| 基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演 |
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| 引用本文： | 李鑫川,徐新刚,鲍艳松,黄文江,罗菊花,董莹莹,宋晓宇,王纪华.基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演[J].中国农业科学,2012,45(17):3486-3496. |
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| 作者姓名： | 李鑫川 徐新刚 鲍艳松 黄文江 罗菊花 董莹莹 宋晓宇 王纪华 |
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| 作者单位： | 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 |
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| 基金项目： | 国家自然科学基金(41001244);北京市自然科学基金(4112022);江苏高校优势学科建设工程(PAPD) |
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| 摘 要： | 【目的】考虑到利用单一植被指数（VI）反演叶面积指数(LAI)时，存在着不同程度的饱和性和易受土壤背景影响的问题，提出通过分段的方式选择敏感植被指数形成最佳VI组合以提高LAI反演的精度。【方法】通过ACRM辐射传输模型模拟数据，结合地面实测光谱数据，选择常用的植被指数进行土壤敏感性分析以及饱和性分析确定LAI的分段点，并在此基础上分段选择最佳植被指数形成组合VI来实现LAI的最终反演，并利Landsat5 TM开展区域条件下冬小麦LAI反演应用。【结果】以LAI=3是较为适宜的分段点，利用植被指数最佳分段组合OSAVI(LAI≤3)+TGDVI(LAI>3)可在一定程度上有效克服土壤影响因素以及饱和性问题，联合反演的结果明确优于单一植被指数反演精度。【结论】通过分段选择最佳植被指数形成联合VI可以有效提高LAI反演精度。
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| 关 键 词： | 冬小麦 叶面积指数 植被指数 分段反演 遥感 |
| 收稿时间： | 2012-02-15; |
Retrieving LAI of Winter Wheat Based on Sensitive Vegetation Index by the Segmentation Method |
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| LI Xin-chuan,XU Xin-gang,BAO Yan-song,HUANG Wen-jiang,LUO Ju-hua,DONG Ying-ying,SONG Xiao-yu,WANG Ji-hua.Retrieving LAI of Winter Wheat Based on Sensitive Vegetation Index by the Segmentation Method[J].Scientia Agricultura Sinica,2012,45(17):3486-3496. |
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| Authors: | LI Xin-chuan XU Xin-gang BAO Yan-song HUANG Wen-jiang LUO Ju-hua DONG Ying-ying SONG Xiao-yu WANG Ji-hua |
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| Affiliation: | 1Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science & Technology,Nanjing 210044;2School of Atmosphere Physics,Nanjing University of Information Science & Technology,Nanjing 210044;3Beijing Research Center for Information Technology in Agriculture,Beijing 100097;4Center for Earth Observation and Digital Earth,China Academy of Sciences,Beijing 100190) |
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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. |
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| Keywords: | winter wheat leaf area index (LAI vegetation index segmentation inversion remote sensing |
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