{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T14:59:43Z","timestamp":1781708383246,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A2014"],"award-info":[{"award-number":["U21A2014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701503"],"award-info":[{"award-number":["41701503"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101324"],"award-info":[{"award-number":["42101324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["232300420160"],"award-info":[{"award-number":["232300420160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["U21A2014"],"award-info":[{"award-number":["U21A2014"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["41701503"],"award-info":[{"award-number":["41701503"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["42101324"],"award-info":[{"award-number":["42101324"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["232300420160"],"award-info":[{"award-number":["232300420160"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China\u2019s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio\u2013temporal characteristics of plants\u2019 growth and extreme climate, together with the reaction of plants\u2019 growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC\u00b7m\u22122) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi\u2013year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross\u2013correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants\u2019 growth, and also their reaction to extreme climates, and offer essential support for evidence\u2013based ecological management practices in the Yellow River Basin.<\/jats:p>","DOI":"10.3390\/rs15194683","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T04:15:38Z","timestamp":1695615338000},"page":"4683","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Vegetation Dynamics and Its Trends Associated with Extreme Climate Events in the Yellow River Basin, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Yanping","family":"Cao","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Region, Ministry of Education, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zunyi","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Region, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhe","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyang","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Totop Technology Co., Ltd., Beijing 100043, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2017.11.013","article-title":"Changes in global vegetation activity and its driving factors during 1982\u20132013","volume":"249","author":"Zhao","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2021MS002802","DOI":"10.1029\/2021MS002802","article-title":"Machine Learning-Based Modeling of Vegetation Leaf Area Index and Gross Primary Productivity across North America and Comparison with a Process-Based Model","volume":"13","author":"Zhang","year":"2021","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecolind.2014.07.031","article-title":"Spatio-temporal analysis of vegetation variation in the Yellow River Basin","volume":"51","author":"Jiang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_4","first-page":"44","article-title":"Urbanization effects on vegetation cover in major African cities during 2001\u20132017","volume":"75","author":"Yao","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","unstructured":"IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"D05109","DOI":"10.1029\/2005JD006290","article-title":"Global observed changes in daily climate extremes of temperature and precipitation","volume":"111","author":"Alexander","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1755-1315\/697\/1\/012005","article-title":"Spatial-Temporal Change in Vegetation Cover and Climate Factor Drivers of Variation in the Haihe River Basin 2003-2016","volume":"697","author":"Li","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s00704-016-1783-0","article-title":"Recent changes of precipitation in Gansu, Northwest China: An index-based analysis","volume":"129","author":"Li","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shi, G., and Ye, P. (2021). Assessment on Temporal and Spatial Variation Analysis of Extreme Temperature Indices: A Case Study of the Yangtze River Basin. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182010936"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"144244","DOI":"10.1016\/j.scitotenv.2020.144244","article-title":"Changes in precipitation extremes in the Yangtze River Basin during 1960\u20132019 and the association with global warming, ENSO, and local effects","volume":"760","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s00704-018-2519-0","article-title":"Spatiotemporal extremes of temperature and precipitation during 1960\u20132015 in the Yangtze River Basin (China) and impacts on vegetation dynamics","volume":"136","author":"Cui","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"142567","DOI":"10.1016\/j.scitotenv.2020.142567","article-title":"Impacts of extreme climate on Australia\u2019s green cover (2003\u20132018): A MODIS and mascon probe","volume":"766","author":"Saleem","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"140784","DOI":"10.1016\/j.scitotenv.2020.140784","article-title":"Vegetation responses to extreme climatic indices in coastal China from 1986 to 2015","volume":"744","author":"Xu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"054010","DOI":"10.1088\/1748-9326\/ab154b","article-title":"The effects of climate extremes on global agricultural yields","volume":"14","author":"Vogel","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"084005","DOI":"10.1088\/1748-9326\/ab93fa","article-title":"Vulnerability of vegetation activities to drought in Central Asia","volume":"15","author":"Deng","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"025009","DOI":"10.1088\/1748-9326\/8\/2\/025009","article-title":"Global patterns of NDVI-indicated vegetation extremes and their sensitivity to climate extremes","volume":"8","author":"Liu","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6911","DOI":"10.1038\/ncomms7911","article-title":"Leaf onset in the northern hemisphere triggered by daytime temperature","volume":"6","author":"Piao","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.isprsjprs.2022.12.006","article-title":"Recent advances in using Chinese Earth observation satellites for remote sensing of vegetation","volume":"195","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Orusa, T., Viani, A., Moyo, B., Cammareri, D., and Borgogno-Mondino, E. (2023). Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta\u00ae Population Dataset: An Application in Aosta Valley, NW Italy. Remote Sens., 15.","DOI":"10.3390\/rs15092348"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cosust.2009.07.012","article-title":"Global urban land-use trends and climate impacts","volume":"1","author":"Seto","year":"2009","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109323","DOI":"10.1016\/j.agrformet.2023.109323","article-title":"Susceptibility of vegetation low-growth to climate extremes on Tibetan Plateau","volume":"331","author":"Zhang","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mo, Y., Zhang, X., Liu, Z., Zhang, J., Hao, F., and Fu, Y. (2023). Effects of Climate Extremes on Spring Phenology of Temperate Vegetation in China. Remote Sens., 15.","DOI":"10.3390\/rs15030686"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"159942","DOI":"10.1016\/j.scitotenv.2022.159942","article-title":"Multifaceted responses of vegetation to average and extreme climate change over global drylands","volume":"858","author":"He","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, Y., Yu, M., Wei, J., and Zhou, B. (2023). Impacts of Extreme Climates on Vegetation at Middle-to-High Latitudes in Asia. Remote Sens., 15.","DOI":"10.3390\/rs15051251"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e01813","DOI":"10.1016\/j.gecco.2021.e01813","article-title":"Relationship between extreme climate indices and spatiotemporal changes of vegetation on Yunnan Plateau from 1982 to 2019","volume":"31","author":"Yan","year":"2021","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"908945","DOI":"10.3389\/frwa.2022.908945","article-title":"Grain yield and food security evaluation in the yellow river basin under climate change and water resources constraints","volume":"4","author":"Niu","year":"2022","journal-title":"Front. Water"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109409","DOI":"10.1016\/j.ecolind.2022.109409","article-title":"Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China","volume":"143","author":"Zhan","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s00382-021-05767-z","article-title":"Extreme climate changes over three major river basins in China as seen in CMIP5 and CMIP6","volume":"57","author":"Zhu","year":"2021","journal-title":"Clim. Dyn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108818","DOI":"10.1016\/j.ecolind.2022.108818","article-title":"Detection of vegetation coverage changes in the Yellow River Basin from 2003 to 2020","volume":"138","author":"Liu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"128651","DOI":"10.1016\/j.jhydrol.2022.128651","article-title":"Ecohydrological decoupling of water storage and vegetation attributed to China\u2019s large-scale ecological restoration programs","volume":"615","author":"Cao","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133553","DOI":"10.1016\/j.scitotenv.2019.07.359","article-title":"Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China","volume":"693","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1007\/s00704-014-1138-7","article-title":"The Yellow River basin becomes wetter or drier? The case as indicated by mean precipitation and extremes during 1961\u20132012","volume":"119","author":"Liang","year":"2015","journal-title":"Theor. Appl. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Q., Cao, Y., Miao, S., and Huang, X. (2022). Spatiotemporal Characteristics of Drought and Wet Events and Their Impacts on Agriculture in the Yellow River Basin. Land, 11.","DOI":"10.3390\/land11040556"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1755-1315\/687\/1\/012002","article-title":"Quantitative Assessment of NPP Changes in the Yellow River Source Area from 2001 to 2017","volume":"687","author":"Rong","year":"2021","journal-title":"IOP Conf. Ser. Environ. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Peng, D., Wu, C., Zhang, B., Huete, A., Zhang, X., Sun, R., Lei, L., Huang, W., Liu, L., and Liu, X. (2016). The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158173"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cui, T., Wang, Y., Sun, R., Qiao, C., Fan, W., Jiang, G., Hao, L., and Zhang, L. (2016). Estimating vegetation primary production in the Heihe River Basin of China with multi-source and multi-scale data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153971"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yu, T., Sun, R., Xiao, Z., Zhang, Q., Liu, G., Cui, T., and Wang, J. (2018). Estimation of global vegetation productivity from global land surface satellite data. Remote Sens., 10.","DOI":"10.3390\/rs10020327"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, R., Zhu, A., and Xiao, Z. (2020). Evaluation and comparison of light use efficiency and gross primary productivity using three different approaches. Remote Sens., 12.","DOI":"10.3390\/rs12061003"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"122396","DOI":"10.1016\/j.jclepro.2020.122396","article-title":"Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia","volume":"271","author":"Luo","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4217","DOI":"10.3390\/rs6054217","article-title":"Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.5194\/essd-12-1217-2020","article-title":"Annual dynamics of global land cover and its long-term changes from 1982 to 2015","volume":"12","author":"Liu","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s00704-023-04454-9","article-title":"Spatiotemporal changes of extreme climate indices and their influence and response factors in a typical cold river basin in Northeast China","volume":"152","author":"Ren","year":"2023","journal-title":"Theor. Appl. Climatol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, S., Bai, X., Tan, Q., Li, Q., Wu, L., Tian, S., Hu, Z., Li, C., and Deng, Y. (2019). Factors Affecting Long-Term Trends in Global NDVI. Forests, 10.","DOI":"10.3390\/f10050372"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhang, Z., Kong, R., Chang, J., Tian, J., Zhu, B., Jiang, S., Chen, X., and Xu, C.