Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections
Abstract
1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. NDVI Data
2.1.2. Land Cover Data
2.1.3. Meteorological Data
- (1)
- Reanalysis of precipitation, temperature, soil moisture, and relative humidity products. The meteorological data used in this study are sourced from the ERA5 dataset provided by the Copernicus Climate Change Service Data Store, which is the fifth-generation global climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [58]. Integrating advanced models and data assimilation systems with extensive historical observations, ERA5 offers various critical meteorological parameters such as precipitation, temperature, soil moisture, and relative humidity, at a spatial resolution of 0.25° × 0.25°, providing high-precision spatiotemporal variation information [59]. Additionally, for carbon dioxide (CO2) data, this study utilizes information from the Copernicus Atmosphere Monitoring Service (CAMS). CAMS provides comprehensive data on atmospheric composition, including CO2 concentrations, which are essential for understanding and analyzing climate change impacts. The integration of CO2 data from CAMS complements the meteorological parameters from ERA5, offering a more holistic view of environmental conditions and their variability over space and time. The ERA5 dataset integrates advanced numerical models and data assimilation systems, combining a large volume of historical observational data to provide high-precision spatiotemporal variation information. ERA5 offers a wide range of key meteorological parameters that can comprehensively reflect changes in meteorological conditions [60]. Additionally, the long temporal coverage of ERA5 allows it to fully encompass the time period under analysis in this study, which is why this dataset was selected for the analysis.
- (2)
- Shortwave radiation. Shortwave radiation data originate from the SYN1 deg-Level 3 dataset of the CERES (Clouds and the Earth’s Radiant Energy System) project within NASA’s Earth Observing System, specifically utilizing the “daily surface radiation components—downward shortwave radiation” product, with a spatial resolution of 1° × 1° and units measured in watts per square meter (W/m2). The CERES project focuses on the study of Earth’s radiation energy balance, and its shortwave radiation data holds a high degree of specialization and scientific value [61,62].
2.1.4. CMIP6 Data
2.2. Method
2.2.1. Seasonal and Trend Decomposition Using Loess
2.2.2. Trend Analysis and Testing
2.2.3. Partial Least Squares Regression
2.2.4. Ensemble Learning
3. Results
3.1. Historical Trend Analysis of NDVI
3.2. Analysis of Meteorological Drivers of NDVI Variation
3.3. Performance Evaluation of NDVI Prediction Models
3.4. Model-Based NDVI Future Trend Prediction
3.5. Impact of Future Meteorological Factors on NDVI
4. Discussion
4.1. The Impact of Policies and Land Use on NDVI
4.2. Changes in NDVI in China Under a High-Emission Scenario
4.3. Limitations and Outlook of This Study
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
NDVI | Normalized Difference Vegetation Index |
CMIP6 | Coupled Model Intercomparison Project Phase 6 |
CO2 | carbon dioxide |
SSP | Shared Socioeconomic Pathways |
STL | Seasonal and Trend decomposition using Loess |
PLS | Partial Least Squares |
VIP | Variable Importance in Projection |
RH | relative humidity |
SWR | shortwave radiation |
SWVL1 | soil moisture |
T2m | temperature at 2 m |
TP | precipitation |
TMC | Temperate Monsoon Climate |
TCC | Temperate Continental Climate |
PMC | Plateau Mountain Climate |
SMC | Subtropical Monsoon Climate |
TRMC | Tropical Monsoon Climate |
RMSE | Root Mean Square Error |
MAE | Mean Absolute Error |
R2 | Coefficient of Determination |
References
