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International Journal of Digital Earth Volume 16, 2023 - Issue 2
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Research Article

Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method

Rui Yaoa School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaCorrespondence[email protected]
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Yongjun Zhanga School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaView further author information
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Lunche Wangb Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, People’s Republic of China;c Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, People’s Republic of ChinaView further author information
,
Jiayi Lia School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaView further author information
&
Qiquan Yangd College of Surveying and Geo-Informatics, Tongji University, Shanghai, People’s Republic of China;e State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau, People’s Republic of ChinaView further author information
Pages 4749-4768 | Received 21 Sep 2023, Accepted 08 Nov 2023, Published online: 15 Nov 2023

References

  • Beck, P. S. A., C. Atzberger, K. A. Høgda, B. Johansen, and A. K. Skidmore. 2006. “Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A new Method Using MODIS NDVI.” Remote Sensing of Environment 100 (3): 321–334. https://doi.org/10.1016/j.rse.2005.10.021.
  • Cao, Ruyin, Yang Chen, Miaogen Shen, Jin Chen, Ji Zhou, Cong Wang, and Wei Yang. 2018. “A Simple Method to Improve the Quality of NDVI Time-series Data by Integrating Spatiotemporal Information with the Savitzky-Golay Filter.” Remote Sensing of Environment 217:244–257. https://doi.org/10.1016/j.rse.2018.08.022.
  • Cazorla, Beatriz P., Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura. 2023. “A Remote-sensing-based Dataset to Characterize the Ecosystem Functioning and Functional Diversity in the Biosphere Reserve of the Sierra Nevada (Southeastern Spain).” Earth System Science Data 15:1871–1887. https://doi.org/10.5194/essd-15-1871-2023.
  • Chen, Yang, Ruyin Cao, Jin Chen, Licong Liu, and Bunkei Matsushita. 2021. “A Practical Approach to Reconstruct High-Quality Landsat NDVI Time-series Data by gap Filling and the Savitzky–Golay Filter.” ISPRS Journal of Photogrammetry and Remote Sensing 180:174–190. https://doi.org/10.1016/j.isprsjprs.2021.08.015.
  • Chen, J., P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh. 2004. “A Simple Method for Reconstructing a High-Quality NDVI Time-series Data Set Based on the Savitzky–Golay Filter.” Remote Sensing of Environment 91 (3–4): 332–344. https://doi.org/10.1016/j.rse.2004.03.014.
  • Chen, Jing M., Weimin Ju, Philippe Ciais, Nicolas Viovy, Ronggao Liu, Yang Liu, and Xuehe Lu. 2019b. “Vegetation Structural Change Since 1981 Significantly Enhanced the Terrestrial Carbon Sink.” Nature Communications 10:4259. https://doi.org/10.1038/s41467-019-12257-8.
  • Chen, Chi, Taejin Park, Xuhui Wang, Shilong Piao, Baodong Xu, Rajiv K. Chaturvedi, Richard Fuchs, et al. 2019a. “China and India Lead in Greening of the World Through Land-use Management.” Nature Sustainability 2:122–129. https://doi.org/10.1038/s41893-019-0220-7.
  • Choler, Philippe, Arthur Bayle, Bradley Z. Carlson, Christophe Randin, Gianluca Filippa, and Edoardo Cremonese. 2021. “The Tempo of Greening in the European Alps: Spatial Variations on a Common Theme.” Global Change Biology 27. 14-5628.10.1111gcb.15820.
  • Chu, Dong, Huanfeng Shen, Xiaobin Guan, Jing M. Chen, Xinghua Li, Jie Li, and Liangpei Zhang. 2021. “Long Time-series NDVI Reconstruction in Cloud-prone Regions via Spatio-temporal Tensor Completion.” Remote Sensing of Environment 264:112632. https://doi.org/10.1016/j.rse.2021.112632.
