in astronomical and adaptive optics communities. In order to characterize the vertical atmospheric structure in field, a technique is put forward to remote sensing ranged-resolved profiles of atmospheric turbulence by combined with laser guide stars and differential image motion method. Laser guide stars are formed at several successive altitudes by projecting pulsed laser, returned signals of images are received by a optical system with two receiving telescopes, and variance of centroids dist

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44

Molero. "Remote Sensing of Aerosols." Atmosphere 10, no. 11 (2019): 655. http://dx.doi.org/10.3390/atmos10110655.

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45

Romano, Filomena. "Remote Sensing of Clouds." Atmosphere 10, no. 12 (2019): 814. http://dx.doi.org/10.3390/atmos10120814.

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46

Russchenberg, H. W. J., F. Bosveld, D. Swart, et al. "Ground-Based Atmospheric Remote Sensing in the Netherlands." Telecommunications and Radio Engineering 66, no. 17 (2007): 1591–602. http://dx.doi.org/10.1615/telecomradeng.v66.i17.90.

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47

MITSUTA, Yasushi. "Remote sensing technique in the atmospheric boundary layer." Journal of the Visualization Society of Japan 10, no. 37 (1990): 88–91. http://dx.doi.org/10.3154/jvs.10.88.

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48

Guerra, David V., Geary K. Schwemmer, Albert D. Wooten, Sandipan S. Chaudhuri, and Thomas D. Wilkerson. "Prototype holographic atmospheric scanner for environmental remote sensing." Journal of Geophysical Research: Atmospheres 104, no. D18 (1999): 22287–92. http://dx.doi.org/10.1029/1999jd900324.

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49

Budzien, Scott A., Rebecca L. Bishop, Andrew W. Stephan, Andrew B. Christensen, and Donald R. McMullin. "Atmospheric Remote Sensing on the International Space Station." Eos, Transactions American Geophysical Union 91, no. 42 (2010): 381–82. http://dx.doi.org/10.1029/2010eo420002.

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50

Traverso, Andrew J., Rodrigo Sanchez-Gonzalez, Luqi Yuan, et al. "Coherence brightened laser source for atmospheric remote sensing." Proceedings of the National Academy of Sciences 109, no. 38 (2012): 15185–90. http://dx.doi.org/10.1073/pnas.1211481109.

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We have studied coherent emission from ambient air and demonstrated efficient generation of laser-like beams directed both forward and backward with respect to a nanosecond ultraviolet pumping laser beam. The generated optical gain is a result of two-photon photolysis of atmospheric O2, followed by two-photon excitation of atomic oxygen. We have analyzed the temporal shapes of the emitted pulses and have observed very short duration intensity spikes as well as a large Rabi frequency that corresponds to the emitted field. Our results suggest that the emission process exhibits nonadiabatic atomi

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