- Piao, S.; Ciais, P.; Huang, Y.; Shen, Z.; Peng, S.; Li, J.; Zhou, L.; Liu, H.; Ma, Y.; Ding, Y. The impacts of climate change on water resources and agriculture in China. Nature 2010, 467, 43–51. [Google Scholar] [CrossRef] [PubMed]
- Shilong, P.; Jingyun, F. Dynamic changes of vegetation cover in China in the last 18 years. Quat. Res. 2001, 21, 294–302. [Google Scholar]
- Lan, X.Y.; Li, R.W.; Wang, X.; Zhou, T.C.; Li, Y.H.; Duo, J.; Sun, J. Vegetation Dynamics and Sensitivity Responds to Climate Change in the Upstream of the Yellow River, China. Ecosyst. Health Sustain. 2025, 11, 0292. [Google Scholar] [CrossRef]
- Guha, S. Dynamic seasonal analysis on LST-NDVI relationship and ecological health of Raipur City, India. Ecosyst. Health Sustain. 2021, 7, 1927852. [Google Scholar] [CrossRef]
- Letu, H.; Ma, R.; Nakajima, T.Y.; Shi, C.; Hashimoto, M.; Nagao, T.M.; Baran, A.J.; Nakajima, T.; Xu, J.; Wang, T.X.; et al. Surface Solar Radiation Compositions Observed from Himawari-8/9 and Fengyun-4 Series. Bull. Am. Meteorol. Soc. 2023, 104, E1772–E1789. [Google Scholar] [CrossRef]
- Tana, G.; Ri, X.; Shi, C.; Ma, R.; Letu, H.; Xu, J.; Shi, J.C. Retrieval of cloud microphysical properties from Himawari-8/AHI infrared channels and its application in surface shortwave downward radiation estimation in the sun glint region. Remote Sens. Environ. 2023, 290, 113548. [Google Scholar] [CrossRef]
- Dennison, P.E.; Lamb, B.T.; Campbell, M.J.; Kokaly, R.F.; Hively, W.D.; Vermote, E.; Dabney, P.; Serbin, G.; Quemada, M.; Daughtry, C.S. Modeling global indices for estimating non-photosynthetic vegetation cover. Remote Sens. Environ. 2023, 295, 113715. [Google Scholar] [CrossRef]
- Liu, Y.; Tian, J.; Liu, R.; Ding, L. Influences of climate change and human activities on NDVI changes in China. Remote Sens. 2021, 13, 4326. [Google Scholar] [CrossRef]
- Shi, C.; Letu, H.; Nakajima, T.Y.; Nakajima, T.; Wei, L.; Xu, R.; Lu, F.; Riedi, J.; Ichii, K.; Zeng, J.; et al. Near-global monitoring of surface solar radiation through the construction of a geostationary satellite network observation system. Innovation 2025, 6, 100876. [Google Scholar] [CrossRef]
- Ottosen, T.-B.; Kumar, P. The influence of the vegetation cycle on the mitigation of air pollution by a deciduous roadside hedge. Sustain. Cities Soc. 2020, 53, 101919. [Google Scholar] [CrossRef]
- Ukkola, A.; Keenan, T.; Kelley, D.I.; Prentice, D.I. Vegetation plays an important role in mediating future water resources. Environ. Res. Lett. 2016, 11, 094022. [Google Scholar] [CrossRef]
- Xin, Z.; Xu, J.; Zheng, W. Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981–2006): Impacts of climate changes and human activities. Sci. China Ser. D Earth Sci. 2008, 51, 67–78. [Google Scholar] [CrossRef]
- Deery, D.M.; Smith, D.J.; Davy, R.; Jimenez-Berni, J.A.; Rebetzke, G.J.; James, R.A. Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR. J. Remote Sens. 2021, 2021, 9842178. [Google Scholar] [CrossRef] [PubMed]
- Fan, L.; Dong, G.; Frappart, F.; Wigneron, J.-P.; Yue, Y.; Xiao, X.; Zhang, Y.; Tao, S.; Cao, L.; Li, Y.; et al. Satellite-Observed Increase in Aboveground Carbon over Southwest China during 2013–2021. J. Remote Sens. 2024, 4, 0113. [Google Scholar] [CrossRef]
- Liu, D.; Xu, Z. Decline in Coupling Between Vegetation Photosynthesis and Greening in Northern Ecosystems During the Photosynthesis-Up Period. Glob. Change Biol. 2024, 30, e17569. [Google Scholar] [CrossRef]
- Anyamba, A.; Tucker, C.J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J. Arid. Environ. 2005, 63, 596–614. [Google Scholar] [CrossRef]
- Sun, T.; Li, X.; Xu, M.; Zhang, M. Spatial temporal variations of vegetation coverage in the Tarim River Basin from 2000 to 2018. Arid Land Geogr. 2020, 43, 415–424. [Google Scholar] [CrossRef]
- Huete, A.; Justice, C.; Van Leeuwen, W. MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 1999.
- Mu, X.; Yang, Y.; Xu, H.; Guo, Y.; Lai, Y.; McVicar, T.R.; Xie, D.; Yan, G. Improvement of NDVI mixture model for fractional vegetation cover estimation with consideration of shaded vegetation and soil components. Remote Sens. Environ. 2024, 314, 114409. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Chen, Y.; Sun, Q.; Hu, J. Quantitatively estimating of InSAR decorrelation based on Landsat-derived NDVI. Remote Sens. 2021, 13, 2440. [Google Scholar] [CrossRef]
- Banskota, A.; Kayastha, N.; Falkowski, M.J.; Wulder, M.A.; Froese, R.E.; White, J.C. Forest monitoring using Landsat time series data: A review. Can. J. Remote Sens. 2014, 40, 362–384. [Google Scholar] [CrossRef]
- Yengoh, G.T.; Dent, D.; Olsson, L.; Tengberg, A.E.; Tucker, C.J., III. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations; Springer: Berlin/Heidelberg, Germany, 2015. [Google Scholar]
- Zelený, J.; Mercado-Bettín, D.; Müller, F. Towards the evaluation of regional ecosystem integrity using NDVI, brightness temperature and surface heterogeneity. Sci. Total Environ. 2021, 796, 148994. [Google Scholar] [CrossRef] [PubMed]
- Bellón, B.; Bégué, A.; Lo Seen, D.; De Almeida, C.A.; Simões, M. A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sens. 2017, 9, 600. [Google Scholar] [CrossRef]
- Jin, J.; Wang, Y.; Jiang, H.; Cheng, M. Recent NDVI-based variation in growth of boreal intact forest landscapes and its correlation with climatic variables. Sustainability 2016, 8, 326. [Google Scholar] [CrossRef]
- Basuki, Y. Hydrometeorological hazard prediction in the kuto bodri river region central java based on normalized difference vegetation index (ndvi) analysis. IOP Conf. Ser. Earth Environ. Sci. 2021, 683, 012098. [Google Scholar] [CrossRef]
- Digavinti, J.; Manikiam, B. Satellite monitoring of forest fire impact and regeneration using NDVI and LST. J. Appl. Remote Sens. 2021, 15, 042412. [Google Scholar] [CrossRef]
- Ghebrezgabher, M.G.; Yang, T.; Yang, X.; Sereke, T.E. Assessment of NDVI variations in responses to climate change in the Horn of Africa. Egypt. J. Remote Sens. Space Sci. 2020, 23, 249–261. [Google Scholar] [CrossRef]
- Hou, M.; Zhao, H.; Wang, Z.; Yan, X. Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: A review. Clim. Environ. Res. Technol. 2013, 18, 353–364. [Google Scholar]
- Fawcett, D.; Bennie, J.; Anderson, K. Monitoring spring phenology of individual tree crowns using drone-acquired NDVI data. Remote Sens. Ecol. Conserv. 2021, 7, 227–244. [Google Scholar] [CrossRef]
- Fensholt, R.; Sandholt, I.; Stisen, S.; Tucker, C. Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor. Remote Sens. Environ. 2006, 101, 212–229. [Google Scholar] [CrossRef]
- Xu, Y.; Huang, W.-T.; Dou, S.-Q.; Guo, Z.-D.; Li, X.-Y.; Zheng, Z.-W.; Jing, J.-L. Responding mechanism of vegetation cover to climate change and human activities in Southwest China from 2000 to 2020. Huanjing Kexue 2022, 43, 3230–3240. [Google Scholar] [PubMed]
