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Data Details

China’s Terrestrial UNVI Multidimensional Dataset (2018-2021)


ZHAO Hengqian1LIU Xuanqi1ZHANG Lifu*2CHEN Jiahua2FU Hancong1MA Ke1
1 College of Geoscience and Surveying Engineering，China university of mining and technology (Beijing)，Beijing 100083，China2 Aerospace Information Research Institute，Institute of Remote Sensing and Digital Earth，Chinese Academy of Sciences，Beijing 100101，China

DOI:10.3974/geodb.2022.12.01.V1

Published:Dec. 2022

Visitors：7980       Data Files Downloaded：318      
Data Downloaded：1018828.85 MB      Citations：

Key Words：

UNVI,MODIS,Vegetation Index,BRDF-C,Long Time Series

Abstract：

Vegetation index data products are widely used in the inversion of physical and chemical parameters of vegetation, land cover classification and changes studies. Traditional vegetation index products, such as AVHRR-NDVI, are sensitive to soil background changes and have the problem of high value saturation, which is easy to cause the decrease of sensitivity to vegetation detection. The China’s Terrestrial UNVI Multidimensional Dataset (2018-2021) was developed based on the MODIS surface reflectance product MOD09GA, using the Universal Pattern Decomposition algorithm UPDM (Universal Pattern Decomposition Method), which takes 16 days as the synthesis cycle. The practices show that the UNVI has more advantages in reflecting the change of vegetation cover and quantitative inversion of vegetation physical and chemical parameters compared with traditional NDVI products. The synthesis algorithm takes the number of days without cloud data in the synthesis period N as the judgment condition, and uses the Angle normalized synthesis method, the maximum synthesis method in a limited perspective, the direct calculation method and the maximum synthesis method MVC as the main synthesis algorithm to calculate the UNVI. Thus, the 2018-2021 China terrestrial UNVI products with a time resolution of 16 d and a spatial resolution of about 463 m was synthesized. The dataset includes the UNVI products of China terrestrial vegetation indices in 23 time periods with 16 d intervals from 2018 to 2021. The UNVI dataset is archived in .mdd format, and consists of 8 data files with data size of 43.5 GB (Compressed to 4 files with 12.5 GB). The tool and program for .mdd data format can be accessed by the references [23] and [24].Browse

Foundation Item：

Ministry of Education of P. R. China (2022JCCXDC01); China University of Mining and Technology (Beijing) (2020QN07)

Data Citation：

ZHAO Hengqian, LIU Xuanqi, ZHANG Lifu*, CHEN Jiahua, FU Hancong, MA Ke. China’s Terrestrial UNVI Multidimensional Dataset (2018-2021)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.12.01.V1.

ZHAO Hengqian, LIU Xuanqi, ZHANG Lifu, et al. China’s terrestrial UNVI multidimensional dataset (2018-2021) [J]. Journal of Global Change Data & Discovery, 2022, 6(4): 645–655

