干旱遥感监测模型在中国冬小麦区的应用

温度植被干旱指数（TVDI）和植被供水指数（VSWI）由于其物理意义明确，且数据易于获取，因此成为近些年在遥感旱情监测中应用较多的两个模型。为更好地完成遥感监测任务，提高精度，以全国冬小麦主产区为研究区域，利用EOS/MODIS数据，构建两个干旱指数模型，对2009年冬小麦作物主要生长时期进行干旱监测应用，并将其与不同深度土壤湿度进行相关分析、线性拟合比较及应用验证，认为两指数与10 cm深度土壤湿度相关性较好，TVDI大部表现为极显著相关，VSWI的相关性表现差于TVDI。基于土壤湿度的遥感旱情监测，TVDI比VSWI更能体现区域旱情变化趋势，其优势更明显。

Drought monitoring by remote sensing in winter-wheat-growing area of China

Temperature Vegetation Drought Index (TVDI) and Vegetation Supply Water Index (VSWI) have been widely used for drought monitoring in recent years as they have clear significance in physics and they are easy to be gotten. Winter-wheat-growing area of China has been used as research region in this paper, EOS/MODIS data being used to construct those two indexes for drought monitoring during winter wheat growth in 2009. Through analyzing correlation between the two indexes and soil moistures in different depths, linear regression comparison and verification, conclusions have been drawn that both two indexes have better correlations with soil moisture in 10cm depth than that in 20cm depth, and TVDI has excellent correlation with soil moisture, but VSWI does not. As far as soil moisture being concerned for drought monitoring, TVDI surely perform better than VSWI. Furthermore, TVDI could clearly reflect the tendency of regional drought.