- Eastman, J.R.; Sangermano, F.; Machado, E.A.; Rogan, J.; Anyamba, A. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sens. 2013, 5, 4799–4818. [Google Scholar] [CrossRef]
- Guo, B.; Jiang, L.; Ge, D.; Shang, M. Driving Mechanism of Vegetation Coverage Change in the Yarlung Zangbo River Basin under the Stress of Global Warming. J. Trop. Subtrop. Bot. 2017, 25, 209–217. [Google Scholar] [CrossRef]
- Jiang, S.; Chen, X.; Smettem, K.; Wang, T. Climate and land use influences on changing spatiotemporal patterns of mountain vegetation cover in southwest China. Ecol. Indic. 2021, 121, 107193. [Google Scholar] [CrossRef]
- Zheng, C.; Zeng, Y.; Zhao, Y.; Zhao, D.; Wu, B. Monitoring and dynamic analysis of fractional vegetation cover in southwestern China over the past 15 years based on MODIS data. Remote Sens. Nat. Resour. 2017, 29, 128–136. [Google Scholar] [CrossRef]
- Guo, L.; Zuo, L.; Gao, J.; Jiang, Y.; Zhang, Y.; Ma, S.; Zou, Y.; Wu, S. Revealing the fingerprint of climate change in interannual NDVI variability among biomes in Inner Mongolia, China. Remote Sens. 2020, 12, 1332. [Google Scholar] [CrossRef]
- Li, Y.; Jia, K.; Wei, X.; Yao, Y.; Sun, J.; Mu, L. Fractional vegetation cover estimation in northern China and its change analysis. Remote Sens. Nat. Resour. 2015, 27, 112–117. [Google Scholar]
- Cheng, F.; Liu, S.; Yin, Y.; Lü, Y.; An, N.; Liu, X. The dynamics and main driving factors of coastal vegetation in Guangxi based on MODIS NDVI. Acta Ecol. Sin. 2017, 37, 788–797. [Google Scholar]
- Wang, X.; Hou, X. Variation of Normalized Difference Vegetation Index and its response to extreme climate in coastal China during 1982–2014. Geogr. Res. 2019, 4, 807–821. [Google Scholar]
- Lu, C.; Hou, M.; Liu, Z.; Li, H.; Lu, C. Variation characteristic of NDVI and its response to climate change in the middle and upper reaches of Yellow River Basin, China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 8484–8496. [Google Scholar] [CrossRef]
- Zhao, A.; Zhang, A.; Lu, C.; Wang, D.; Wang, H.; Liu, H. Spatiotemporal variation of vegetation coverage before and after implementation of Grain for Green Program in Loess Plateau, China. Ecol. Eng. 2017, 104, 13–22. [Google Scholar] [CrossRef]
- Zhang, Y.; Wen, X.; Luo, S.; Li, M. Study on the Relationship between Vegetation Cover Change and Climate Factors in Zoige Wetland in Recent 20 Years. Plateau Meteorol. 2022, 41, 317–327. [Google Scholar] [CrossRef]
- Zhao, H.; Cao, X. Vegetation cover changes and its climate driving in Three-River-Source National Park. Plateau Meteorol. 2022, 41, 328–337. [Google Scholar]
- Wu, D.; Wu, H.; Zhao, X.; Zhou, T.; Tang, B.; Zhao, W.; Jia, K. Evaluation of spatiotemporal variations of global fractional vegetation cover based on GIMMS NDVI data from 1982 to 2011. Remote Sens. 2014, 6, 4217–4239. [Google Scholar] [CrossRef]
- Zhu, J.; Yang, Q.; Li, X.; Li, Y. Characteristics and East Downscaling Forecast Model of Summer Precipitation in Northwest China. Plateau Meteorol. 2023, 42, 646–656. [Google Scholar]
