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Data Details

Grid Dataset of 1-km Vegetation Health Index in Yellow River-Huangshui River Valley (2000-2020)


SUN Nansha1CHEN Qiong1,2LIU Fenggui1,2ZHOU Qiang1,2GUO Yuanyuan1,3
1 School of Geographic Science，Qinghai Normal University，Xining 810008，China2 Academy of Plateau Science and Sustainability，Xining 810008，China3 Center for Agricultural Resources Research，Institute of Genetics and Developmental Biology，Chinese Academy of Sciences，Shijiazhuang 050022，China

DOI:10.3974/geodb.2022.08.03.V1

Published:Aug. 2022

Visitors：2522       Data Files Downloaded：66      
Data Downloaded：946.93 MB      Citations：

Key Words：

Yellow River-Huangshui River Valley,Vegetation Health Index,growing season,2000-2020

Abstract：

Yellow River–Huangshui River Valley (YHV) is the most important agricultural area in grain production in Qinghai Province of China. The dataset is developed using Vegetation Health Index (VHI) model, integrated with the daily data of MOD09GA and MOD11A1. VHI is the parameter which can couple the NDVI and LST to reflect the agricultural drought level of the region. The dataset includes the following data from 2000 to 2020: (1) the boundary data of the YHV; (2) the average VHI data in the growing season from March to November; (3) the seasonal average VHI data of March to May, June to October and September to November. The spatial resolution of grid data is 1-km. The dataset is archived in .shp and .tif data formats, and consists of 406 data files with data size of 20.9 MB (Compressed into one single file with 14.3 MB).Browse

Foundation Item：

Ministry of Science and Technology of P. R. China (2019YFA0606902)

Data Citation：

SUN Nansha, CHEN Qiong, LIU Fenggui, ZHOU Qiang, GUO Yuanyuan.Grid Dataset of 1-km Vegetation Health Index in Yellow River-Huangshui River Valley (2000-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.08.03.V1.

SUN Nansha, CHEN Qiong, LIU Fenggui, et al. Vegetation health index 1-km grid dataset in Yellow River–Huangshui River valley (2000–2020) [J]. Journal of Global Change Data & Discovery, 2022, 6(4): 589–596

References：

[1] Zhang, L. F., Jiao, W. Z., Zhang, H. M., et al. Studying Drought Phenomena in the Continental United States in 2011 and 2012 using Various Drought Indices [J]. Remote Sensing of Environment, 2017, 190: 96-106.
     [2] Cong, D. M., Zhao, S. H., Chen, C., et al. Characterization of Droughts during 2001–2014 based on Remote Sensing: A Case Study of Northeast China [J]. Ecological Informatics, 2017, 39: 56-67.
     [3] Wu, Z. M., Qiu, J. X., Liu, S. X., et al. Advances in agricultural drought monitoring based on soil moisture [J]. Progress in Geography, 2020, 39(10): 1758-1769.
     [4] IPCC. Climate Change 2021: The Physical Science Basis. http://www.ipcc.ch.
     [5] Ministry of Emergency Management of the People's Republic of China. Basic situation of natural disasters in China in 2021 [EB/OL]. [2022-01-23]. https://www.mem.gov.cn/xw/yjglbgzdt/202201/t20220123_407204.shtml.
     [6] Luo, J., Zhang, Y. L., Liu, F. G., et al. Reconstruction of cropland spatial patterns for 1726 on Yellow River-Huangshui River Valley in northeast Qinghai-Tibet Plateau [J]. Geographical Research, 2014, 33(7): 1285-1296.
     [7] Mu, L. L., Wu, B. F., Y, N. N., et al. Validation of Agricultural Drought Indices and Their Uncertainty Analysis [J]. Bulletin of Soil and Water Conservation, 2007(2): 119-122.
     [8] Kogan, F. N. Application of vegetation index and brightness temperature for drought detection [J]. Advances in Space Research, 1995, 15(11): 91-100.
     [9] Bayarjargal, Y., Karnieli, A., Bavasgalan, M., et al. A comparative study of NOAA-AVHRR derived drought indices using change vector analysis [J]. Remote Sensing of Environment, 2006, 105(1): 9-22.
     [10] Eskinder, G., Oagile, D., Reuben, S., et al. Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia [J]. Environmental Systems Research, 2018, 7(1): 1-18.
     [11] Zhang, A. X., Jia, G. S. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data [J]. Remote Sensing of Environment, 2013, 134: 12-23.
     [12] Kogan, F. N. Operational space technology for global vegetation assessment [J]. Bulletin of the American Meteorological Society, 2001, 82(9): 1949-1964.
     [13] Ding, J., Liu, X. Y., Guo, Y. C., et al. Study on vegetation change in Qinghai-Tibet Plateau from 1980 to 2015 [J]. Ecology and Environmental Sciences, 2021, 30(2): 288-296.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_YHV_VHI_2000-2020.pdf1614.00kbDownLoad
1 YHV_VHI_2000-2020.rar 14691.71KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum