Scientia Agricultura Sinica ›› 2012, Vol. 45 ›› Issue (17): 3486-3496.doi: 10.3864/j.issn.0578-1752.2012.17.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Retrieving LAI of Winter Wheat Based on Sensitive Vegetation ndex by the Segmentation Method

 LI  Xin-Chuan, XU  Xin-Gang, BAO  Yan-Song, HUANG  Wen-Jiang, LUO  Ju-Hua, DONG  Ying-Ying, SONG  Xiao-Yu, WANG  Ji-Hua   

  1. 1.南京信息工程大学气象灾害省部共建教育部重点实验室，南京210044
    2.南京信息工程大学大气物理学院，南京210044
    3.北京农业信息技术研究中心，北京 100097
    4.中国科学院对地观测与数字地球中心，北京 100190
  • Received:2012-02-15 Online:2012-09-01 Published:2012-09-01

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Abstract: 【Objective】The method of inversion leaf area index (LAI) using a single vegetation index (VI) is influenced by different degrees of saturability and soil background. This paper proposed a method choosing sensitive vegetation index by the segmentation method to form optimal VI combination, and to improve the accuracy of LAI inversion.【Method】In this study the ACRM radiation transmission model was used to simulate data, and the ground measured spectrum data were obtained. The study analyzed soil sensitivity and saturability about the common vegetation index to determine the segment point of LAI, and chose the best vegetation index based on segment point of LAI to form a combination VI for achieving the final inversion of the LAI. This method was also used in the regional winter wheat LAI inversion application with the Landsat5 TM data. 【Result】The analysis showed that, LAI = 3 was the more appropriate segment point, and the use of vegetation index segment combination OSAVI (LAI ≤3) + TGDVI (LAI＞3) partly overcame soil factors and the saturation problems. The joint inversion results were significantly superior to the single vegetation index retrieval accuracy.【Conclusion】LAI was effectively inversed with the higher accuracy by choosing the best vegetation index through the segmentation method.

Key words: winter wheat, leaf area index (LAI), vegetation index, segmentation inversion, remote sensing

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