Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
John, Jinya
- Retrieval of Atmospheric Parameters and Data-Processing Algorithms forAVIRIS-NG Indian Campaign Data
Abstract Views :292 |
PDF Views:119
Authors
Manoj K. Mishra
1,
Anurag Gupta
1,
Jinya John
1,
Bipasha P. Shukla
1,
Philip Dennison
2,
S. S. Srivastava
1,
Nitesh K. Kaushik
1,
Arundhati Misra
1,
D. Dhar
1
Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Geography, University of Utah, Salt Lake City, UT, US
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Geography, University of Utah, Salt Lake City, UT, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1089-1100Abstract
Applications of high-spatial resolution imaging spectrometer data acquired from the Airborne Visible/ Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) under India campaign 2015–16, require a thorough compensation for atmospheric absorption and scattering. The data-processing algorithms used for retrieving critically important atmospheric parameters, namely ‘water vapour and aerosol optical depth (AOD)’ over land and water surfaces are presented. Over land surfaces, the dark dense vegetation method and radiative transfer modelling are used for deriving spectral AOD for boxes of 20 × 20 pixels. For AOD retrieval over water surfaces, dark-target approximation is used with near-infrared and shortwave infrared measurements. Estimation of precipitable water vapour is carried out using short-wave hyperspectral measurements for each pixel. A differential absorption technique (continuum interpolated band ratio) has been used for this purpose. The retrieved AOD and water vapour values were compared with in situ sun-photometer and radiosonde data respectively, indicating good matches. Further, these parameters were used to derive ‘atmospherically corrected surface reflectance and remote sensing reflectance’, for land and water surface respectively, assuming horizontal surfaces having Lambertian reflectance.Keywords
Aerosol, Atmospheric Correction, Hyperspectral Imaging, Surface Reflectance, Water Vapour.Full Text
References
- Vane, G., Green, R. O., Chrien, T. G., Enmark, H. T., Hansen, E. G. and Porter, W. M., The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing Environ., 1993, 44(2–3), 127–143; doi:10.1016/ S0034-4257 (93)90012-M.
- Green, R. O. et al., Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65(3), 227–248; doi:10.1016/S0034-4257 (98)0064-9.
- Hamlin, L., Green, R. O., Mouroulis, P., Eastwood, M., Wilson, D., Dudik, M. and Paine, C., Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments. In IEEE Aerospace Conference Proceeding, 2011, pp. 1–7; http://dx.doi.org/10.1109/AERO.2011.5747395.
- Gao, B. C. and Goetz, A. F., Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J. Geophys. Res. – Atmos., 1990, 95(D4), 3549–3564.
- Adler-Golden, S. M. et al., Atmospheric correction for short-wave spectral imagery based on MODTRAN4. SPIE Proc. Imag. Spectrom., 1999, 3753, 61–69.
- Etheridge, D. M., Steele, L. P., Langenfelds, R. L., Francey, R. J., Barnola, J.-M. and Morgan, V. I., Natural and anthropogenic hanges in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 1996, 101, 4115–4128.
- Leung, D. Y. C., Caramanna, G. and Maroto-Valer, M. M., An overview of current status of carbon dioxide capture and storage technologies. Renew. Sust. Energ. Rev., 2014, 39, 426–443.
- Wunch, D. et al., Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmos. Meas. Tech., 2017, 10, 2209–2238.
- Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noel, S. and Rozanov, V. V., SCIAMACHY: mission objectives and measurement modes. J. Atmos. Sci., 1999, 5, 127–150.
- Hamazaki, T., Kaneko, Y. and Kuze, A., Carbon dioxide monitoring from the GOSAT satellite. In Proceedings of XX ISPRS Conference, Istanbul, Turkey, 12–13 July 2004, pp. 3–5.
- Frankenberg, C. et al., The Orbiting Carbon Observatory (OCO2): spectrometer performance evaluation using pre-launch direct sun measurements. Atmos. Meas. Tech., 2015, 8, 301–313.
- Tadic, J. M. et al., A comparison of in situ aircraft measurements of carbon dioxide and methane to GOSAT data measured over railroad Valley Playa, Nevada, USA. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 7764–7774.
- Green, R. O., Measuring the spectral expression of carbon dioxide in the solar reflected spectrum with AVIRIS. In Proceedings of the 11th Annual Airborne Earth Science Workshop, Jet Propulsion Laboratory, Pasadena, CA, USA, 2001.