-Y. (2019). Changes in Forest Net Primary Productivity in the Yangtze River Basin and Its Relationship with Climate Change and Human Activities. Remote Sens., 11.","DOI":"10.3390\/rs11121451"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, H., Li, N., Zhu, J., Pan, Z., and Qin, F. (2020). Spatial and Temporal Characteristics and Driving Forces of Vegetation Changes in the Huaihe River Basin from 2003 to 2018. Sustainability, 12.","DOI":"10.3390\/su12062198"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ndlovu, M., Clulow, A.D., Savage, M.J., Nhamo, L., Magidi, J., and Mabhaudhi, T. (2021). An Assessment of the Impacts of Climate Variability and Change in KwaZulu-Natal Province, South Africa. Atmosphere, 12.","DOI":"10.3390\/atmos12040427"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sun, X., Li, G., Wang, J., and Wang, M. (2021). Quantifying the Land Use and Land Cover Changes in the Yellow River Basin while Accounting for Data Errors Based on GlobeLand30 Maps. Land, 10.","DOI":"10.3390\/land10010031"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ji, Q., Liang, W., Fu, B., Zhang, W., Yan, J., L\u00fc, Y., Yue, C., Jin, Z., Lan, Z., and Li, S. (2021). Mapping Land Use\/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13071299"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"110083","DOI":"10.1016\/j.ecolind.2023.110083","article-title":"Vegetation dynamics alter the hydrological interconnections between upper and mid-lower reaches of the Yellow River Basin, China","volume":"148","author":"Wang","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"892747","DOI":"10.3389\/fenvs.2022.892747","article-title":"Vegetation Dynamics and its Response to Climate Change in the Yellow River Basin, China","volume":"10","author":"Zhan","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e01299","DOI":"10.1016\/j.gecco.2020.e01299","article-title":"Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982\u20132014","volume":"24","author":"Lamchin","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4367","DOI":"10.1111\/gcb.15729","article-title":"Annual precipitation explains variability in dryland vegetation greenness globally but not locally","volume":"27","author":"Ukkola","year":"2021","journal-title":"Glob. Change Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.ecolind.2018.09.034","article-title":"Spatiotemporal variations of extreme climate events in Northeast China during 1960\u20132014","volume":"96","author":"Guo","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"110247","DOI":"10.1016\/j.ecolind.2023.110247","article-title":"Divergent vegetation variation and the response to extreme climate events in the National Nature Reserves in Southwest China, 1961\u20132019","volume":"150","author":"Wang","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100554","DOI":"10.1016\/j.wace.2023.100554","article-title":"Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change","volume":"40","author":"Yaseen","year":"2023","journal-title":"Weather Clim. Extrem."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.1038\/s41467-021-24262-x","article-title":"Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets","volume":"12","author":"Madakumbura","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s41612-022-00255-5","article-title":"Changes in temporal inequality of precipitation extremes over China due to anthropogenic forcings","volume":"5","author":"Duan","year":"2022","journal-title":"npj Clim. Atmos. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2981","DOI":"10.1002\/joc.7402","article-title":"Anthropogenic influence on extreme temperatures in China based on CMIP6 models","volume":"42","author":"Hu","year":"2022","journal-title":"Int. J. Climatol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1080\/01431160210154812","article-title":"Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA","volume":"24","author":"Wang","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"108842","DOI":"10.1016\/j.agrformet.2022.108842","article-title":"The role of climate change and vegetation greening on evapotranspiration variation in the Yellow River Basin, China","volume":"316","author":"Zhao","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41561-022-01114-x","article-title":"Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends","volume":"16","author":"Higgins","year":"2023","journal-title":"Nat. Geosci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/s43017-023-00392-2","article-title":"Climate extremes drive negative vegetation growth","volume":"4","author":"Leake","year":"2023","journal-title":"Nat. Rev. Earth Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4683\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:57:21Z","timestamp":1760129841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4683"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":62,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194683"],"URL":"https:\/\/doi.org\/10.3390\/rs15194683","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]}}}