  • Dalagnol, Ricardo, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão. 2023. “AnisoVeg: Anisotropy and Nadir-normalized MODIS Multi-angle Implementation Atmospheric Correction (MAIAC) Datasets for Satellite Vegetation Studies in South America.” Earth System Science Data 15:345–358. https://doi.org/10.5194/essd-15-345-2023.
  • Forzieri, Giovanni, Diego G. Miralles, Philippe Ciais, Ramdane Alkama, Youngryel Ryu, Gregory Duveiller, Ke Zhang, et al. 2020. “Increased Control of Vegetation on Global Terrestrial Energy Fluxes.” Nature Climate Change 10 (4): 356–362. https://doi.org/10.1038/s41558-020-0717-0.
  • Gao, Jixi, Yuanli Shi, Hongwei Zhang, Xuhui Chen, Wenguo Zhang, Wenming Shen, Tong Xiao, and Yuhuan Zhang. 2023. “China Regional 250m Normalized Difference Vegetation Index Data Set (2000–2022).” National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.11888/Terre.tpdc.300328.
  • Geng, Xiaojun, Xuancheng Zhou, Guodong Yin, Fanghua Hao, Xuan Zhang, Zengchao Hao, Vijay P. Singh, and Yongshuo H. Fu. 2020. “Extended Growing Season Reduced River Runoff in Luanhe River Basin.” Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.124538.
  • Gerber, Florian, Rogier de Jong, Michael E. Schaepman, Gabriela Schaepman-Strub, and Reinhard Furrer. 2018. “Predicting Missing Values in Spatio-temporal Remote Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 56:2841–2853. https://doi.org/10.1109/TGRS.2017.2785240.
  • Hilker, Thomas, Alexei I. Lyapustin, Compton J. Tucker, Forrest G. Hall, Ranga B. Myneni, Yujie Wang, Jian Bi, Yhasmin Mendes de Moura, and Piers J. Sellers. 2014. “Vegetation Dynamics and Rainfall Sensitivity of the Amazon.” Proceedings of the National Academy of Sciences of the United States of America 111 (45): 16041–16046. https://doi.org/10.1073/pnas.1404870111.
  • Huang, Anqi, Runping Shen, Wenli Di, and Huimin Han. 2021. “A Methodology to Reconstruct LAI Time Series Data Based on Generative Adversarial Network and Improved Savitzky-Golay Filter.” International Journal of Applied Earth Observation and Geoinformation 105:102633. https://doi.org/10.1016/j.jag.2021.102633.
  • Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1–2): 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
  • Ims, Rolf A., John-Andre Henden, Marita A. Strømeng, Anders V. Thingnes, Mari J. Garmo, and Jane U. Jepsen. 2019. “Arctic Greening and Bird Nest Predation Risk Across Tundra Ecotones.” Nature Climate Change 9 (8): 607–610. https://doi.org/10.1038/s41558-019-0514-9.
  • Jakubauskas, Mark E., David R. Legates, and Jude H. Kastens. 2001. “Harmonic Analysis of Time-series AVHRR NDVI Data.” Photogrammetric Engineering and Remote Sensing 67:461–470. https://www.asprs.org/wp-content/uploads/pers/2001journal/april/2001_apr_461-470.pdf.
  • Jönsson, Per, and Lars Eklundh. 2002. “Seasonality Extraction by Function Fitting to Time-series of Satellite Sensor Data.” IEEE Transactions on Geoscience and Remote Sensing 40 (8): 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.
  • Kong, Dongdong, Yongqiang Zhang, Xihui Gu, and Dagang Wang. 2019. “A Robust Method for Reconstructing Global MODIS EVI Time Series on the Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 155:13–24. https://doi.org/10.1016/j.isprsjprs.2019.06.014.
  • Li, Shuang, Liang Xu, Jiajia Chen, Yazhen Jiang, Shuying Sun, Shaohuai Yu, Zhenyu Tan, and Xinghua Li. 2023. “Monitoring Vegetation Dynamics (2010–2020) in Shengnongjia Forestry District with Cloud-removed MODIS NDVI Series by a Spatio-temporal Reconstruction Method.” The Egyptian Journal of Remote Sensing and Space Sciences 26 (3): 527–543. https://doi.org/10.1016/j.ejrs.2023.06.010.