- Dennison, P. E. et al., High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: radiative transfer modeling and power plant plume detection. Remote Sensing Environ., 2013, 139, 116–129.
- Thorpe, A. K. et al., Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG. Atmos. Meas. Tech., 2017, 10, 3833–3850.
- Kotchenova, S. Y., Vermote, E. F., Matarrese, R. and Klemm Jr, F. J., Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl. Opt., 2006, 45(26), 6762.
- Lenoble, J., Radiative Transfer in Scattering and Absorbing Atmospheres: Standard Computational Procedures, A. Deepak Publishing, Hampton, VA.
- Tanre, D. et al., Simulation of the Satellite Signal in the Solar Spectrum (5S), User’s Guide, Laboratoire d’Optique Atmospherique, U.S.T. de Lille, 59655 Villeneuve dáscq, France, 1986.
- Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M. and Morcrette, J. J., Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sensing, 1997, 35(3), 675.
- Kaufman, Y. J., Tanre, D. and Boucher, O., A satellite view of aerosols in the climate system. Nature, 2002, 419, 215–223.
- Kaufman, Y. J. et al., Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm – theory. Geophys. Res. Lett., 2002, 29(23), 31-1–31-4.
- Kaufman, Y. J. et al., The MODIS 2.1 μm channel – correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(5), 1286–1298.
- Hsu, N. C. et al., Enhanced deep-blue aerosol retrieval algorithm: the second generation. J. Geophys. Res.: Atmos., 2013, 118, 9296– 9315; https://doi.org/10.1002/jgrd.50712.
- Mishra, M. K., Retrieval of aerosol optical depth from INSAT-3D imager over Asian landmass and adjoining ocean: retrieval uncertainty and validation. J. Geophys. Res.: Atmosp., 2018, 123; https://doi.org/10.1029/2017JD028116.
- Bruegge, C. J. et al., In situ atmospheric water-vapor retrieval in support of AVIRIS validation. Proc. SPIE, 1990, 1298, 150– 163.
- Fowle F. E., The spectroscopic determination of aqueous vapour. Astrophys. J., 1912, 35, 149–162.
- Frouin, R., Deschamps, Pierre-Yves and Lecomte, P., Determination from space of atmospheric total water vapor amounts by differential absorption near 940 nm: theory and airborne verification. J. Appl. Meteorol., 1990, 29, 448–460.
- Berk, A., Bernstein, L. S. and Robertson, D. C., MODTRAN: a moderate resolution model for LOWTRAN7. GL-TR-89-0122. Air Force Geophysical Laboratory, Hanscom AFB, MA, 1989, p. 38.
- Gordon, H. R., Brown, J. W. and Evans, R. H., Exact Rayleigh scattering calculations for use with the Nimbus 7 coastal zone color scanner. Appl. Opt., 1988, 27, 862–871.
- Gordon, H. R., Atmospheric correction of ocean color imagery in the Earth Observing System era. J. Geophys. Res., 1997, 102, 17081–17106.
- Cloud Microphysical Characterization during AVIRIS-NG Campaign
Abstract Views :317 |
PDF Views:112
Authors
Affiliations
1 Atmospheric Sciences Division, Atmosphere and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Physics, Electronics and Space Sciences, Gujarat University, Navrangpura, Ahmedabad 380 009, IN
1 Atmospheric Sciences Division, Atmosphere and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Physics, Electronics and Space Sciences, Gujarat University, Navrangpura, Ahmedabad 380 009, IN
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1196-1200Abstract
Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) air campaign has provided a unique opportunity to characterize the properties of tropical clouds at microscale. A novel approach based on spectral matching technique has been used to derive the cloud microphysical parameters (CMPs) such as optical thickness and effective radius over campaign sites of Kurnool (Andhra Pradesh) and Chilika (Odisha) region in India. It is found that the derived CMPs correspond to medium opacity and effective radius ranging from 4 to 18 μm. The hyperspectral bands coupled with high spatial resolution of the observations make it possible to identify pockets populated densely with large particles within a cloud. This has great applications for picking up fast developing convective cloud cells. More insight with different cloud type observations is anticipated with AVIRISN G phase-2 campaign.Keywords
Cloud Microphysical Parameters, Hyperspectral Imaging, Remote Sensing, Spectral Matching.Full Text
References
- Goetz, A. F., Vane, G., Solomon, J. E. and Rock, B. N., Imaging spectrometry for earth remote sensing. Science, 1985, 228(4704), 1147–1153.
