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A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
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2016
Source: Remote Sens. 8(3), 1-12
[PDF-2.88 MB]
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Journal Title:Remote Sensing
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Personal Author:
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NOAA Program & Office:
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Description:In-land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.
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Source:Remote Sens. 8(3), 1-12
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Document Type:
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Funding:
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Rights Information:CC BY
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Compliance:Library
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Main Document Checksum:urn:sha256:283598d5f354bceeaa69570ab9d64d991466546bca4252dc75892b1aed72601d
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Supporting Files
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