International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B1. Istanbul 2004
largest relative differences were mostly found in the blue
spectral band, with very few exceptions.
Comparing the relative HDRF-BRF differences with regard to
the viewing direction, there is a clear trend of higher
differences for the forward looking camera. Further we
investigated the relative differences of the HDRF-BRF
values with regard to the nine cameras. For most scenes and
spectral bands, the Ba camera (view zenith of 45.6°) shows
the smallest differences. This indicates, that whenever the
hemispherical irradiance component is neglected and HDRF
data are equated with BRF data, the introduced uncertainties
can be reduced by applying off-nadir data in the backward
scattering direction, instead of nadir data.
4. CONCLUSION
All remote sensing data depend on the illumination and
view geometry of the sensor, as well as on their opening
angle. Different reflectance quantities have been defined to
describe the corresponding conditions of the measurements.
The basis for the proper use of these reflectance quantities is
a standardized nomenclature, well known throughout the
remote sensing community. This study summarized the
nomenclature articles of Nicodemus (1977) and Martonchik
(2000) to give an easy access to the concept.
Further the importance of using the adequate reflectance
product is shown. All reflectance measurements performed
under natural conditions include a diffuse fraction. Its
amount is a function of the atmospheric conditions, the
topography, the surroundings of the observed surface, and
the wavelength. It thus introduces spectral effects to
spectrometer data. The presented case studies are
concentrating on the opening angle of the illumination,
restricting it to directional irradiance only, or allowing for a
diffuse irradiance component. The effect of varying direct to
diffuse irradiance ratio is significant in modelled data, as
well as in analysed MISR reflectance products.
This study is addressing different remote sensing
communities. It shows that the use of any remote sensing
data has to include the analysis of the corresponding
illumination and view geometry, and the opening angle, as a
prerequisite for any further analysis. This will explain many
unexpected results and significantly reduce uncertainties.
Some satellite products of the MODIS and MISR sensor
account for these considerations. It is the responsibility of
the users to choose for the adequate product, guided by the
reflectance nomenclature. Further, state of the art models
allow users to account for the anisotropy of the target and
the illumination conditions.
The publication shall motivate the remote sensing
community to take reflectance nomenclature into account
and use the presented common basis for the sake of
clarification and comparability. Even though not all
applications of remote sensing data take the directionality
of the reflectance signal into account, the appropriate
selection and denomination of the used reflectance quantity
is a prerequisite for every scientific publication.
5. ACKNOWLEDGEMENT
This. work was supported by the Swiss National Science
Foundation, under contract 200020-101517. MISR data were
obtained from the NASA Langley Research Center
Atmospheric Sciences Data Center.
6. REFERENCE
Breuer, L., K. Eckhardt, H. Frede, Plant parameter values for
models in temperate climates, Ecol Model, 169 (2-3), 237-
293, 2003.
Deering, D., S. Ahmad, T. Eck, B. Banerjee, Temporal
Attributes of the Bidirectional Reflectance for three Boreal
forest canopies, /GARSS, pp. 1239-1241, Florence, Italy,
1995.
Diner, D., J. Martonchik, C. Borel, S. Gerstl, H. Gordon, Y.
Knyazikhin, R. Myneni, B. Pinty, M. Verstraete, Multi-angle
Imaging Spectro-Radiometer Level 2 Surface Retrieval
Algorithm Theoretical Basis, JPL D-11401, Rev. D, December
2, 1999, JPL, California Institute of Technology, 1999.
Engelsen, O., B. Pinty,..M. Verstraete, ,].. Martonchik,
Parametric bidirectional reflectance factor models:
evaluation, improvements and applications, pp. 114, EC
JRC, Ispra, Italy, 1996.
Green, R., J. Dozier, D. Roberts, T. Painter, Spectral snow-
reflectance models for grain-size and liquid-water fraction in
melting snow for the solar-reflected spectrum, Ann Glaciol,
34, 2002, 34, 71-73, 2002.
Kimes, D., Dynamics of Directional Reflectance Factor
Distributions for Vegetation Canopies, Appl Optics, 22 (9),
1364-1372, 1983.
Lyapustin, A., J. Privette, A new method of retrieving surface
bidirectional reflectance from ground measurements:
Atmospheric sensitivity study, J Geophys Res-Atmos, 104
(D6), 6257-6268, 1999.
Macke, A., M. Mishchenko, Applicability of regular particle
shapes in light scattering calculations for atmospheric ice
particles, Appl Optics, 35 (21), 4291-4296, 1996.
Martonchik, J., Retrieval of Surface Directional Reflectance
Properties Using Ground-Level Multiangle Measurements,
Remote Sens Environ, 50 (3), 303-316, 1994.
Martonchik, J.V., D.J. Diner, B. Pinty, M.M. Verstraete, R.B.
Myneni, Y. Knyazikhin, and H.R. Gordon, Determination of
land and ocean reflective, radiative, and biophysical
properties using multiangle imaging, /eee T Geosci Remote,
36 (4), 1266-1281, 1998.
Martonchik, J., C. Bruegge, A. Strahler, A review of
reflectance nomenclature used in remote sensing, Remote
Sensing Reviews, 19, 9-20, 2000.
Nicodemus, F., J. Richmond, J. Hsia, I. Ginsberg, T. Limperis,
Geometrical Considerations and Nomenclature for
Reflectance, NBS, US Department of Commerce, Washington,
DC. 1977.
Nolin, A., J. Dozier, A hyperspectral method for remotely
sensing the grain size of snow, Remote Sens Environ, 74 (2),
207-216, 2000.
Stamnes, K., S. Tsay, W. Wiscombe, K. Jayaweera,
Numerically Stable Algorithm for Discrete-Ordinate-Method
Radiative-Transfer in Multiple-Scattering and Emitting
Layered Media, Appl Optics, 27 (12), 2502-2509, 1988.
Vermote, E., A. Vermeulen, Atmospheric correction
algorithm: Spectral reflectances (MOD09), MODIS ATBD, pp.
107, Univ. of Maryland, Dept. of Geography, 1999.
Warren, S., Optical-Properties of Snow, Rev Geophys, 20 (1),
67-89, 1982.
KI
AI
Th
wi
de:
lat
uti
co
M
the
to
