Estimation of surface albedo from NOAA AVHRR data in high latitudes

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 48, Issue 3, 1996, Pages 424-441

  Laine, Vesa ^a   Heikinheimo, Martti ^a  

^a FINNISH METEOROLOGICAL INSTITUTE   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALBEDO; ALBEDO EFFECT; AVHRR; AVHRR DATA; HIGH LATITUDE; HIGH LATITUDES; MONITORING; NOAA; SATELLITE DATA; SURFACE ALBEDO;

EID: 0029833818     PISSN: 02806495     EISSN: None     Source Type: Journal    
DOI: 10.3402/tellusa.v48i3.12069     Document Type: Article

References (20)

1. Allen, R. C. Jr., Durkee, P. and Carlyle, W. H. 1990. Snow/cloud discrimination with multispectral satellite measurements. J. Appl. Meteor. 29, 994-1004.
2. Bringfelt B., Gustafsson N., Vilmusenaho P. and Järvenoja S. 1995. Updating of the HIRLAM physiography and climate data base. HIRLAM Tecnical Report no. 19 (available from SMHI, Norrköping, Sweden.)
3. Derrien, M., Farki, B., Harang, L., LeGleau, H., Noyalet, A., Pochic, D. and Sairouni, A. 1993. Automatic cloud detection applied to NOAA-11/AVHRR Imagery. Remote Sens. Environ. 46, 246-267.
4. Di, L. and Rundquist, D. C. 1994. A one-step algorithm for correction and calibration of AVHRR level 1b data. Photogrammetric Engineering & Remote Sensing 60, 165-171.
5. Garand, L. 1988. Automated recognition of oceanic cloud patterns. Part I. Methodology and application to cloud climatology. J. Clim. 1, 20-39.
6. Gerstl S. A. C. and Simmer C 1986. Radiation physics and modelling for off-nadir satellite-sensing of nonLambertian surfaces. Remote Sens. Environ 20, 1-29.
7. Gutman, G. 1988. A simple method for estimating monthly mean albedo of land surfaces from AVHRR data. J. Appl. Meteor 27, 973-988.
8. Karlsson K. G., 1989. Developement of an cloud classification model. Int. J. Remote Sens 10, 687-693.
9. Laine, V., 1992. Atmospheric aerosol optical thickness and size distribution from satellite data over the Baltic Sea. Diss. Commentationes Physico-Mathematicae et Chemico-Medicae 137, (The Finnish Society of Sciences and Letters).
10. Li, Z. and Garand, L. 1994. Estimation of surface albedo from space: A parameterization for global application. J. Geophys. Res 99, 8335-8350.
11. Liljas, E. 1987. Automated recognition of cloudtypes from satellites and its application to nowcasting. Int. Conf. on Agrometeorology, Cesena 1987, Fondzione Cesena Agricultura Publ.
12. Paltridge, G. W. and Mitchell R. M. 1990. Atmospheric and viewing angle correction of vegetation indices and grassland fuel moisture content derived from NOAA/ AVHRR. Remote Sens. Environ. 31, 121-135.
13. Phulpin, T., Derrien, M. and Brard A., 1983. A two dimensional histogram procedure to analyze cloud cover from NOAA satellite high resolution imagery. J. Clim. Appl. Meteorol 22, 1332-1345.
14. Solantie, R. 1988. Albedo in Finland on the basis of observations on aircraft. Finnish Met. Institute, Meteorological Publications 12, 106pp.
15. Saunders, R. W. and Kriebel K. T. 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens. 9, 123-150.
16. Suttles, J. T., Green, R. N., Minnis, P., Smith, G. L., Staylor, W. F., Wielicki, B. A., Walker, I. J., Young, D. F., Taylor, V. R. and Stowe, L. L. 1988. Angular radiation models for Earth-atmosphere system, vol. 1, Shortwave radiation. NASA Ref. Publ. 1184, 114pp.
17. Taylor, R. V. and Stowe, L. L. 1984. Reflectance characteristics of uniform earth and cloud surfaces derived from NIMBUS-7 ERB. J. Geophys. Res. 89, 4987-4996.
18. Teillet, P. M. and Holben B.N. 1994. Towards operational radiometric calibration of NOAA AVHRR imagery in the visible and near-infrared channels. Canadian J. Remote Sens. 20, 1-10.
19. Van der Veen, C. J. 1992. Land ice and climate. In: Climate System modelling, Trenberth K. E. (ed.), p. 437-450, Cambridge University Press, 788 pp.
20. Wydick, J. E., Davis, P. A. and Gruber A., 1987. Estimation of broadband planetary albedo from operational narrowband satellite measurements. NOAA Technical Report NESDIS 27.