{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:55:34Z","timestamp":1775760934465,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,22]],"date-time":"2016-03-22T00:00:00Z","timestamp":1458604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 (&gt;4.5) and FAPAR (&gt;0.8) values.<\/jats:p>","DOI":"10.3390\/rs8030263","type":"journal-article","created":{"date-parts":[[2016,3,22]],"date-time":"2016-03-22T11:50:37Z","timestamp":1458647437000},"page":"263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":143,"title":["A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation"],"prefix":"10.3390","volume":"8","author":[{"given":"Martin","family":"Claverie","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Jessica","family":"Matthews","sequence":"additional","affiliation":[{"name":"NOAA\u2019s National Centers for Environmental Information (NCEI), 151 Patton Avenue, Asheville, NC 28801, USA"},{"name":"Cooperative Institute for Climate and Satellites\u2013NC (CICS-NC), North Carolina State University, 151 Patton Avenue, Asheville, NC 28801, USA"}]},{"given":"Eric","family":"Vermote","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Christopher","family":"Justice","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,22]]},"reference":[{"key":"ref_1","unstructured":"Gobron, N., and Verstraete, M. 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