【摘要】 温度植被干旱指数(TVDI)是利用光学遥感进行干旱监测常用的遥感指数。但前人更多的是利用单时次的遥感数据计算TVDI,这使不同时间TVDI的可比性不高。利用历史遥感数据构建了NDVI-LST(LST,地表温度)、EVI(增强植被指数)-LST、SAVI(土壤调整植被指数)-LST3种特征空间,讨论了TVDI方法在甘肃省陇东地区的适用性。结果表明:(1)特征空间法可用于甘肃省陇东地区土壤水分的监测,EVI-LST特征空间构建的TVDI与土壤相对湿度RSM的相关性更高;(2)DEM(数字高程模型)对特征空间法有一定的改进作用,LST经过DEM订正后,一方面特征空间干、湿边的拟合程度提高,另一方面TVDI与RSM的相关性得到一定程度的提高;(3)利用历史遥感数据建立的特征空间提高了TVDI的时空可比性:TVDI能够较好的指示出研究区每年RSM的空间分布特征及不同年份间RSM的差异。更多还原

【Abstract】 Clear principle and concise physical meanings make Temperature Vegetation Dryness Index(TVDI)be a wildly used method in drought monitoring.It was not temporal comparable that building feature space by using single time remote sensing data in most former studies.In this paper,NDVI-LST,EVI-LST and SAVI-LSTfeature space is building by history MODIS data and the applicability is discussed.The results showed that:(1)In over all,TVDI method can be used to monitoring the Relative Soil Moisture(RSM)status in Longdong area.The correlation between TVDI calculated by EVI-LSTspace and RSM is highest.(2)Land Surface Temperature(LST)corrected by DEM improved TVDI method.In one hand,goodness of fitting of the dry and wet edge is improved.On the other hand,the correlation between TVDI and RSM is improved in a manner.(3)Building feature space though history remote sensing data is a reasonable and feasible method.Temporal and spatial comparable are enhanced by this method.TVDIindicated the history RSM spatial distribution every single year well and the variance of RSM in different year well too.更多还原