Chinese Geographical Science

, Volume 29, Issue 5, pp 756–767 | Cite as

Effect of Mathematical Expression of Vegetation Indices on the Estimation of Phenology Trends from Satellite Data

  • Lu Zuo
  • Ronggao LiuEmail author
  • Yang Liu
  • Rong Shang


Vegetation indices (VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI (normalized difference vegetation index) and SR (simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date (SOS) and end date (EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.


vegetation phenology vegetation index trend consistency noise effect spatially temporal comparability northern China Mongolia 


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Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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