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TitleRetrieval of BRDF for pure landcover types from MODIS and MISR using an angular unmixing approach
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorTrishchenko, AORCID logo; Khlopenkov, K; Luo, Y
SourceWeather and Environmental Satellites; by Vonder Haar, T H (ed.); Huang, H -L A (ed.); Proceedings of SPIE, the International Society of Optical Engineering vol. 5549, 2004 p. 167-175,
Alt SeriesEarth Sciences Sector, Contribution Series 20043296
MeetingInternational symposium on optical science and technology, SPIE's 49th annual meeting; Denver; US; August 2 - 6, 2004
Mediapaper; on-line; digital
File formatpdf
Areasouthern Great Planes
Subjectsgeophysics; remote sensing; reflectance; modelling; Normalized Difference Vegetation Index (NDVI); Bi-directional Reflectance Distribution Function (BRDF); MODIS; MISR; Atmospheric Radiation Measurement (ARM); Southern Great Planes (SGP); Agriculture
Illustrationssatellite images; graphs
AbstractInformation about the surface bi-directional reflectance distribution function (BRDF) and albedo is required as a boundary condition for radiative transfer modeling, aerosol retrievals, cloud retrievals, and atmospheric modeling. The typical spatial resolution provided by MODIS and MISR standard surface products (~1km) is insufficient to measure the BRDF of the pure surface types, because most pixels at this scale correspond to mixed classes. We present an approach for the retrieval of the basic surface BRDFs from the observations of MODIS/Terra and MISR using an angular unmixing method. Our analysis is focused on the Atmospheric Radiation Measurement (ARM) Program area in the Southern Great Planes (SGP) region, which is a predominantly agricultural area with a few major crop types. Pure surface classes were identified using high-resolution (30m) Landsat imagery and results of a ground survey.

Assuming that the reflectance for each coarse pixel is a linear superposition of reflectances of basic surface types, it is possible to estimate the original BRDF parameters for each landcover type. In our case, three dominant classes were selected: wheat, grass, and baresoil. In the case of wheat and grass, the dispersion of the results is smaller than in the case of soil. This can be explained by the relatively low fractional coverage of the soil class within large pixels and by the significant variability of soil reflectance depending on wetness, soil type (sand, clay, etc.), and other factors. The correlation between the BRDF shape factors and the normalized difference vegetation index (NDVI) has also been analyzed. There is a high degree of correlation between the NDVI and BRDF isotropic factor (r0 in the case of MISR), while the correlation with other BRDF parameters was found to be smaller. In general, the NDVI can be used as a crude proxy for the BRDF shape

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