Title of article :
Spectral unmixing model to assess land cover fractions in Mongolian steppe regions
Author/Authors :
Byambakhuu، نويسنده , , Ishgaldan and Sugita، نويسنده , , Michiaki and Matsushima، نويسنده , , Dai، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2010
Abstract :
The land cover fractions (LCFs) and spectral reflectance of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil were measured at 58 sites in semi-arid and arid regions of Mongolia in the summers of 2005 and 2006. These data sets allowed a detailed assessment of the impact of measurement geometry as represented by the solar zenith angle θs, sensor view zenith angle θv and azimuth view angle ϕ in the estimation of LCF values by means of the spectral unmixing model (SUM). The bidirectional distribution function (BRDF) was fitted to the reflectance data and then used to produce reflectance at various measurement geometries. LCFs from these reflectance data for a given combination of θs, θv, and ϕ were compared with visually determined LCFs. It was found that θs in the range of 30–45° produced a better agreement of LCFs. For θv, the agreement is not very sensitive to the choice of angle for the range 30–70°, although θv = 50° showed a slightly better performance. The azimuth view angle does not have strong influences to the LCF estimation, except for the case of ϕ = 180° (view toward the sun), which does not allow precise fitting of BRDF function over a tall vegetation site. Overall, this study verified the results of earlier studies obtained mostly for the American continents that SUM is capable of producing LCF estimates accurately and also found that its accuracy was, in general, much better than that by the more traditional approach of the supervised classification method (SCM) applied to images of a digital camera.
Keywords :
Mongolia , Semi-arid and arid area , BRDF , Viewing geometry , Land cover fractions , Spectral unmixing model
Journal title :
Remote Sensing of Environment
Journal title :
Remote Sensing of Environment