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Cloud Microphysical Characterization during AVIRIS-NG Campaign


Affiliations
1 Atmospheric Sciences Division, Atmosphere and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
2 Department of Physics, Electronics and Space Sciences, Gujarat University, Navrangpura, Ahmedabad 380 009, India
 

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.
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  • 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.


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  • Cloud Microphysical Characterization during AVIRIS-NG Campaign

Abstract Views: 351  |  PDF Views: 127

Authors

Bipasha Paul Shukla
Atmospheric Sciences Division, Atmosphere and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Jinya John
Department of Physics, Electronics and Space Sciences, Gujarat University, Navrangpura, Ahmedabad 380 009, India
Sambit Kumar Panda
Atmospheric Sciences Division, Atmosphere and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India

Abstract


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.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1196-1200