Article Type:
Research Article

Daily NDVI Relationship to Cloud Cover

Qiuhong Tang Institute of Industrial Science, University of Tokyo, Tokyo, Japan

Search for other papers by Qiuhong Tang in
Current site
Google Scholar
PubMed Close
and
Taikan Oki Institute of Industrial Science, University of Tokyo, Tokyo, Japan

Search for other papers by Taikan Oki in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2007
DOI:
https://doi.org/10.1175/JAM2468.1
Page(s):
377–387
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

A normalized difference vegetation index (NDVI) cloud index (NCI) was derived from Pathfinder Advanced Very High Resolution Radiometer (AVHRR) daily NDVI data and compared with observed cloud amounts and a sunshine duration–cloud index (SCI) over an area of diverse land cover. Ground observations from 120 meteorological stations were significantly related to the daily NCI and the SCI, with R2 values of 0.41 and 0.50, respectively. The daily NCI and interpolated cloud indices derived from ground observations over the 776 900 km2 study area were compared. The correlation coefficient between the NCI and the observed cloud amount was less than 0.6 for less than 20% of the area. The correlation coefficient between the NCI and the observed sunshine duration index was less than 0.6 for less than 10% of the area and less than 0.7 for 41% of the area. There were strong correlations for high elevations in summer, and correlations for low elevations in winter were weaker. A frozen soil surface or snow cover degrades the NDVI relationship to clouds. The NCI and observed cloud indices had high correlation coefficients in areas with diverse land uses, suggesting that the NCI may be useful in estimating cloudiness over a large region.

Corresponding author address: Qiuhong Tang, Wilson Ceramic Laboratory, Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195-2700. Email: tangqh@iis.u-tokyo.ac.jp

Abstract

A normalized difference vegetation index (NDVI) cloud index (NCI) was derived from Pathfinder Advanced Very High Resolution Radiometer (AVHRR) daily NDVI data and compared with observed cloud amounts and a sunshine duration–cloud index (SCI) over an area of diverse land cover. Ground observations from 120 meteorological stations were significantly related to the daily NCI and the SCI, with R2 values of 0.41 and 0.50, respectively. The daily NCI and interpolated cloud indices derived from ground observations over the 776 900 km2 study area were compared. The correlation coefficient between the NCI and the observed cloud amount was less than 0.6 for less than 20% of the area. The correlation coefficient between the NCI and the observed sunshine duration index was less than 0.6 for less than 10% of the area and less than 0.7 for 41% of the area. There were strong correlations for high elevations in summer, and correlations for low elevations in winter were weaker. A frozen soil surface or snow cover degrades the NDVI relationship to clouds. The NCI and observed cloud indices had high correlation coefficients in areas with diverse land uses, suggesting that the NCI may be useful in estimating cloudiness over a large region.

Corresponding author address: Qiuhong Tang, Wilson Ceramic Laboratory, Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195-2700. Email: tangqh@iis.u-tokyo.ac.jp

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smithz, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp.

  • Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer, 1976: A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 28 pp.

  • Carlson, T. N., and D. A. Ripley, 1997: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62 , 241–252.

    • Search Google Scholar
    • Export Citation

  • Choudhury, B. J., and R. E. Golus, 1988: Estimating soil wetness using satellite data. Int. J. Remote Sens., 9 , 1251–1257.

  • Goward, S. N., B. Markham, D. G. Dye, W. Dulaney, and J. Yang, 1991: Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer. Remote Sens. Environ., 35 , 257–277.

    • Search Google Scholar
    • Export Citation

  • Grist, J., S. E. Nicholson, and A. Mpolokang, 1997: On the use of NDVI for estimating rainfall fields in the Kalahari of Botswana. J. Arid Environ., 35 , 195–214.

    • Search Google Scholar
    • Export Citation

  • Holben, B. N., 1986: Characteristics of maximum-value composite images for temporal AVHRR data. Int. J. Remote Sens., 7 , 1417–1434.

  • Hou, Y. T., K. A. Campana, K. E. Mitchell, S. K. Yang, and L. L. Stowe, 1993: Comparison of an experimental NOAA AVHRR cloud dataset with other observed and forecast cloud datasets. J. Atmos. Oceanic Technol., 10 , 833–849.

