{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:27:59Z","timestamp":1772180879762,"version":"3.50.1"},"reference-count":111,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Key Project of PCL","award":["2020YFA0608703"],"award-info":[{"award-number":["2020YFA0608703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC\u00b7m\u22122. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC\u00b7m\u22122\/d for EVI vs. 0.63 gC\u00b7m\u22122\/d for LAI vs. 0.30 gC\u00b7m\u22122\/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change.<\/jats:p>","DOI":"10.3390\/rs16214101","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T09:52:54Z","timestamp":1730713974000},"page":"4101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Hanmin","family":"Yin","sequence":"first","affiliation":[{"name":"Pengcheng Laboratory, Shenzhen 518000, China"},{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of the Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9456-0065","authenticated-orcid":false,"given":"Xiaofei","family":"Ma","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"}]},{"given":"Xiaohan","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of the Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9114-205X","authenticated-orcid":false,"given":"Huping","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of the Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2348-5050","authenticated-orcid":false,"given":"Wentao","family":"Yu","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6243-5880","authenticated-orcid":false,"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Junbo","family":"Wei","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory, Shenzhen 518000, China"}]},{"given":"Jincheng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory, Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5302-9849","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory, Shenzhen 518000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. 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