- Zhao, Q.; Zhang, J.; Zhao, T.; Li, J. Vegetation Changes and Its Response to Climate Change in China Since 2000. Plateau Meteorol. 2021, 40, 292–301. [Google Scholar] [CrossRef]
- Lai, X.; Fan, G.; Hua, W.; Ding, X. Progress in the study of influence of the Qinghai-Xizang Plateau land atmosphere interaction on east Asia regional climate. Plateau Meteorol. 2021, 40, 1263–1277. [Google Scholar]
- Bao, Y.; Wang, Y.; Nan, S.; Yu, M. Response of Vegetation over the Qinghai-Xizang Plateau to Projected Warming Climate. Plateau Meteorol. 2023, 42, 553–563. [Google Scholar] [CrossRef]
- Liang, Z.; Sun, R.; Duan, Q. Spatiotemporal variation of NDVI in the Yellow River water conservation zone and its driving factors. Prog. Geogr. 2023, 42, 1717–1732. [Google Scholar] [CrossRef]
- Cui, C.; Zhang, W.; Hong, Z.; Meng, L. Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering. Int. J. Digit. Earth 2020, 13, 1733–1749. [Google Scholar] [CrossRef]
- Huang, S.; Ming, B.; Huang, Q.; Leng, G.; Hou, B. A case study on a combination NDVI forecasting model based on the entropy weight method. Water Resour. Manag. 2017, 31, 3667–3681. [Google Scholar] [CrossRef]
- Gao, P.Q.; Du, W.F.; Lei, Q.W.; Li, J.Z.; Zhang, S.J.; Li, N. NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN-LSTM. Water Resour. Manag. 2023, 37, 1481–1497. [Google Scholar] [CrossRef]
- Didan, K.; Munoz, A.B.; Solano, R.; Huete, A. MODIS vegetation index user’s guide (MOD13 series). Univ. Ariz. Veg. Index Phenol. Lab 2015, 35, 2–33. [Google Scholar]
- van Leeuwen, W.J.; Huete, A.R.; Laing, T.W. MODIS vegetation index compositing approach: A prototype with AVHRR data. Remote Sens. Environ. 1999, 69, 264–280. [Google Scholar] [CrossRef]
- Xu, L.; Herold, M.; Tsendbazar, N.-E.; Masiliūnas, D.; Li, L.; Lesiv, M.; Fritz, S.; Verbesselt, J. Time series analysis for global land cover change monitoring: A comparison across sensors. Remote Sens. Environ. 2022, 271, 112905. [Google Scholar] [CrossRef]
- Bell, B.; Hersbach, H.; Simmons, A.; Berrisford, P.; Dahlgren, P.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Radu, R.; Schepers, D. The ERA5 global reanalysis: Preliminary extension to 1950. Q. J. R. Meteorol. Soc. 2021, 147, 4186–4227. [Google Scholar] [CrossRef]
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D. The ERA5 global reanalysis. Q. J. R. Meteorolog. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
- Muñoz-Sabater, J.; Dutra, E.; Agustí-Panareda, A.; Albergel, C.; Arduini, G.; Balsamo, G.; Boussetta, S.; Choulga, M.; Harrigan, S.; Hersbach, H. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 2021, 13, 4349–4383. [Google Scholar] [CrossRef]
- Smith, G.; Priestley, K.; Loeb, N.; Wielicki, B.; Charlock, T.; Minnis, P.; Doelling, D.; Rutan, D. Clouds and Earth Radiant Energy System (CERES), a review: Past, present and future. Adv. Space Res. 2011, 48, 254–263. [Google Scholar] [CrossRef]
- Wielicki, B.A.; Barkstrom, B.R.; Baum, B.A.; Charlock, T.P.; Green, R.N.; Kratz, D.P.; Lee, R.B.; Minnis, P.; Smith, G.L.; Wong, T. Clouds and the Earth's Radiant Energy System (CERES): Algorithm overview. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1127–1141. [Google Scholar] [CrossRef]
