准全球陆表干旱严重程度的多传感器遥感监测方法研究

Monitoring Global Land Surface Drought Severity by Multi-Sensors Remote Sensing Data

在全球气候变暖的背景下,持续的干旱事件将对生态系统和人类社会产生不利影响。尽管存在多源卫星遥感资料及多种干旱指数,然而区域和全球尺度干旱事件的监测仍具有挑战。采用TRMM(Tropical Rainfall Measuring Mission)数据量化降水异常、MODIS(Moderate Resolution Imaging Spectroradiometer)归一化植被指数(Normalized Difference Vegetation Index,NDVI)和陆表温度(Land Surface Temperature,LST)数据表征植被生长异常,构建了一种兼顾降水异常和植被生长状况异常的多传感器陆表干旱严重程度指数(Multi-sensors Drought Severity Index,MDSI)。结果表明:MDSI能够准确检测准全球范围(50°S～50°N,0°～180°～0°)的气象干旱事件,如亚马逊流域2005和2010年干旱、中国川渝地区2006年干旱、中国云南2010年干旱、非洲东部2011年干旱、2012年美国中部干旱等;MDSI与PDSI(Palmer Drought Severity Index)呈现出大致相同的干湿空间格局,并且MDSI有助于湿润地区干旱程度的检测。

In the context of global warming, persistent droughts may cause adverse impacts on ecosystems and human societies. Although several multi-source satellite remote sensing records and types of drought indices exist, detection of droughts at regional to global scales remains a challenge. On the basis of precipitation data of the Tropical Rainfall Measuring Mission （TRMM） used to quantify rainfall anomalies and the Moderate Resolution Imaging Spectroradiometer （MODIS） vegetation index and land surface data used to reflect vegetation growth anomalies, this study develops a Multi-sensor Drought Severity Index （MDSI） to accurately monitor meteorological drought events at almost the global scale （50°S-50°N, 0°-180°-0°）. These events include the 2005 and 2010 droughts in the Amazon; the 2006 drought in Chongqing, China; the 2010 drought in Yunnan, China; the 2011 drought in the eastern Africa; and the 2012 drought in the central parts of America. The spatial distribution patterns of the drought and flood events of the MDSI are essentially the same as those of the Palmer Drought Severity Index （PDSI）. Therefore, the MDSI is useful for detecting drought conditions in humid areas.