应用生态学报2018，Vol.29Issue(2)：599-606,8.DOI:10.13287/j.1001-9332.201802.019
基于不同光谱指数的植被物候期遥感监测差异
Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices
摘要
Abstract
Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes.Remote sensing spectrum has been widely used in the extraction of vegetation phenology information.However,there are many differences between phenology extracted by remote sensing and site observations,with their physical meaning remaining unclear.We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands.The results showed that there were significant differences between NDVI-phenology and SR-phenology.SOS derived from NDVI averaged 18.9 days earlier than that from SR.EOS derived from NDVI averaged 19.0 days later than from SR.NDVI-phenology had a longer growing season.There were significant differences in the inter-annual variation of phenology from NDVI and SR.More than 20％ of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend.These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR.The observed data source of NDVI and SR were completely consistent,only the mathematical expressions were different,but phenology results were significantly different.Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index.How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.关键词
植被物候/遥感/数学表达/植被指数/时间趋势Key words
vegetation phenology/remote sensing/mathematical expression/vegetation index/temporal trend引用本文复制引用
左璐,王焕炯,刘荣高,刘洋,商荣..基于不同光谱指数的植被物候期遥感监测差异[J].应用生态学报,2018,29(2):599-606,8.基金项目
本文由国家重点研发计划项目(2016YFA0600201)和中国科学院特色研究所项目(TSYJS04)资助 This work was supported by the State Key Research and Development Program of China (2016YFA0600201) and the Distinctive Institutes Development Program,Chinese Academy of Sciences(TSYJS04). （2016YFA0600201）