- O'Neill, B.C.; Tebaldi, C.; Van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, R.; Kriegler, E.; Lamarque, J.-F.; Lowe, J. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2016, 9, 3461–3482. [Google Scholar] [CrossRef]
- Zelinka, M.D.; Myers, T.A.; McCoy, D.T.; Po-Chedley, S.; Caldwell, P.M.; Ceppi, P.; Klein, S.A.; Taylor, K.E. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 2020, 47, e2019GL085782. [Google Scholar] [CrossRef]
- Semmler, T.; Danilov, S.; Rackow, T.; Sidorenko, D.; Barbi, D.; Hegewald, J.; Sein, D.; Wang, Q.; Jung, T. AWI AWI-CM1.1MR Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2018. [CrossRef]
- Wu, T.; Chu, M.; Dong, M.; Fang, Y.; Jie, W.; Li, J.; Li, W.; Liu, Q.; Shi, X.; Xin, X.; et al. BCC BCC-CSM2MR Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2018. [CrossRef]
- Chai, Z. CAS CAS-ESM1.0 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2020. [CrossRef]
- Danabasoglu, G. NCAR CESM2 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Danabasoglu, G. NCAR CESM2-FV2 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Danabasoglu, G. NCAR CESM2-WACCM Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Lovato, T.; Peano, D. CMCC CMCC-CM2-SR5 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2020. [CrossRef]
- Lovato, T.; Peano, D.; Butenschön, M. CMCC CMCC-ESM2 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2021. [CrossRef]
- Song, Z.; Qiao, F.; Bao, Y.; Shu, Q.; Song, Y.; Yang, X. FIO-QLNM FIO-ESM2.0 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Ridley, J.; Menary, M.; Kuhlbrodt, T.; Andrews, M.; Andrews, T. MOHC HadGEM3-GC31-MM Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Jungclaus, J.; Bittner, M.; Wieners, K.-H.; Wachsmann, F.; Schupfner, M.; Legutke, S.; Giorgetta, M.; Reick, C.; Gayler, V.; Haak, H.; et al. MPI-M MPI-ESM1.2-HR Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Yukimoto, S.; Koshiro, T.; Kawai, H.; Oshima, N.; Yoshida, K.; Urakawa, S.; Tsujino, H.; Deushi, M.; Tanaka, T.; Hosaka, M.; et al. MRI MRI-ESM2.0 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Bentsen, M.; Oliviè, D.J.L.; Seland, Ø.; Toniazzo, T.; Gjermundsen, A.; Graff, L.S.; Debernard, J.B.; Gupta, A.K.; He, Y.; Kirkevåg, A.; et al. NCC NorESM2-MM Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2019. [CrossRef]
- Lee, W.-L.; Liang, H.-C. AS-RCEC TaiESM1.0 Model Output Prepared for CMIP6 CMIP Historical; Earth System Grid Federation: Greenbelt, MD, USA, 2020. [CrossRef]
- Cleveland, R.B.; Cleveland, W.S.; McRae, J.E.; Terpenning, I. STL: A seasonal-trend decomposition. J. off. Stat. 1990, 6, 3–73. [Google Scholar]
- Hou, Q.; Ji, Z.; Yang, H.; Yu, X. Impacts of climate change and human activities on different degraded grassland based on NDVI. Sci. Rep. 2022, 12, 15918. [Google Scholar] [CrossRef] [PubMed]
- Kendall, M.G. Rank Correlation Methods. 1948. Available online: https://psycnet.apa.org/record/1948-15040-000 (accessed on 24 October 2024).