  • Li, Shuang, Liang Xu, Yinghong Jing, Hang Yin, Xinghua Li, and Xiaobin Guan. 2021. “High-quality Vegetation Index Product Generation: A Review of NDVI Time Series Reconstruction Techniques.” International Journal of Applied Earth Observation and Geoinformation 105:102640. https://doi.org/10.1016/j.jag.2021.102640.
  • Li, Xiaoma, Yuyu Zhou, Ghassem R. Asrar, and Zhengyuan Zhu. 2018. “Creating a Seamless 1 km Resolution Daily Land Surface Temperature Dataset for Urban and Surrounding Areas in the Conterminous United States.” Remote Sensing of Environment 206:84–97. https://doi.org/10.1016/j.rse.2017.12.010.
  • Li, Xuecao, Yuyu Zhou, Lin Meng, Ghassem R. Asrar, Chaoqun Lu, and Qiusheng Wu. 2019. “A Dataset of 30 m Annual Vegetation Phenology Indicators (1985–2015) in Urban Areas of the Conterminous United States.” Earth System Science Data 11:881–894. https://doi.org/10.5194/essd-11-881-2019.
  • Liu, Huiyu, Fusheng Jiao, Jingqiu Yin, Tingyou Li, Haibo Gong, Zhaoyue Wang, and Zhenshan Lin. 2020. “Nonlinear Relationship of Vegetation Greening with Nature and Human Factors and its Forecast – A Case Study of Southwest China.” Ecological Indicators 111:106009. https://doi.org/10.1016/j.ecolind.2019.106009.
  • Liu, Yongchang, Zhi Li, Yaning Chen, Patient Mindje Kayumba, Xuanxuan Wang, Chuanxiu Liu, Yunxia Long, and Fan Sun. 2022. “Biophysical Impacts of Vegetation Dynamics Largely Contribute to Climate Mitigation in High Mountain Asia.” Agricultural and Forest Meteorology 327:109233. https://doi.org/10.1016/j.agrformet.2022.109233.
  • Liu, Ronggao, Rong Shang, Yang Liu, and Xiaoliang Lu. 2017. “Global Evaluation of Gap-filling Approaches for Seasonal NDVI with Considering Vegetation Growth Trajectory, Protection of Key Point, Noise Resistance and Curve Stability.” Remote Sensing of Environment 189:164–179. https://doi.org/10.1016/j.rse.2016.11.023.
  • Liu, Zihan, Wenfeng Zhan, Jiameng Lai, Falu Hong, Jinling Quan, Benjamin Bechtel, Fan Huang, and Zhaoxu Zou. 2019. “Balancing Prediction Accuracy and Generalization Ability: A Hybrid Framework for Modelling the Annual Dynamics of Satellite-Derived Land Surface Temperatures.” ISPRS Journal of Photogrammetry and Remote Sensing 151:189–206. https://doi.org/10.1016/j.isprsjprs.2019.03.013.
  • Militino, Ana F., M. Dolores Ugarte, Unai Perez-Goya, and Marc G. Genton. 2019. “Interpolation of the Mean Anomalies for Cloud Filling in Land Surface Temperature and Normalized Difference Vegetation Index.” IEEE Transactions on Geoscience and Remote Sensing 57:6068–6078. https://doi.org/10.1109/TGRS.2019.2904193.
  • Parizi, Esmaeel, Seiyed Mossa Hosseini, Behzad Ataie-Ashtiani, and Craig T. Simmons. 2020. “Normalized Difference Vegetation Index as the Dominant Predicting Factor of Groundwater Recharge in Phreatic Aquifers: Case Studies Across Iran.” Scientific Reports 10:17473. https://doi.org/10.1038/s41598-020-74561-4.
