• Journal of Geo-information Science
  • Vol. 22, Issue 9, 1910 (2020)
Ruru CHEN1、2、3, Zhongmin HU4, Shenggong LI1、2、3, and Qun GUO1、2、3、*
Author Affiliations
  • 1Key Lab of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2National Ecosystem Science Data Center, Beijing 100101, China
  • 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
  • 4School of Geography, South China Normal University, Shipai Campus, Guangzhou 510631, China
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    DOI: 10.12082/dqxxkx.2020.190237 Cite this Article
    Ruru CHEN, Zhongmin HU, Shenggong LI, Qun GUO. Assessment of Normalized Difference Vegetation Index from Different Data Sources in Grassland of Northern China[J]. Journal of Geo-information Science, 2020, 22(9): 1910 Copy Citation Text show less


    Productivity of grasslands in northern China plays an important role in regional carbon-water cycle and the development of agriculture and husbandry. Normalized Difference Vegetation Index (NDVI) has been widely used as an indicator of net primary productivity. However, it still remains unclear about the consistency among numerous NDVI datasets in characterizing the spatial and temporal dynamics of grasslands in Northern China. In this study, taking MODIS NDVI as a benchmark dataset, three NDVI datasets (MODIS NDVI, GIMMS NDVI, and SPOT NDVI) were used to compare and analyze the spatial and temporal consistency of NDVI in the grassland of northern China from 2000 to 2015. The most suitable NDVI datasets were selected to reveal the spatial and temporal patterns of NDVI in the study area. Our results show that in terms of the inter-annual variability and changing trend, GIMMS NDVI and MODIS NDVI presented high consistency over the entire grassland area, especially in alpine grassland area including alpine meadow, alpine grassland, alpine desert, and part of temperate grassland area (i.e., desert steppe), with correlation coefficients of 0.60, 0.47, 0.51, and 0.74 respectively. While SPOT NDVI values were much higher than those of the other two datasets, especially in alpine grasslands on Qinghai-Tibet Plateau, with a higher NDVI of 0.15 on average, which implied that caution should be taken when using SPOT NDVI to analyze vegetation dynamics or model productivity in alpine grasslands. GIMMS NDVI and SPOT NDVI displayed relatively high consistency in both temporal variability and changing trend in part of typical and meadow steppes, with correlation coefficients of 0.85 and 0.60, respectively, however, all the three NDVI datasets were highly consistent in their variation ranges in this area, with differences of NDVI less than 0.06. Based on GIMMS NDVI datasets, i.e. the one with the longest time series and highest consistency with MODIS NDVI, we further analyzed the spatial and temporal patterns of NDVI in the study area. We found that NDVI increased generally from 1982 to 2015, with 25% of grassland areas (mainly in temperate grassland area) being significant (p<0.05). There was no significant change of NDVI for the entire alpine grassland area though a significant decreasing trend occurred in a small proportion of the region. Our study has implications for model communities to select datasets and provides an advanced understanding of the responses of vegetation to future climate change in the grassland of northern China.