    • Search Google Scholar
    • Export Citation

  • HYDRO1k Team, cited. 2003: HYDRO1k: Elevation Derivative Database. [Available online at http://edcdaac.usgs.gov/gtopo30/hydro.].

  • Jackson, R. D., P. N. Slater, and P. J. Pinter, 1983: Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sens. Environ., 13 , 187–208.

    • Search Google Scholar
    • Export Citation

  • James, M. E., and S. N. V. Kalluri, 1994: The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. Int. J. Remote Sens., 15 , 3347–3363.

    • Search Google Scholar
    • Export Citation

  • Justice, C. O., J. R. G. Townshend, B. N. Holben, and C. J. Tucker, 1985: Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens., 6 , 1271–1318.

    • Search Google Scholar
    • Export Citation

  • Loveland, T. R., Z. Zhu, D. O. Ohlen, J. F. Brown, B. C. Reed, and L. Yang, 1999: An analysis of the IGBP global land-cover characterization process. Photogramm. Eng. Remote Sens., 65 , 1021–1032.

    • Search Google Scholar
    • Export Citation

  • New, M., M. Hulme, and P. Jones, 1999: Representing twentieth-century space–time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology. J. Climate, 12 , 829–856.

    • Search Google Scholar
    • Export Citation

  • Roerink, G. J., M. Menenti, and W. Verhoef, 2000: Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens., 21 , 1911–1917.

    • Search Google Scholar
    • Export Citation

  • Stowe, L. L., 1991: Cloud and aerosol products at NOAA/NESDIS. Global Planet. Change, 4 , 25–32.

  • Stowe, L. L., E. P. McClain, R. Carey, P. Pellegrino, G. G. Gutman, P. Davis, C. Long, and S. Hart, 1991: Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data. Adv. Space Res., 11 , 51–54.

    • Search Google Scholar
    • Export Citation

  • Stowe, L. L., P. A. Davis, and E. P. McClain, 1999: Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the Advanced Very High Resolution Radiometer. J. Atmos. Oceanic Technol., 16 , 656–681.

    • Search Google Scholar
    • Export Citation

  • Sun, B., P. Ya Groisman, and I. I. Mokhov, 2001: Recent changes in cloud-type frequency and inferred increases in convection over the United States and the former USSR. J. Climate, 14 , 1864–1880.

    • Search Google Scholar
    • Export Citation

  • Szilagyi, J., D. C. Rundquist, D. C. Gosselin, and M. B. Parlange, 1998: NDVI relationship to monthly evaporation. Geophys. Res. Lett., 25 , 1753–1756.

    • Search Google Scholar
    • Export Citation

  • Tarpley, J. D., S. R. Schneider, and R. L. Money, 1984: Global vegetation indices from the NOAA-7 meteorological satellite. J. Climate Appl. Meteor., 23 , 491–494.

    • Search Google Scholar
    • Export Citation

  • Tucker, C. J., 1979: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ., 8 , 127–150.

  • Tucker, C. J., H. E. Dregne, and W. W. Newcomb, 1991: Expansion and contraction of the Sahara Desert from 1980 to 1990. Science, 253 , 299–301.

    • Search Google Scholar
    • Export Citation

  • Verhoef, W., M. Menenti, and S. Azzali, 1996: A colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981–1992). Int. J. Remote Sens., 17 , 231–235.

    • Search Google Scholar
    • Export Citation

  • Wiegand, C. L., A. J. Richardson, and E. T. Kanemasu, 1979: Leaf area index estimates for wheat from LANDSAT and their implications for evapotranspiration and crop modeling. Agron. J., 71 , 336–342.

    • Search Google Scholar
    • Export Citation

  • Yang, D., C. Li, H. Hu, Z. Lei, S. Yang, T. Kusuda, T. Koike, and K. Musiake, 2004: Analysis of water resources variability in the Yellow River of China during the last half century using historical data. Water Resour. Res., 40 .W06502, doi:10.1029/2003WR002763.

    • Search Google Scholar
    • Export Citation

  • Zhang, S. Y., and T. H. L. Williams, 1997: Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrologic model. Photogramm. Eng. Remote Sens., 63 , 69–77.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1033 501 151
PDF Downloads 546 163 12