  • Piao, Shilong, Xuhui Wang, Taejin Park, Chi Chen, Xu Lian, Yue He, Jarle W. Bjerke, et al. 2019. “Characteristics, Drivers and Feedbacks of Global Greening.” Nature Reviews Earth & Environment 1:14–27. https://doi.org/10.1038/s43017-019-0001-x.
  • Savitzky, Abraham, and M. J. E. Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures.” Analytical Chemistry 36: 1627–1639. https://doi.org/10.1021/ac60214a047.
  • Song, Wenqi, Yuhao Feng, and Zhiheng Wang. 2022. “Ecological Restoration Programs Dominate Vegetation Greening in China.” Science of the Total Environment 848:157729. https://doi.org/10.1016/j.scitotenv.2022.157729.
  • Sun, Liang, Zhongxin Chen, Feng Gao, Martha Anderson, Lisheng Song, Limin Wang, Bo Hu, and Yun Yang. 2017. “Reconstructing Daily Clear-sky Land Surface Temperature for Cloudy Regions from MODIS Data.” Computers & Geosciences 105:10–20. https://doi.org/10.1016/j.cageo.2017.04.007.
  • Tian, Jiaqi, Xiaolin Zhu, Jin Chen, Cong Wang, Miaogen Shen, Wei Yang, Xiaoyue Tan, Shuai Xu, and Zhilin Li. 2021. “Improving the Accuracy of Spring Phenology Detection by Optimally Smoothing Satellite Vegetation Index Time Series Based on Local Cloud Frequency.” ISPRS Journal of Photogrammetry and Remote Sensing 180:29–44. https://doi.org/10.1016/j.isprsjprs.2021.08.003.
  • Xie, Fei, and Hui Fan. 2021. “Deriving Drought Indices from MODIS Vegetation Indices (NDVI/EVI) and Land Surface Temperature (LST): Is Data Reconstruction Necessary?” International Journal of Applied Earth Observation and Geoinformation 101:102352. https://doi.org/10.1016/j.jag.2021.102352.
  • Xue, Jingru, and Baofeng Su. 2017. “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications.” Journal of Sensors 2017: 1353691. https://doi.org/10.1155/2017/1353691.
  • Yang, Xue, Jin Chen, Qingfeng Guan, Huan Gao, and Wei Xia. 2022. “Enhanced Spatial–Temporal Savitzky–Golay Method for Reconstructing High-Quality NDVI Time Series: Reduced Sensitivity to Quality Flags and Improved Computational Efficiency.” IEEE Transactions on Geoscience and Remote Sensing 60:1–17. https://doi.org/10.1109/tgrs.2022.3190475.
  • Yang, Jie, and Xin Huang. 2021. “The 30m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019.” Earth System Science Data 13: 3907–3925. https://doi.org/10.5194/essd-13-3907-2021.
  • Yang, Gang, Huanfeng Shen, Liangpei Zhang, Zongyi He, and Xinghua Li. 2015. “A Moving Weighted Harmonic Analysis Method for Reconstructing High-quality SPOT VEGETATION NDVI Time-Series Data.” IEEE Transactions on Geoscience and Remote Sensing 53:6008–6021. https://doi.org/10.1109/TGRS.2015.2431315.
  • Yang, Bo-Yi, Tianyu Zhao, Li-Xin Hu, Matthew H.E.M Browning, Joachim Heinrich, Shyamali C. Dharmage, Bin Jalaludin, et al. 2021. “Greenspace and Human Health: An Umbrella Review.” The Innovation 2:100164. https://doi.org/10.1016/j.xinn.2021.100164.
  • Yao, Rui, Xin Huang, Yongjun Zhang, Lunche Wang, Jiayi Li, and Qiquan Yang. 2023a. “Reconstructed 250 m Resolution NDVI Dataset in China.” https://doi.org/10.5281/zenodo.7988989.
