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TitleRetrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument
AuthorLacaze, R; Chen, J M; Roujean, J -L; Leblanc, S GORCID logo
SourceRemote Sensing of Environment 79, 1, 2002 p. 84-95, Open Access logo Open Access
Alt SeriesEarth Sciences Sector, Contribution Series 20043052
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
AbstractThe potential use of the information from a sampling of the bidirectional reflectance distribution function (BRDF) has suffered from the lack of solid applications in ecology, where it is expected to play the role of an advanced descriptor of vegetation as a complement to hyperspectral measurements. Such a shortcoming stems from the lack of consistent angular data sets with an adequate resolution at global scale. In this context, the POLDER instrument is particularly relevant because it acquires directional radiance signatures at a high angular resolution and thereby provides the first global BRDF product. In this paper, we investigate how to discriminate vegetation types in using only a portion of the BRDF, in particular, the two paramount directional signatures, which are the maximum (hot spot) and the minimum (dark spot) of reflectance observed in the backscattering and forward scattering regions, respectively. A directional index hot¯dark spot (HDS) is formulated using these two signatures. It is defined as the normalized difference between the reflectances at the hot spot and dark spot. It is shown that the HDS can be linearly related with the foliage clumping index for three different vegetation types observed by the spaceborne POLDER sensor. The significance of the clumping index mapping for ecological studies is evaluated using the Boreal Ecosystem Productivity Simulator (BEPS). Considering foliage clumping in BEPS, the estimation of daily canopy photosynthesis can differ about 20% for a black spruce site. In this context, it is expected that the findings of this study will have a strong impact on the use of directional optical remote sensing to improve the assessment of terrestrial productivity and carbon cycle.

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