References：

[1] Wang, Q., Yang, Y. P., Huang, J. Z., et al. Environmental Remote Sensing [M]. Beijing: Science Press, 2004: 366.
     [2] Han, A. H. Study on monitoring method of forest biomass and carton storage based on remote sensing [D]. Beijing: Beijing Forestry University, 2009.
     [3] Tian, Q. J., Min, X. J. Advances in study on vegetation indices [J]. Advances in Earth Sciences, 1998(4): 10-16.
     [4] Noboru, F., Akiko, O., Motomasa, D. Pattern decomposition method for hyper-multispectral satellite data analysis [P]. SPIE Asia-Pacific Remote Sensing, 2001.
     [5] Zhang, L. F., Mitsushita, Y., Furumi, S., et al. Universality of modified pattern decomposition method for satellite sensors [R]. Asia GIS Conference, Wuhan, 2003.
     [6] Zhang, L. F. The universal pattern decomposition method and the vegetation index based on the UPDM [D]. Wuhan: Wuhan University, 2005.
     [7] Zhang, L. F., Zhang, L. P., Muramatsu, K., et al. Universal pattern decomposition method based on hyper spectral satellite remote sensing data [J]. Wuhan: Information Science of Wuhan University, 2005(3): 264-268.
     [8] Daigo, M., Ono, A., Fujiwara, N., et al. Pattern decomposition method for hyper-multi-spectral data analysis [J]. International Journal of Remote Sensing, 2004, 25(6): 1153-1166.
     [9] Zhang, L. F., Furumi, S., Muramatsu, K., et al. Sensor-independent analysis method for hyper-multispectral data based on the pattern decomposition method [J]. International Journal of Remote Sensing, 2006, 27(21): 4899-4910.
     [10] Zhang, L. F., Qiao, N., Baig, M. H. A., et al. Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD [J]. Remote Sensing Letters, 2019, 10(7): 629–638.
     [11] Zhang, L. F., Furumi, S., Muramatsu, K., et al. A new vegetation index based on the universal pattern decomposition method [J]. International Journal of Remote Sensing, 2007, 28(1): 107-124.
     [12] De Wasseige, C., Vancutsem, C., Defourny, P. Sensitivity analysis of compositing strategies: modelling and experimental investigations. Vegetation 2000 [C]. Lake Maggiore, Italy. 2000, 267-274.
     [13] Holben, B. N. Characterization of maximum value composites from temporal AVHRR data [J]. International Journal of Remote Sensing, 1986, 7(11): 1417-1434.
     [14] Lovell, J. L., Graetz, R. D., King, E. A. Compositing AVHRR data for the Australian continent: seeking best practice [J]. Canadian Journal of Remote Sensing, 2003, 29(6): 770-782.
     [15] Duchenmin, B., Maisongrande, P. Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT: I. Investigation of concepts based on simulation [J]. Remote Sensing of Environment, 2002, 81(1): 90-100.
     [16] Long, X., Li, J., Liu, Q. H. Review on VI compositing algorithm [J]. Remote Sensing Technology and Application, 2013, 28(6): 969-977.
     [17] Zhang, L. F., Zhong, T., Liu, H. L., et al. UNVI multidimensional dataset of 2017 China’s terrestrial at 1∶1 000000 scale [J]. Journal of Remote Sensing (Chinese), 24(11): 1293-1298.
     [18] Jiao, W. Z., Zhang, L. F., Chang, Q., et al. Evaluating an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental United States [J]. Remote Sensing, 2016, 8(3): 224.
     [19] Du, H. S., Jiang, H. L., Zhang, L. F., et al. Evaluation of spectral scale effects in estimation of vegetation leaf area index using spectral indices methods [J]. Chinese Geographical Science, 2016, 26(6): 731-744.
     [20] Wang, S. H., Yang, D., Li, Z., et al. A global sensitivity analysis of commonly used satellite-derived vegetation indices for homogeneous canopies based on model simulation and random forest learning [J]. Remote Sensing, 2019, 11(21): 2547.
     [21] Liu, H. L., Zhang, F. Z., Zhang, L. F., et al. UNVI-based time series for vegetation discrimination using separability analysis and random forest classification [J]. Remote Sensing, 2020, 12(3): 529.
     [22] Jiang, H. L., Zhang, L. F., Yang, H., et al. Research on spectral scale effect in the estimation of vegetation leaf chlorophyll content [J]. Spectroscopy and Spectral Analysis, 2016, 36(1): 169-176.
     [23] Zhang, L. F., Sun, X. J., Zhang, X., et al. New version for the multi-dimensional data format (MDD) analysis tool (MARS v2.03) [J/DB/OL]. Digital Journal of Global Change Data Repository, 2017. https://doi.org/10.3974/geodb.2017.02.20.V1.
     [24] Zhang, L. F., Sun, X. J., Zhang, X., et al. A multi-dimensional data format (MDD) and analysis tool [J]. Journal of Global Change Data & Discovery, 2017, 1(2): 121-135. DOI: 10.3974/geodp.2017. 02.01.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_UNVI_China_2018-2021.pdf5747.00kbDownLoad
1 2018_UNVI.zip 3048892.23KB
2 2019_UNVI.zip 3376913.76KB
3 2020_UNVI.zip 3374122.70KB
4 2021_UNVI.zip 3378042.84KB
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