{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:56:49Z","timestamp":1782817009073,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2019YFA0606902"],"award-info":[{"award-number":["2019YFA0606902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVI3g) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30\u00b0N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45\u00b0N to 60\u00b0N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (p &lt; 0.05), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30\u00b0N.<\/jats:p>","DOI":"10.3390\/rs15102559","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T02:02:11Z","timestamp":1684116131000},"page":"2559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluating the Consistency of Vegetation Phenological Parameters in the Northern Hemisphere from 1982 to 2015"],"prefix":"10.3390","volume":"15","author":[{"given":"Xigang","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaning","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2013.11.020","article-title":"Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type","volume":"143","author":"Jeganathan","year":"2014","journal-title":"Remote Sens. 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