{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:47:19Z","timestamp":1772524039885,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"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":["41901385"],"award-info":[{"award-number":["41901385"]}],"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":["2022JJ40873"],"award-info":[{"award-number":["2022JJ40873"]}],"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":["21A0177"],"award-info":[{"award-number":["21A0177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province of China","award":["41901385"],"award-info":[{"award-number":["41901385"]}]},{"name":"Natural Science Foundation of Hunan Province of China","award":["2022JJ40873"],"award-info":[{"award-number":["2022JJ40873"]}]},{"name":"Natural Science Foundation of Hunan Province of China","award":["21A0177"],"award-info":[{"award-number":["21A0177"]}]},{"name":"Education Department of Hunan Province of China","award":["41901385"],"award-info":[{"award-number":["41901385"]}]},{"name":"Education Department of Hunan Province of China","award":["2022JJ40873"],"award-info":[{"award-number":["2022JJ40873"]}]},{"name":"Education Department of Hunan Province of China","award":["21A0177"],"award-info":[{"award-number":["21A0177"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001\u20132020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C\/m2, with a mean value of 72.75 g C\/m2, showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001\u20132005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010\u20132015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China.<\/jats:p>","DOI":"10.3390\/rs15112871","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T02:12:45Z","timestamp":1685585565000},"page":"2871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors"],"prefix":"10.3390","volume":"15","author":[{"given":"Shuaiyang","family":"Qi","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Huaiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Forestry, Beijing 100091, China"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hu, Q., and Zou, F. (2021). Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Qinghai-Tibetan Plateau from 2001 to 2017. Remote Sens., 13.","DOI":"10.3390\/rs13081566"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, G., Shao, Q., Fan, J., Ning, J., Rong, K., Huang, H., Liu, S., Zhang, X., Niu, L., and Liu, J. (2022). Change Trend and Restoration Potential of Vegetation Net Primary Productivity in China over the Past 20 Years. Remote Sens., 14.","DOI":"10.3390\/rs14071634"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.scitotenv.2018.10.295","article-title":"Evaluating the Responses of Net Primary Productivity and Carbon Use Efficiency of Global Grassland to Climate Variability along an Aridity Gradient","volume":"652","author":"Liu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108409","DOI":"10.1016\/j.ecolind.2021.108409","article-title":"Land Use Change Induced by the Implementation of Ecological Restoration Programs Increases Future Terrestrial Ecosystem Carbon Sequestration in Red Soil Hilly Region of China","volume":"133","author":"Ning","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_5","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_6","unstructured":"Redmann, R.E. (2008). Encyclopedia of Entomology, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Running, S.W., and Hunt, E.R. (1993). Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models, Woodhead Publishing Limited.","DOI":"10.1016\/B978-0-12-233440-5.50014-2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1029\/1999GB001206","article-title":"Atmospheric Disturbances I Mineralization \u2022 Canopy Radiation Model C, N CO:On C-Budget","volume":"14","author":"Chen","year":"2000","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1029\/97GB00059","article-title":"Equilibrium Responses of Global Net Primary Production and Carbon Storage to Doubled Atmospheric Carbon Dioxide: Sensitivity to Changes in Vegetation Nitrogen Concentration","volume":"11","author":"Melillo","year":"1997","journal-title":"Global Biogeochem. Cycles"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1002\/eco.104","article-title":"Ecohydrology Bearing-Invited Commentary Transformation Ecosystem Change and Ecohydrology: Ushering in a New Era for Watershed Management","volume":"130","author":"Wilcox","year":"2010","journal-title":"Ecohydrology"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, C., Jiang, Q., Deng, X., Lv, K., and Zhang, Z. (2020). Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China. Remote Sens., 12.","DOI":"10.3390\/rs12213670"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.asr.2022.07.068","article-title":"Quantitative Contribution of Climate Change and Anthropological Activities to Vegetation Carbon Storage in the Dongting Lake Basin in the Last Two Decades","volume":"71","author":"Qi","year":"2023","journal-title":"Adv. Sp. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109724","DOI":"10.1016\/j.ecolind.2022.109724","article-title":"Novel Model for NPP Prediction Based on Temperature and Land Use Changes: A Case in Sichuan and Chongqing, China","volume":"145","author":"Zhou","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yin, C., Luo, M., Meng, F., Sa, C., Yuan, Z., and Bao, Y. (2022). Contributions of Climatic and Anthropogenic Drivers to Net Primary Productivity of Vegetation in the Mongolian Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14143383"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"84","article-title":"Modeling Net Primary Productivity of Terrestrial Ecosystems in the Semi-Arid Climate of the Mongolian Plateau Using LSWI-Based CASA Ecosystem Model","volume":"46","author":"Bao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liang, L., Geng, D., Yan, J., Qiu, S., Shi, Y., Wang, S., Wang, L., Zhang, L., and Kang, J. (2022). Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China. Remote Sens., 14.","DOI":"10.3390\/rs14081902"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0020-1693(00)85959-9","article-title":"Migration Behaviour and Chemical Speciation of Np and Am under Nuclear Waste Repository Conditions","volume":"95","author":"Bidoglio","year":"1984","journal-title":"Inorg. Chim. Acta"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, L., Huang, C., and Qiao, N. (2017). An NDVI-Based Vegetation Phenology Is Improved to Be More Consistent with Photosynthesis Dynamics through Applying a Light Use Efficiency Model over Boreal High-Latitude Forests. Remote Sens., 9.","DOI":"10.3390\/rs9070695"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108057","DOI":"10.1016\/j.ecolind.2021.108057","article-title":"Better Revisiting Chlorophyll Content Retrieval with Varying Senescent Material and Solar-Induced Chlorophyll Fluorescence Simulation on Paddy Rice during the Entire Growth Stages","volume":"130","author":"Shan","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107260","DOI":"10.1016\/j.compag.2022.107260","article-title":"Downscaling Solar-Induced Chlorophyll Fluorescence for Field-Scale Cotton Yield Estimation by a Two-Step Convolutional Neural Network","volume":"201","author":"Kang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"eabc7447","DOI":"10.1126\/sciadv.abc7447","article-title":"A Unified Vegetation Index for Quantifying the Terrestrial Biosphere","volume":"7","author":"Walther","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, S., Zhu, L., Luo, Z., and Qiu, H. (2023). Quantitative Analysis of the Contributions of Climatic and Anthropogenic Factors to the Variation in Net Primary Productivity, China. Remote Sens., 15.","DOI":"10.3390\/rs15030789"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"153951","DOI":"10.1016\/j.scitotenv.2022.153951","article-title":"Spatial-Temporal Variations of Terrestrial Evapotranspiration across China from 2000 to 2019","volume":"825","author":"Fu","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109914","DOI":"10.1016\/j.ecolind.2023.109914","article-title":"Effects of Land Use Patterns on the Interannual Variations of Carbon Sinks of Terrestrial Ecosystems in China","volume":"146","author":"Liu","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108963","DOI":"10.1016\/j.ecolind.2022.108963","article-title":"Projected Global Warming-Induced Terrestrial Ecosystem Carbon across China under SSP Scenarios","volume":"139","author":"Kong","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_30","first-page":"1276","article-title":"Optimization of Net Primary Productivity Estimation Model for Terrestrial Vegetation in China Based on CERN Data","volume":"42","author":"Su","year":"2022","journal-title":"Acta Ecol. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11434-006-0457-1","article-title":"Simulation of Maximum Light Use Efficiency for Some Typical Vegetation Types in China","volume":"51","author":"Zhu","year":"2006","journal-title":"Chinese Sci. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yin, C., Chen, X., Luo, M., Meng, F., Sa, C., Bao, S., Yuan, Z., Zhang, X., and Bao, Y. (2023). Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau. Remote Sens., 15.","DOI":"10.3390\/rs15081986"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Y., Xie, Z., Qin, Y., and Zheng, Z. (2019). Estimating Relations of Vegetation, Climate Change, and Human Activity: A Case Study in the 400 Mm Annual Precipitation Fluctuation Zone, China. Remote Sens., 11.","DOI":"10.3390\/rs11101159"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.scitotenv.2017.12.317","article-title":"Dynamic Analysis of Pan Evaporation Variations in the Huai River Basin, a Climate Transition Zone in Eastern China","volume":"625","author":"Li","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108544","DOI":"10.1016\/j.ecolind.2022.108544","article-title":"Quantitative Estimation of the Factors Impacting Spatiotemporal Variation in NPP in the Dongting Lake Wetlands Using Landsat Time Series Data for the Last Two Decades","volume":"135","author":"Zhang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_36","first-page":"1219","article-title":"Analysis of Spatio temporal Variation Characteristics and Influencing Factors of Net Primary Productivity in Terrestrial Ecosystems of China","volume":"43","author":"Tu","year":"2023","journal-title":"Acta Ecol. Sin."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107328","DOI":"10.1016\/j.agwat.2021.107328","article-title":"Spatiotemporal Variations of Water Productivity for Cropland and Driving Factors over China during 2001\u20132015","volume":"262","author":"Yang","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109689","DOI":"10.1016\/j.ecolind.2022.109689","article-title":"Analysis of Long-Term Wetland Variations in China Using Land Use\/Land Cover Dataset Derived from Landsat Images","volume":"145","author":"An","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_39","first-page":"21","article-title":"Comparison of GLOPEM and MOD17A3NPP","volume":"1","author":"Fan","year":"2013","journal-title":"J. Shaanxi Meteorol."},{"key":"ref_40","first-page":"35","article-title":"Monthly 1-Km Raster Dataset of Net Primary Productivity of Chinese Terrestrial Ecosystems North of 18\u00b0N Latitude (1985\u20132015)","volume":"3","author":"Chen","year":"2019","journal-title":"J. Glob. Chang. Data Discov."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liang, W., Quan, Q., Wu, B., and Mo, S. (2023). Response of Vegetation Dynamics in the Three-North Region of China to Climate and Human Activities from 1982 to 2018. Sustainability, 15.","DOI":"10.3390\/su15043073"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109436","DOI":"10.1016\/j.ecolind.2022.109436","article-title":"Spatio-Temporal Evolution and Driving Factors of Eco-Environmental Quality Based on RSEI in Chang-Zhu-Tan Metropolitan Circle, Central China","volume":"144","author":"Zhang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_43","first-page":"2111","article-title":"The Spatiotemporal Changes of NPP and Its Driving Mechanisms in China from 2001 to 2020","volume":"31","author":"Shi","year":"2022","journal-title":"Ecol. Environ. Sci."},{"key":"ref_44","first-page":"1821","article-title":"Prediction of Net Primary Productivity Change Pattern in China Based on Vegetation Dynamic Models","volume":"77","author":"Ma","year":"2022","journal-title":"Acta Geogr. Sin."},{"key":"ref_45","first-page":"397","article-title":"The Characteristics of NPP of Terrestrial Vegetation in China Based on MOD17A3 Data","volume":"27","author":"Li","year":"2018","journal-title":"Ecol. Environ. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Naeem, S., Zhang, Y., Tian, J., Qamer, F.M., Latif, A., and Paul, P.K. (2020). Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015. Remote Sens., 12.","DOI":"10.3390\/rs12071113"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cervantes-Duarte, R., Gonz\u00e1lez-Rodr\u00edguez, E., Funes-Rodr\u00edguez, R., Ramos-Rodr\u00edguez, A., Torres-Hern\u00e1ndez, M.Y., and Aguirre-Bahena, F. (2021). Variability of Net Primary Productivity and Associated Biophysical Drivers in Bah\u00eda de La Paz (Mexico). Remote Sens., 13.","DOI":"10.3390\/rs13091644"},{"key":"ref_48","first-page":"162","article-title":"Spatial Pattern Change and Analysis of NPP in Terrestrial Vegetation Ecosystem in China","volume":"51","author":"Sun","year":"2020","journal-title":"Trans. Chinese Soc. Agric. Mach."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, B., S\u00e1nchez-Ruiz, S., Campos-Taberner, M., Garc\u00eda-Haro, F.J., and Gilabert, M.A. (2022). Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14061310"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rafique, R., Zhao, F., De Jong, R., Zeng, N., and Asrar, G.R. (2016). Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison. Remote Sens., 8.","DOI":"10.3390\/rs8030177"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:29Z","timestamp":1760125589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112871"],"URL":"https:\/\/doi.org\/10.3390\/rs15112871","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}