  • Yao, Rui, Xin Huang, Yongjun Zhang, Lunche Wang, Jiayi Li, and Qiquan Yang. 2023b. “Reconstructed 250 m Resolution EVI Dataset in China.” https://doi.org/10.5281/zenodo.7979989.
  • Yao, Rui, Lunche Wang, Xin Huang, Qian Cao, Jing Wei, Panxing He, Shaoqiang Wang, and Lizhe Wang. 2023c. “Global Seamless and High-Resolution Temperature Dataset (GSHTD), 2001–2020.” Remote Sensing of Environment 286:113422. https://doi.org/10.1016/j.rse.2022.113422.
  • Yao, Rui, Lunche Wang, Xin Huang, Xinxin Chen, and Zhengjia Liu. 2019. “Increased Spatial Heterogeneity in Vegetation Greenness Due to Vegetation Greening in Mainland China.” Ecological Indicators 99:240–250. https://doi.org/10.1016/j.ecolind.2018.12.039.
  • Yao, Rui, Lunche Wang, Xin Huang, Liang Sun, Ruiqing Chen, Xiaojun Wu, Wei Zhang, and Zigeng Niu. 2021. “A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data.” IEEE Transactions on Geoscience and Remote Sensing 59:1–15. https://doi.org/10.1109/tgrs.2021.3053284.
  • Zeng, Zhenzhong, Shilong Piao, Laurent Z. X. Li, Tao Wang, Philippe Ciais, Xu Lian, Yuting Yang, Jiafu Mao, Xiaoying Shi, and Ranga B. Myneni. 2018. “Impact of Earth Greening on the Terrestrial Water Cycle.” Journal of Climate 31 (7): 2633–2650. https://doi.org/10.1175/JCLI-D-17-0236.1.
  • Zeng, Chao, Huanfeng Shen, Mingliang Zhong, Liangpei Zhang, and Penghai Wu. 2015. “Reconstructing MODIS LST Based on Multitemporal Classification and Robust Regression.” IEEE Geoscience and Remote Sensing Letters 12:512–516. https://doi.org/10.1109/LGRS.2014.2348651.
  • Zhang, Yulong, Conghe Song, Lawrence E. Band, Ge Sun, and Junxiang Li. 2017. “Reanalysis of Global Terrestrial Vegetation Trends from MODIS Products: Browning or Greening?” Remote Sensing of Environment 191:145–155. https://doi.org/10.1016/j.rse.2016.12.018.
  • Zhang, Huifang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, et al. 2023. “A 250 m Annual Alpine Grassland AGB Dataset Over the Qinghai–Tibet Plateau (2000–2019) in China Based on in Situ Measurements, UAV Photos, and MODIS Data.” Earth System Science Data 15:821–846. https://doi.org/10.5194/essd-15-821-2023.
  • Zhang, Liangliang, Zhao Zhang, Yuchuan Luo, Juan Cao, Ruizhi Xie, and Shaokun Li. 2021. “Integrating Satellite-Derived Climatic and Vegetation Indices to Predict Smallholder Maize Yield Using Deep Learning.” Agricultural and Forest Meteorology 311:108666. https://doi.org/10.1016/j.agrformet.2021.108666.
  • Zhao, Lin, Aiguo Dai, and Bo Dong. 2018. “Changes in Global Vegetation Activity and its Driving Factors during 1982–2013.” Agricultural and Forest Meteorology 249:198–209. https://doi.org/10.1016/j.agrformet.2017.11.013.
  • Zheng, Lei, Guosong Zhao, Jinwei Dong, Quansheng Ge, Jian Tao, Xuezhen Zhang, Youcun Qi, Russell B. Doughty, and Xiangming Xiao. 2019. “Spatial, Temporal, and Spectral Variations in Albedo due to Vegetation Changes in China’s Grasslands.” ISPRS Journal of Photogrammetry and Remote Sensing 152:1–12. https://doi.org/10.1016/j.isprsjprs.2019.03.020

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