Editorial Type:
Article
Article Type:
Research Article

The Impact of the Diurnal Variation of Albedo on the Remote Sensing of the Daily Mean Albedo of Grassland

I. F. Grant CSIRO Atmospheric Research, Aspendale, Victoria, Australia

Search for other papers by I. F. Grant in
Current site
Google Scholar
PubMed Close
,
A. J. Prata CSIRO Atmospheric Research, Aspendale, Victoria, Australia

Search for other papers by A. J. Prata in
Current site
Google Scholar
PubMed Close
, and
R. P. Cechet CSIRO Atmospheric Research, Aspendale, Victoria, Australia

Search for other papers by R. P. Cechet in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2000
DOI:
https://doi.org/10.1175/1520-0450(2000)039<0231:TIOTDV>2.0.CO;2
Page(s):
231–244
Restricted access

Abstract

The correction of a land surface albedo estimate made at one solar zenith angle (SZA) from a polar-orbiting satellite to a standard SZA or to a daily mean albedo requires knowledge of the dependence of the albedo on SZA. This paper uses ground-based measurements of the clear-sky albedo at a uniform grassland site at Uardry (34.39°S, 145.30°E) in southeastern Australia to investigate the accuracy to which the daily mean albedo can be inferred from the albedo at 1030 LST, given knowledge of the SZA dependence of albedo to various levels of detail. During nine months in which the daily mean albedo varied from 0.20 to 0.27, the albedo always had the expected minimum near noon but the strength of the albedo’s SZA dependence varied greatly. For a few months, albedos were up to 0.04 higher in the afternoon than in the morning, and variations on finer timescales of up to 0.02 also appeared in the diurnal albedo cycle for days or weeks. These features of the diurnal variation were all seen at two or three surface points separated by up to 750 m and so are expected to appear at the ∼1-km resolution of many satellite sensors. For the Uardry grassland site, the error in estimating the daily mean albedo from the 1030 LST, albedo can be up to 0.03, which is 15% of an albedo of 0.20, if the albedo is assumed to be constant through the day. The maximum error is reduced to about 0.02 if a simple model of the SZA dependence is used with even an approximate value for the parameter that controls the strength of the dependence, and to 0.01 or less if the strength of the dependence is appropriate to the state of the vegetation on the day. Afternoon–morning asymmetry in the albedo can contribute almost 0.01 to the error in inferring a daily albedo from a morning measurement.

Corresponding author address: I. F. Grant, CSIRO Atmospheric Research, PMB 1, Aspendale, VIC 3195, Australia.

Abstract

The correction of a land surface albedo estimate made at one solar zenith angle (SZA) from a polar-orbiting satellite to a standard SZA or to a daily mean albedo requires knowledge of the dependence of the albedo on SZA. This paper uses ground-based measurements of the clear-sky albedo at a uniform grassland site at Uardry (34.39°S, 145.30°E) in southeastern Australia to investigate the accuracy to which the daily mean albedo can be inferred from the albedo at 1030 LST, given knowledge of the SZA dependence of albedo to various levels of detail. During nine months in which the daily mean albedo varied from 0.20 to 0.27, the albedo always had the expected minimum near noon but the strength of the albedo’s SZA dependence varied greatly. For a few months, albedos were up to 0.04 higher in the afternoon than in the morning, and variations on finer timescales of up to 0.02 also appeared in the diurnal albedo cycle for days or weeks. These features of the diurnal variation were all seen at two or three surface points separated by up to 750 m and so are expected to appear at the ∼1-km resolution of many satellite sensors. For the Uardry grassland site, the error in estimating the daily mean albedo from the 1030 LST, albedo can be up to 0.03, which is 15% of an albedo of 0.20, if the albedo is assumed to be constant through the day. The maximum error is reduced to about 0.02 if a simple model of the SZA dependence is used with even an approximate value for the parameter that controls the strength of the dependence, and to 0.01 or less if the strength of the dependence is appropriate to the state of the vegetation on the day. Afternoon–morning asymmetry in the albedo can contribute almost 0.01 to the error in inferring a daily albedo from a morning measurement.

