摘要
目前用于中国干旱监测的遥感方法大多是可见光和热红外指数法,受云雨、植被和地形的影响较大,不能满足中国南方地区干旱监测的需求。该研究基于被动微波辐射传输方程,首先构建了基于AMSR-E(advanced microwave scanning radiometer-EOS)数据的地表温度反演模型,R2=0.79,RMSE(root mean square error)为2.54℃,实现了中国地表温度的被动微波遥感监测。然后,拟合了不同下垫面归一化植被指数(normalized difference vegetation index,NDVI)与微波极化差异指数(microwave polarization difference index,MPDI)的关系。在此基础上改进了植被供水指数(vegetation supply water index,VSWI),构建了基于AMSR-E数据的被动微波遥感气象干旱指数,并以中国2009年的旱情为例进行实例验证。研究表明,该干旱指数与AMSR-E L3土壤湿度数据有着显著的负相关关系(R2=0.75),且能基本表征2009年中国实际的气象干旱状况。
Drought is a recurrent complex phenomenon that affects nearly all climatic zones in the world. It is one of the major natural hazards in China, resulting in considerable economic, social, and environmental costs. Preparation for drought should be an important part of policies. Therefore, it is necessary to develop a dynamic and real-time drought monitoring approach in China. Remote sensing technology is one feasible way. However, the main drought indices of remote sensing for monitoring drought dynamics at present are based on visible and near infrared bands. They are always seriously impacted by rainfalls, clouds, vegetation, and terrain conditions. Hence, current drought monitoring technologies cannot meet the needs in south China, where the weather is always cloudy. Passive microwave emissions can penetrate non-precipitating clouds, thereby providing a better representation of land surface parameters under nearly all sky conditions. What is more, daily passive microwave data are available from microwave radiometers as compared to optical sensors like Landsat TM, ASTER, or MODIS, of which only weekly series products are available. So passive microwave remote sensing has unique advantages in long-time drought monitoring over those based on visible and near infrared bands. In this study, we first developed a semi-empirical model for retrieving land surface temperature using the AMSR-E C-band （6.9GHz） and X-band （10.7GHz） passive microwave remote sensing data. The approach provided a good retrieval accuracy of land surface temperature （error=2.54℃,R 2 =0.79）. Next, this paper built an empirical relation between the AMSR-E 6.9GHz Microwave Polarization Difference Index （MPDI） and the NOAA-AVHRR Normalized Differential Vegetation Index （NDVI）. Further, we improved the Vegetation Supply Water Index （VSWI） on the basis of the relation between NDVI and MPDI. Then, we used the new-developed drought index to monitor the drought dynamics of China in 2009. Results showed that many regions and citi
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2013年第16期151-158,F0003,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
广东省科技计划项目(编号:2011B020313001)
中国气象局/河南省农业气象保障与应用技术重点开放实验室开放研究基金课题(编号:AMF201111)
关键词
干旱
监测
遥感
地表温度
植被供水指数(VSWI)
drought
monitoring
remote sensing
land surface temperature
vegetation supply water index
