{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T08:56:47Z","timestamp":1767949007916,"version":"3.49.0"},"reference-count":78,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2013,12,27]],"date-time":"2013-12-27T00:00:00Z","timestamp":1388102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1\/12\u00b0 spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 \u00d7 40\u00b0), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well  inter-calibrated before being used concurrently.<\/jats:p>","DOI":"10.3390\/rs6010257","type":"journal-article","created":{"date-parts":[[2013,12,27]],"date-time":"2013-12-27T12:54:46Z","timestamp":1388148886000},"page":"257-284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"first","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, A-1190 Vienna, Austria"}]},{"given":"Anja","family":"Klisch","sequence":"additional","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, A-1190 Vienna, Austria"}]},{"given":"Matteo","family":"Mattiuzzi","sequence":"additional","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, A-1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-1700","authenticated-orcid":false,"given":"Francesco","family":"Vuolo","sequence":"additional","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Strasse 82, A-1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2013,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. 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