Corresponding author address: I. F. Grant, CSIRO Atmospheric Research, PMB 1, Aspendale, VIC 3195, Australia.

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

  • Barkstrom, B. R., E. F. Harrison, G. L. Smith, R. N. Green, J. Kibler, R. Cess, and the ERBE Science Team, 1989: Earth Radiation Budget Experiment (ERBE) archival and April 1995 results. Bull. Amer. Meteor. Soc.,70, 1254–1262.

  • Brest, C., and S. Goward, 1987: Deriving surface albedo measurement from narrow band satellite data. Int. J. Remote Sens.,8, 351–367.

  • Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison, 1986: Comparison of regional clear-sky albedos inferred from satellite observations and model computations. J. Climate Appl. Meteor.,25, 214–226.

  • Brooks, D. R., E. F. Harrison, P. Minnis, J. T. Suttles, and R. S. Kandel, 1986: Development of algorithms for understanding the temporal and spatial variability of the earth’s radiation balance. Rev. Geophys.,24, 422–438.

  • Charlock, T. P., and Coauthors, cited 1997: CERES Algorithm Theoretical Basis Document Subsystem 5.0—Compute surface and atmospheric fluxes. NASA EOS CERES Doc., Release 2.2. [Available online at http://eospso.gsfc.nasa.gov/atbd/cerestables.html].

  • Dickinson, R. E., 1983: Land surface processes and climate—Surface albedos and energy balance. Advances in Geophysics, Vol. 25, Academic Press, 305–353.

  • Dorman, J. L., and P. J. Sellers, 1989: A global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the Simple Biosphere Model (SiB). J. Appl. Meteor.,28, 833–855.

  • Duchon, C. E., and M. S. O’Malley, 1999: Estimating cloud type from pyranometer observations. J. Appl. Meteor.,38, 132–141.

  • Garratt, J. R., P. B. Krummel, and E. A. Kowalczyk, 1993: The surface energy balance at local and regional scales—A comparison of general circulation model results with observations. J. Climate,6, 1090–1109.

  • Grant, I. F., A. J. Prata, G. Rondeaux, and M. Stevens, 1999: Australian sites for the validation of satellite retrievals of the radiative properties of land surfaces. Proc. ENAMORS Workshop Optical Remote Sensing of Terrestrial Surfaces: New Sensors, Advanced Algorithms, and the Opportunity for Novel Applications, Tuusula, Finland, European Commission DGXII Environment and Climate Programme, in press.

  • Gutman, G., 1988: A simple method for estimating monthly mean albedo of land surfaces from AVHRR data. J. Appl. Meteor.,27, 973–988.

  • Irons, J. R., K. J. Ranson, and C. S. T. Daughtry, 1988: Estimating big bluestem albedo from directional reflectance measurements. Remote Sens. Environ.,25, 185–199.

  • Kandel, R., and Coauthors, 1998: The ScaRaB earth radiation budget dataset. Bull. Amer. Meteor. Soc.,79, 765–783.

  • Kimes, D. S., P. J. Sellers, and W. W. Newcomb, 1987: Hemispherical reflectance variations of vegetation canopies and implications for global and regional energy budget studies. J. Climate Appl. Meteor.,26, 959–972.

  • Kimes, D. S., A. G. Kerber, and P. J. Sellers, 1993: Spatial averaging errors in creating hemispherical reflectance (albedo) maps from directional reflectance data. Remote Sens. Environ.,45, 85–94.

  • Kriebel, K. T., 1979: Albedo of vegetated surfaces: Its variability with differing irradiances. Remote Sens. Environ.,8, 283–290.

  • Li, Z., and L. Garand, 1994: Estimation of surface albedo from space:A parameterization for global application. J. Geophys. Res.,99, 8335–8350.

  • Lucht, W., 1998: Expected retrieval accuracies of bidirectional reflectance and albedo from EOS-MODIS and MISR angular sampling. J. Geophys. Res.,103, 8763–8778.

  • Mason, I., and J. Delderfield, 1990: Scientific requirements and specification for the ATSR-2 on ERS-2. Rutherford Appleton Laboratories Rep. ER-RS-MSL-AT-2001, 35 pp. [Available from Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire OX11 0QX, United Kingdom.].