- Mann, H.B. Nonparametric tests against trend. Econom. J. Econom. Soc. 1945, 13, 245–259. [Google Scholar] [CrossRef]
- Mehmood, T.; Liland, K.H.; Snipen, L.; Sæbø, S. A review of variable selection methods in partial least squares regression. Chemom. Intell. Lab. Syst. 2012, 118, 62–69. [Google Scholar] [CrossRef]
- Yin, S. Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China. Environ. Sci. Pollut. Res. 2022, 29, 44348–44363. [Google Scholar] [CrossRef]
- Mkhabela, M.S.; Bullock, P.R.; Sapirstein, H.D. Characterising the most critical climatic parameters that impact the quality of spring-wheat (Triticum aestivum L.) on the Canadian Prairies using partial least squares (PLS) analysis. J. Cereal Sci. 2018, 81, 44–51. [Google Scholar] [CrossRef]
- Guo, M.; Yao, Q.C.; Suo, H.Q.; Xu, X.X.; Li, J.; He, H.S.; Yin, S.; Li, J.N. The importance degree of weather elements in driving wildfire occurrence in mainland China. Ecol. Indic. 2023, 148, 110152. [Google Scholar] [CrossRef]
- Sabziparvar, A.; Khoshhal Jahromi, F. Evaluating the most effective climatic parameters affecting the monthly mean soil temperature estimates using the PLS method. Arabian J. Geosci. 2022, 15, 1044. [Google Scholar] [CrossRef]
- Farrés, M.; Platikanov, S.; Tsakovski, S.; Tauler, R. Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. J. Chemom. 2015, 29, 528–536. [Google Scholar] [CrossRef]
- Wold, S. PLS for multivariate linear modeling. In Chemometric Methods in Molecular Design; Weily-VCH: Hoboken, NJ, USA, 1995; pp. 195–218. [Google Scholar]
- Fushiki, T. Estimation of prediction error by using K-fold cross-validation. Stat. Comput. 2011, 21, 137–146. [Google Scholar] [CrossRef]
- Gong, H.; Cao, L.; Duan, Y.; Jiao, F.; Xu, X.; Zhang, M.; Wang, K.; Liu, H. Multiple effects of climate changes and human activities on NPP increase in the Three-north Shelter Forest Program area. For. Ecol. Manag. 2023, 529, 120732. [Google Scholar] [CrossRef]
- Zhang, Y.; Yang, Z.; Liu, F.; Xu, M.; Zhang, J. Impact of the Project of Returning Farmland to Forest on Promoting Forest Coverage Rates in Mountainous Areas: An Empirical Analysis Based on Remote Sensing in Yunnan. Forests 2024, 15, 1956. [Google Scholar] [CrossRef]
- Guerra, T.N.; Araújo, E.L.; Sampaio, E.V.; Ferraz, E.M. Urban or rural areas: Which types of surrounding land use induce stronger edge effects on the functional traits of tropical forests plants? Appl. Veg. Sci. 2017, 20, 538–548. [Google Scholar] [CrossRef]
- Liu, W.; Zhang, H.; Lü, X.t.; Zhou, Y.; Yang, Y.; Lü, Y.; Yang, J.; Wen, L.; Pan, Q.; Han, X. Dynamics and drivers of primary productivity along a 40-year grazing exclusion chronosequence in a typical steppe of Inner Mongolia. Restor. Ecol. 2025, 33, e14377. [Google Scholar] [CrossRef]
- Marklein, A.; Elias, E.; Nico, P.; Steenwerth, K. Projected temperature increases may require shifts in the growing season of cool-season crops and the growing locations of warm-season crops. Sci. Total Environ. 2020, 746, 140918. [Google Scholar] [CrossRef]
- Yang, H.; Zhong, C.H.; Jin, T.Y.; Chen, J.H.; Zhang, Z.J.; Hu, Z.M.; Wu, K. Stronger Impact of Extreme Heat Event on Vegetation Temperature Sensitivity under Future Scenarios with High-Emission Intensity. Remote Sens. 2024, 16, 3708. [Google Scholar] [CrossRef]
- Lee, Y.-H.; Sang, W.-G.; Baek, J.-K.; Kim, J.-H.; Shin, P.; Seo, M.-C.; Cho, J.-I. The effect of concurrent elevation in CO2 and temperature on the growth, photosynthesis, and yield of potato crops. PLoS ONE 2020, 15, e0241081. [Google Scholar] [CrossRef]