- Ben-Dor, E., Schläpfer, D., Plaza, A. J. and Malthus, T., Hyperspectral remote sensing. In Airborne Measurements for Environmental Research: Methods and Instruments (eds Wendisch, M. and Brenguier, J. L.), 2013, pp. 413–456.
- Heiden, U., Segl, K., Roessner, S. and Kaufmann, H., Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sensing Environ., 2007, 111(4), 537–552.
- Hörig, B., Kühn, F., Oschütz, F. and Lehmann, F., HyMap hyperspectral remote sensing to detect hydrocarbons. Int. J. Remote Sensing, 2001, 22(8), 1413–1422.
- Govender, M., Chetty, K. and Bulcock, H., A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA, 2007, 33(2), 145–151.
- Adam, E., Mutanga, O. and Rugege, D., Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manage., 2010, 18(3), 281– 296.
- Rotstayn, L. D., Indirect forcing by anthropogenic aerosols: a global climate model calculation of the effective–radius and cloud-lifetime effects. J. Geophys. Res.: Atmos., 1999, 104(D8), 9369–9380.
- Rosenfeld, D., Woodley, W. L., Lerner, A., Kelman, G. and Lindsey, D. T., Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res.: Atmos., 2008, 113(D4), D04208.
- Harikishan, G., Padmakumari, B., Maheskumar, R. S., Pandithurai, G. and Min, Q. L., Macrophysical and microphysical properties of monsoon clouds over a rain shadow region in India from ground-based radiometric measurements. J. Geophys. Res. Atmos., 2014, 119; doi:10.1002/2013JD020872; Hirsh, E., Agassi, E. and Koren, I., Determination of optical and microphysical properties of thin warm clouds using ground based hyper-spectral analysis. Atmos. Meas. Tech., 2012, 5, 851–871.
- King, N. J. and Vaughan, G., Using passive remote sensing to retrieve the vertical variation of cloud droplet size in marine stratocumulus: an assessment of information content and the potential for improved retrievals from hyperspectral measurements. J. Geophys. Res.: Atmos., 2012, 117(D15), 1–15.
- Green, R. O. et al., Imaging spectroscopy and the airborne visible/ infrared imaging spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65(3), 227–248.
- Green, R. O., Bhattacharya, B. K., Eastwood, M. L., Saxena, M., Thompson, D. R. and Sadasivarao, B., Overview of the joint NASA–ISRO imaging spectroscopy science campaign in India. In AGU Fall Meeting Abstracts, 2016.
- Hamlin, L., Green, R. O., Mouroulis, P., Eastwood, M., Wilson, D., Dudik, M. and Paine, C., Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments. In IEEE Aerospace Conference, Big Sky, Montana, USA, 2011, pp. 1–7.
- Thorpe, A. K. et al., Mapping methane concentrations from a controlled release experiment using the next generation airborne visible/ infrared imaging spectrometer (AVIRIS-NG). Remote Sensing Environ., 2016, 179, 104–115.
- Bue, B. D. et al., Real-time atmospheric correction of AVIRIS-NG Imagery. IEEE Trans. Geosci. Remote Sensing, 2015, 53(12), 6419–6428.
- Clark, R. N., Swayze, G. A., Wise, R., Livo, K. E., Hoefen, T., Kokaly, R. F. and Sutley, S. J., USGS digital spectral library splib06a. US Geological Survey, Digital Data Series, 2007, 2007, 231.
- Baldridge, A. M., Hook, S. J., Grove, C. I. and Rivera, G., The ASTER spectral library version 2.0. Remote Sensing Environ., 2009, 113(4), 711–715.
- Mayer, B. and Kylling, A., The libRadtran software package for radiative transfer calculations – description and examples of use. Atmos. Chem. Phys., 2005, 5(7), 1855–1877.
- Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J. and Goetz, A. F. H., The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ., 1993, 44(2–3), 145–163.
- Jayeshlal, G. S. et al., Lidar studies on the optical characteristics of high altitude cirrus clouds at a low latitude station, Gadanki (13.5°N, 79.2°E) India. Int. Arch. Photogramm., Remote Sensing Spatial Inf. Sci., 2014, 40(8), 253.