  • Minnis, P., S. Mayor, W. L. Smith Jr., and D. F. Young, 1997: Asymmetry in the diurnal variation of surface albedo. IEEE Trans. Geosci. Remote Sens.,35, 879–891.

  • Monteith, J. L., and G. Szeicz, 1961: The radiation balance of bare soil and vegetation. Quart. J. Roy. Meteor. Soc.,87, 159–170.

  • Mossavati, R., and Coauthors, 1998: Geostationary Earth Radiation Budget Experiment (GERB). Proc. Ninth Conf. on Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., 736–738.

  • Pinker, R. T., O. E. Thompson, and T. F. Eck, 1980: The albedo of a tropical evergreen forest. Quart. J. Roy. Meteor. Soc.,106, 551–558.

  • Pinty, B., and D. Raymond, 1987: A method for the estimate of broadband directional surface albedo from a geostationary satellite. J. Climate Appl. Meteor.,26, 1709–1722.

  • Prata, A. J., I. F. Grant, R. P. Cechet, and G. F. Rutter, 1998: Five years of shortwave radiation budget measurements at a continental land site in south-eastern Australia. J. Geophys. Res.,103, 26 093–26 106.

  • Rossow, W. B., and Y.-C. Zhang, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 2. Validation and first results. J. Geophys. Res.,100, 1167–1197.

  • Roujean, J.-L., M. Leroy, and P.-V. Deschamps, 1992: A bidirectional model of the Earth’s surface for the correction of remote sensing data. J. Geophys. Res.,97, 20 455–20 468.

  • Rutan, D., and T. Charlock, 1997: Spectral reflectance, directional reflectance, and broadband albedo of the Earth’s surface. Preprints, Ninth Conf. on Atmospheric Radiation, Long Beach, CA, Amer. Meteor. Soc., 466–470.

  • Salomonson, V. V., and W. E. Marlatt, 1971: Airborne measurements of reflected solar radiation. Remote Sens. Environ.,2, 1–8.

  • Saunders, R. W., 1990: The determination of broad band surface albedo from AVHRR visible and near-infrared radiances. Int. J. Remote Sens.,11, 49–67.

  • Schmetz, J., H. Woick, S. Tjemkes, and M. Rattenborg, 1998: From Meteosat to Meteosat Second Generation (MSG). Preprints, Ninth Conf. on Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., 335–338.

  • Toll, D. L., D. Shirey, and D. S. Kimes, 1997: NOAA AVHRR land surface albedo algorithm development. Int. J. Remote Sens.,18, 3761–3796.

  • Viollier, M., R. Kandel, and P. Raberanto, 1995: Inversion and space-time-averaging algorithms for ScaRaB (Scanner for the Earth Radiation Budget): Comparison with ERBE. Ann. Geophys.,13, 959–968.

  • Walthall, C. L., J. M. Norman, J. M. Welles, G. Campbell, and B. L. Blad, 1985: Simple equation to approximate the bidirectional reflectance from vegetative canopies and bare soil surfaces. Appl. Opt.,24, 383–387.

  • Wanner, W., A. H. Strahler, B. Hu, P. Lewis, J.-P. Muller, X. Li, C. L. Barker Schaaf, and M. J. Barnsley, 1997: Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm. J. Geophys. Res.,102, 17 143–17 161.

  • Whitlock, C. H., and Coauthors, 1995: First global WCRP shortwave surface radiation budget dataset. Bull. Amer. Meteor. Soc.,76, 905–922.

  • Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System experiment. Bull. Amer. Meteor. Soc.,77, 853–868.

  • Wild, M., A. Ohmura, H. Gilgen, and E. Roeckner, 1995: Validation of general circulation model radiative fluxes using surface observations. J. Climate,8, 1309–1324.

  • Young, D. F., and Coauthors, cited 1997: CERES Algorithm Theoretical Basis Document Subsystem 7.0—Time interpolation and synoptic flux computation for single and multiple satellites. NASA EOS CERES Doc., Release 2.2. [Available online at http://eospso.gsfc.nasa.gov/atbd/cerestables.html].

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 884 194 21
PDF Downloads 573 146 15