{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:37:38Z","timestamp":1773967058212,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFC3204400"],"award-info":[{"award-number":["2022YFC3204400"]}]},{"name":"National Key Research and Development Program of China","award":["32071583"],"award-info":[{"award-number":["32071583"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFC3204400"],"award-info":[{"award-number":["2022YFC3204400"]}]},{"name":"National Natural Science Foundation of China","award":["32071583"],"award-info":[{"award-number":["32071583"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000\u20132019 using the Carnegie\u2013Ames\u2013Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann\u2013Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC\/(m2\u00b7a) during 2000\u20132019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors\u2014particularly precipitation\u2014having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.<\/jats:p>","DOI":"10.3390\/rs16111966","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T06:08:14Z","timestamp":1717049294000},"page":"1966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Guangyu","family":"Lv","sequence":"first","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7194-8742","authenticated-orcid":false,"given":"Lei","family":"Fang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"given":"Yanbo","family":"Peng","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"},{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Chuanxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Jianyu","family":"Yao","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7806-6179","authenticated-orcid":false,"given":"Shilong","family":"Ren","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6912-435X","authenticated-orcid":false,"given":"Jinyue","family":"Chen","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"given":"Jilin","family":"Men","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"given":"Qingzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-1056","authenticated-orcid":false,"given":"Guoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"},{"name":"Innovation Research Center for Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Qiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4015","DOI":"10.1073\/pnas.1700304115","article-title":"Climate Change, Human Impacts, and Carbon Sequestration in China","volume":"115","author":"Fang","year":"2018","journal-title":"Proc. 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