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Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data
Abstract
Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina. We first simulated the total at-sensor radiances using MODTRAN 4 for atmospheric correction. Then ground spectroradiometric measurements were used to improve the retrieved reflectance for each pixel on the Hyperion image. Using the improved pixel-based surface reflectance spectra, 12 two-band "vegetation indices (VIs)" were constructed using all available 168 Hyperion bands. Finally, we evaluated the correlation of each possible vegetation index with LAI measurements to determine the most effective bands for forest LAI estimation. The experimental results indicate that most of the important hyperspectral bands with high R/sup 2/ are related to bands in the shortwave infrared (SWIR) region and some in the near-infrared (NIR) region. The bands are centered near 820, 1040, 1200, 1250, 1650, 2100, and 2260 nm with bandwidths ranging from 10-300 nm. It is notable that the originally defined VIs that use red and NIR bands did not produce higher correlation with LAI than VIs constructed with bands in SWIR and NIR regions.
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Metadata
- Authors
- Gong, P., Pu, R., Biging, G. S., & Larrieu, M. R.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2003-06
- Volume
- 41
- Issue
- 6
- Pages
- 1355-1362
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2151880387
- Cited by
- 436
- References
- 0
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. https://app.cochat.ai/papers/10.1109/tgrs.2003.812910
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- Gong, P., Pu, R., Biging, G. S., & Larrieu, M. R. (2003). Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2003.812910
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
