中文摘要：

归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）目前已被国内外广泛应用于干旱的定量识别与监测,它是利用遥感方法获得的一个衡量地表植被生长状态以及植被覆盖度的指标。因此在气象干旱背景下,对干旱时段的NDVI进行计算,建立玉米干旱风险诊断模型,通过作物对干旱的响应表征农业干旱的风险。本文在气象干旱条件下,从玉米对干旱的响应出发,利用NDVI建立玉米干旱风险诊断模型,评估了研究区典型干旱年内典型干旱时段玉米的干旱风险。利用K均值聚类算法（K-Means）将玉米干旱风险分为低风险、中等风险及高风险3类,运用GIS与RS手段绘制了玉米干旱风险等级图,并对玉米干旱风险的时空格局进行了动态分析。结果表明,在玉米干旱风险时间变化规律上,由于土壤水的影响,玉米干旱风险不能完全与典型干旱年内典型干旱时段的降水距平百分率成反比;从玉米干旱风险空间分布特征来说,受纬度、海拔高度以及防旱抗旱能力的影响,干旱风险表现为南北差异明显。研究玉米干旱风险的时空动态格局,能够为政府部门和田间管理者掌握干旱的发生、发展变化,以及因地制宜地制定防御对策提供科学依据。

英文摘要：

Normalized Difference Vegetation Index （NDVI） is an indicator which is able to measure the growth status and coverage of vegetation on the surface. It has been widely used at home and abroad for the quantitative identification and monitor of drought. Thus against the background of meteorological drought, NDVI in drought period is calculated in order to establish the drought risk diagnosis model of maize which is able to reflect the agricultural drought risk by the response of drought from crops. The NDVI under meteorological drought is used to build the drought risk diagnosis model of maize for assessing the drought risk of maize during the typical drought period of typical years in the study area. K-Means clustering algorithm is used to divide risks into three levels, namely, low, moderate, and high. Then the grade maps of risk are drawn by GIS and RS. And the temporal and spatial distribution of drought risk of maize is dynamically analyzed. The analysis result shows that based on the time changes of maize drought risk, because of water effect on soil, precipitation anomaly percentage, at drought period in typical years is not completely inverse proportional to the drought risk ofmaize; based on the drought spatial distribution features of maize, drought risk, which is affected by the latitude, altitude, and drought control ability, has a large differences between the north and south. The study on the temporal and spatial dynamic distribution of the drought risk of maize could provide a scientific basis for learning the occurrence and development of drought and making defense strategies based on local conditions for government departments and the farmers.