基于ASTER数据黑河中游植被含水量反演研究

doi:10.11873/j.issn.1004-0323.2015.5.0876

遥感技术与应用

2015, Vol. 30

Issue (5): 876-883 DOI: 10.11873/j.issn.1004-0323.2015.5.0876

模型与反演

基于ASTER数据黑河中游植被含水量反演研究

闻熠^1,2,3,黄春林^1,2,卢玲¹,顾娟⁴

(1.中国科学院寒区旱区环境与工程研究所甘肃省遥感重点实验室,甘肃兰州 730000;
2.中国科学院寒区旱区环境与工程研究所黑河遥感试验研究站,甘肃兰州 730000;
3.中国科学院大学,北京 100049
4.兰州大学西部环境与气候变化研究院,甘肃兰州 730000)

Theretrieval of Vegetation Water Content based on ASTER Images in Middle of Heihe River Basin

Wen Yi^1,2,3,Huang ChunLin^1,2,Lu Ling¹,Gu Juan⁴

(1.Key Laboratory of Remote Sensing of Gansu Province,Cold and Arid Regions Environmental and
Engineering Research Institute,Chinese Academy of Sciences,Lanzhou 730000,China;
2.Heihe Remote Sensing Experimental Research Station,Cold and Arid Regions Environmental and
Engineering Research Institute,Chinese Academy of Sciences,Lanzhou 730000,China;
3.University of Chinese Academy of Sciences,Beijing 100049,China;
4.MoE Key Laboratory of West China's Environmental System,
Lanzhou University,Lanzhou 730000,China)

全文: PDF

摘要：

植被含水量是影响植物生长的主要限制因子之一,也是衡量植被生理状态和形态结构的重要参数。应用遥感技术定量估测植被含水量,对于农业旱情监测\,作物产量估计和科学研究具有重要意义。基于2012年黑河生态水文遥感试验期间获得的6景ASTER遥感数据和同步观测的研究区生物量观测数据集,选取NDVI、RVI、SAVI和MSAVI 4种植被指数分别与单位面积内植被含水量的关系进行比较分析,建立了不同植被指数的植被含水量反演模型,并对反演结果进行了验证。研究结果表明：4种植被指数均与实测的植被含水量有较高的相关性(R²>0.846),利用MSAVI反演的植被含水量精度略优于其他3种指数,其均方根误差(RMSE)在0.794 kg/m²内。模型较为可靠,可以为大范围获取植被含水量信息提供有效方法。

关键词： 植被含水量; 植被指数; 遥感; ASTER; 黑河流域

Abstract:

Vegetation Water Content (VWC) is one of the main limiting factors of affecting growth of plants,which is an important parameter to character vegetation physiological status and morphology.Quantitative estimation of VWC by utilizing remote sensing technology has important significances for agricultural drought monitoring,crop yield estimation and scientific research.In this paper,six periods ASTER images and ground\|based measurements of VWC at 11 sampling sites are used to develop the empirical inversion model of VWC,which are obtained during the Heihe Watershed Allied Telemetry Experimental Research (Hi\|WATER) in 2012.The four types of vegetation indexes (NDVI,RVI,SAVI,and MSAVI) are adopted in this study.We analyze the relationship between different vegetation indexes and the measured VWC,then develop and validate these VI\|based empirical models for VWC retrieval.Results show that the correlation is very high between the measured VWC and the selected four vegetation indexes (R²>0.846).It indicates that we can retrieve VWC with high accuracy by using the four types of vegetation indexes.Among these vegetation indexes,the MSAVI\|based retrieval model achieves the highest accuracy and the root mean square error (RMSE) is only 0.794 kg/m².The study also prove that the developed VWC retrieval model with MSAVI is reliable and an effective way for monitoring spatial variation of regional VWC.

Key words: Vegetation water content Vegetation index Remote sensing ASTER Heihe River Basin

收稿日期: 2014-10-07 出版日期: 2015-12-08

TP 79

基金资助:

国家自然科学基金项目(41101387,91325106)和中国科学院“百人计划”项目 (29Y127D01) 资助。

通讯作者: 黄春林(1979-),男,宁夏青铜峡人,研究员,主要从事陆面数据同化研究。Email:huangcl@lzb.ac.cn。

作者简介: 闻熠(1988-),男,湖北黄冈人,硕士研究生,主要从事定量遥感研究。Email:wenyi@lzb.ac.cn。

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引用本文:

闻熠,黄春林,卢玲,顾娟. 基于ASTER数据黑河中游植被含水量反演研究[J]. 遥感技术与应用, 2015, 30(5): 876-883.

Wen Yi,Huang ChunLin,Lu Ling,Gu Juan. Theretrieval of Vegetation Water Content based on ASTER Images in Middle of Heihe River Basin. Remote Sensing Technology and Application, 2015, 30(5): 876-883.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.5.0876 或 http://www.rsta.ac.cn/CN/Y2015/V30/I5/876

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