A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
AbstractIn- 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. View Full-Text
Share & Cite This Article
Claverie, M.; Matthews, J.L.; Vermote, E.F.; Justice, C.O. A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation. Remote Sens. 2016, 8, 263.
Claverie M, Matthews JL, Vermote EF, Justice CO. A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation. Remote Sensing. 2016; 8(3):263.Chicago/Turabian Style
Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O. 2016. "A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation." Remote Sens. 8, no. 3: 263.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.