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作者	单位
孙晨红，杨贵军，董燕生，杨小冬，陈晓宁，徐鹏，王晨	（1.西安科技大学，陕西西安710054； 2.国家农业信息化工程技术研究中心，北京 100097； 3.农业部农业信息技术重点实验室，北京 100097 ）

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中文摘要:

为了探讨“旱冻交加”的气候条件下遥感监测小麦苗情的可行性，以河南省冬小麦为例，利用多时相MODIS为遥感数据源，引入了广泛用于小麦苗情监测的距平植被指数（AVI），同时基于植被健康状态指数（VHI）和晚霜冻害综合指数（I），构建了旱冻双重胁迫条件下的小麦苗情综合指数（CI），以反映冬小麦拔节期的生长状况，并分别利用实测样点苗情分类和距平植被指数对结果进行验证。结果表明，CI的总体分类精度为85.00%，Kappa系数为0.74；冬小麦总茎数与CI呈线性正相关关系，拟合方程为y=8.732CI+1.256，决定系数为0.605 3；与实测样本对比，CI预测精度为73.30%，苗情分类分级更符合小麦生长实际情况。因此认为旱冻双重胁迫下可以利用CI对冬小麦苗情进行监测。

英文摘要:

In order to explore the feasibility to monitor the growth of wheat seedlings under the stresses of “drought and freezing” by remote sensing. In this paper, the winter wheat in Henan Province was taken as the subject and multi-temporal MODIS as the remote sensing data sources, the Anomaly Vegetation Index (AVI) was introduced to monitor wheat seedling condition. Based on Vegetation Health Index (VHI) and Last Frost Freezing Composite Index (I), the Wheat Seedlings Composite Index (CI) was established to reflect the growth situation of winter wheat at jointing stage under double stresses of drought and freezing injury. Then the monitoring results obtained were validated by the classification of measured ground-sample and AVI, respectively. The results showed that total classification accuracy of CI was 85.00%, Kappa coefficient was 0.74. The relationship between the total tillers of winter wheat and CI were positively and linear correlated with a regression equation y=8.732CI+1.256, with R=0.605 3.Compared with the measured ground-sample, the accuracy of CI reached 73.30%, and the monitoring results of CI was more consistent with the measured ground-sample than AVI. Therefore CI could be used to monitor winter wheat growth conditions under double stresses of drought and freezing injury.

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