Study On Surface Drought Monitoring By Using Remote Sensing Data In Anhui Province

The surface drought is one of the most hazardous natural disasters to human beings in the world, which has the following features:wide-ranging, long-duration and enormous economic damage. For nearly half a century, the ecological environment is becoming more and more serious because of the human over-exploitation the natural resources, therefore, the global climate change and extreme weather events have become increasingly prominent. At the same time, the frequency, strength and scope of surface drought is greater intensity. The traditional surface drought monitoring was simulated by the meteorological data such as the land surface temperature or water content, which is more suitable for small areas, but not for wide range area because of its long laborious and inefficient characteristics. In recent years, the developed rapidly remote sensing technology can provide a wide range of long time series method for the surface drought monitor and research, and it plays an essential role in a wide range of surface drought monitoring study.The scientific progress and main research methods of the Drought Monitoring at home and abroad were briefly reviewed. The popular drought remote sensing monitoring models were described in detailed and the best conditions for each model were also introduced. According to the typical geographical features of Anhui Province, the Temperature Vegetation Drought Index (TVDI) was adopted to monitor the surface drought from2000to2013. The results of four typical study regions (Bozhou City located in Huaibei Plain; Hefei located in Jianghuai hilly area; Anqing located in Dabie Mountains; Xucheng located in south-east Anhui Province) were verified and analyzed.The SDI (Surface Drought Index) inverted by the TVDI method was verified through the Meteorological data released by the Bureau of Meteorology in January, April, and May2011. The results suggested that the remote sensing monitoring was consistent with the drought data in large measure, except for scattered spaces. The correlations between the SDI and NDVI (Normalized Differential Vegetation Index) and LST (Land Surface Temperature) of each March from2000to2013were analyzed. The negative relationships were demonstrated between SDI and NDVI, but did not have a significant correlation between them. The significantly positive relationships were shown between SDI and LST, and correlation coefficients were0.82to0.94. The temporal and spatial variations of SDI were analyzed. During2000-2013in Anhui Province, surface drought was slight in March and the interannual variations were little. The degree of drought declined from north to south along latitude. The frequency of surface drought in Huaibei Plain and Jianghuai Hilly were high, but the frequency and intensity in Dabie Mountains and south-east Anhui areas.In conclusion, TVDI method could be suitable for drought monitoring in complex areas. With the development of technology, the accuracy of remote sensing monitoring will be improved. The combination of tradition methods and remote sensing technology would be widely applied into monitoring and predicting surface drought in future.