【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0.794kg/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。更多还原

【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 0.794kg/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.更多还原