Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Remote Sensing

Linlin Lu⁴  Claudia Kuenzer²  Cuizhen Wang¹  Huadong Guo⁴  Qingting Li⁴  Arnon Karnieli³ 

[1] Department of Geography, University of South Carolina, Columbia, SC 29208, USA; E-Mail:;German Remote Sensing Data Centre (DFD), German Aerospace Centre (DLR), D-82234 Wessling, Germany; E-Mail:Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China;;Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China; E-Mails:

关键词: MODIS;    vegetation index;    dryland;    vegetation dynamics;    time series;    phenology;   

DOI  :  10.3390/rs70607597

来源: mdpi

【 摘 要 】

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.