- Dakos, V.; Matthews, B.; Hendry, A.P.; Levine, J.; Loeuille, N.; Norberg, J.; Nosil, P.; Scheffer, M.; De Meester, L. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 2019, 3, 355–362. [Google Scholar] [CrossRef]
- Suz, L.M.; Bidartondo, M.I.; van der Linde, S.; Kuyper, T.W. Ectomycorrhizas and tipping points in forest ecosystems. New Phytol. 2021, 231, 1700–1707. [Google Scholar] [CrossRef] [PubMed]
- Moore, J.C. Predicting tipping points in complex environmental systems. Proc. Natl. Acad. Sci. USA 2018, 115, 635–636. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, K.A.; Seeboonruang, U.; Chen, W. Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach. Environments 2023, 10, 204. [Google Scholar] [CrossRef]
- Cai, S.H.; Song, X.N.; Hu, R.H.; Guo, D. Ecosystem-Dependent Responses of Vegetation Coverage on the Tibetan Plateau to Climate Factors and Their Lag Periods. ISPRS Int. J. Geo-Inf. 2021, 10, 394. [Google Scholar] [CrossRef]
- Hua, L.; Wang, H.D.; Sui, H.G.; Wardlow, B.; Hayes, M.J.; Wang, J.X. Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region. Remote Sens. 2019, 11, 1873. [Google Scholar] [CrossRef]
- Liu, N.; Ding, Y.X.; Peng, S.Z. Temporal effects of climate on vegetation trigger the response biases of vegetation to human activities. Global Ecol. Conserv. 2021, 31, e01822. [Google Scholar] [CrossRef]
- WMO. WMO Guidelines on the Calculation of Climate Normals; WMO: Geneva, Switzerland, 2017. [Google Scholar]
ID | Model Name | Country | Resolution | Weights |
---|---|---|---|---|
1 | AWI-CM-1-1-MR [65] | Germany | ~0.9375° × 0.9375° (~90 km) | 0.0928 |
2 | BCC-CSM2-MR [66] | China | ~1.125° × 1.125° (~106.9 km) | 0.0427 |
3 | CAS-ESM2-0 [67] | China | ~1.4° × 1.4° (~132.9 km) | 0.0808 |
4 | CESM2 [68] | USA | ~0.9375° × 1.25° (~101.4 km) | 0.0697 |
5 | CESM2-FV2 [69] | USA | ~0.9° × 1.25° (~99.3 km) | 0.0900 |
6 | CESM2-WACCM [70] | USA | ~0.938° × 1.25° (~101.4 km) | 0.0912 |
7 | CMCC-CM2-SR5 [71] | Italy | ~0.938° × 1.25° (~101.4 km) | 0.0779 |
8 | CMCC-ESM2 [72] | Italy | ~0.938° × 1.25° (~101.4 km) | 0.0788 |
9 | FIO-ESM-2-0 [73] | China | ~0.938° × 1.25° (~101.4 km) | 0.0865 |
10 | HadGEM3-GC31-MM [74] | UK | ~0.556° × 0.833° (~70.1 km) | 0.0418 |
11 | MPI-ESM1-2-HR [75] | Germany | ~0.938° × 0.938° (~90 km) | 0.0361 |
12 | MRI-ESM2-0 [76] | Japan | ~1.125° × 1.125° (~106.9 km) | 0.0914 |
13 | NorESM2-MM [77] | Norway | ~0.938° × 1.25° (~101.4 km) | 0.0353 |
14 | TaiESM1 [78] | China, Taiwan | ~0.938° × 1.25° (~101.4 km) | 0.0850 |
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Li, A.; Yin, S.; Li, N.; Shi, C. Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections. Atmosphere 2025, 16, 738. https://doi.org/10.3390/atmos16060738
Li A, Yin S, Li N, Shi C. Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections. Atmosphere. 2025; 16(6):738. https://doi.org/10.3390/atmos16060738
Chicago/Turabian StyleLi, Ao, Shuai Yin, Nan Li, and Chong Shi. 2025. "Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections" Atmosphere 16, no. 6: 738. https://doi.org/10.3390/atmos16060738
APA StyleLi, A., Yin, S., Li, N., & Shi, C. (2025). Comprehensive Analysis of the Driving Forces Behind NDVI Variability in China Under Climate Change Conditions and Future Scenario Projections. Atmosphere, 16(6), 738. https://doi.org/10.3390/atmos16060738