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利用静止气象卫星红外通道遥感监测中国沙尘暴
引用本文:胡秀清,卢乃锰,张鹏.利用静止气象卫星红外通道遥感监测中国沙尘暴[J].应用气象学报,2007,18(3):266-275.
作者姓名:胡秀清  卢乃锰  张鹏
作者单位:1.中国气象局中国遥感卫星辐射测量和定标重点开放实验室 国家卫星气象中心, 北京 100081
摘    要:气象卫星的红外窗区通道 (8~12 μm) 对于通常大气气溶胶几乎没有响应, 但对于较大颗粒且浓度较强的沙尘气溶胶, 尤其是沙尘暴有明显的信号反应。空气中的沙尘在红外分裂窗通道表现出两个特征:一是对地表发射到空间的红外信号产生衰减, 造成卫星探测到的地气系统亮温降低, 这就是所谓的红外差值沙尘指数IDDI; 二是沙尘粒子在红外分裂窗两个通道的比辐射率不同, 11 μm比12 μm的比辐射率低, 从而造成这两个通道的亮温差是负值。基于这两个特征和沙尘多通道光谱聚类法, 针对静止气象卫星观测数据进行了沙尘暴卫星遥感监测业务算法开发, 输出沙尘暴监测产品和红外差值沙尘指数产品, 这一算法不仅用于已经退役的GMS-5卫星, 而且应用于正在运行的静止气象卫星FY-2C, 它还为沙尘暴的定量或半定量遥感提供参考借鉴。

关 键 词:静止气象卫星    沙尘暴遥感    红外差值沙尘指数    分裂窗亮温差
收稿时间:2006-03-24
修稿时间:2006-03-242007-01-17

Remote Sensing and Detection of Dust Storm in China Using the Thermal Bands of Geostationary Meteorological Satellite
Hu Xiuqing,Lu Naimeng,Zhang Peng,National Satellite Meteorological Center,Beijing ;.Remote Sensing and Detection of Dust Storm in China Using the Thermal Bands of Geostationary Meteorological Satellite[J].Quarterly Journal of Applied Meteorology,2007,18(3):266-275.
Authors:Hu Xiuqing  Lu Naimeng  Zhang Peng  National Satellite Meteorological Center  Beijing ;
Institution:1.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA), National Satellite Meteorological Center, Beijing 1000812.Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101
Abstract:The earth observation system from space includes two kinds of platform:Polar orbit and geostationary satellite. Optical sensors onboard polar satellite have the advantage of high spatial resolution and more spectral bands in visible to infrared regions such as AVHRR/NOAA, MODIS/EOS, MVIS/FY-1C/1D etc. But it can conduct only twice a day. It is not enough for hazardous dust weather whose time scale is very short and moves quickly. It is difficult to understand dust moving and evolution as a whole using polar sensors' observation. Geostationary Meteorological Satellite such as GMS-5, FY-2, GOES and Meteosat can observe the earth continuously all daytime and night at high temporal resolution. Developing an algorithm for remote sensing and monitoring dust event will be very useful for forecast model, environment and climate monitoring and scientific research. Dust cloud is not easy to be discriminated like other strong weather phenomena such as typhoon. It is to be understood of the optical and radiative mechanism of airborne dust. The base theories of remote sensing of airborne dust will be introduced using the thermal and other bands of geostationary sensors. Observation signal of thermal infrared window (8—12 μm) bands have almost no sensitivity to general aerosols with small and thin particle sizes. There are some sensitivity to large and strong dust particles, especially in dust storm or heavy dust storm. The airborne dust can exert two kinds of features on thermal infrared observation signals. Firstly the infrared radiance of ground target into space will be reduced by dust layer and the brightness temperature of the observed underlying target be decreased. This kind of temperature reduction is called infrared difference dust index (IDDI). Secondly the emissivities of airborne dust are different in these two split window bands and produce the negative brightness temperature difference for dust targets. Based on these above theories and the traditional sophisticated multispectral classification technique, a set of algorithms for automatically detecting dust storm is developed using observation data of geostationary meteorological satellite. The first step of this algorithm is to extract the data of all bands from normalized disk image of observation and to conduct the interested region projection and calibration processing. The data reading is not only from present time but also from previous ten days for integrating background brightness temperature image. It is ready for IDDI image integration. The second step is cloud mask processing which is very important for dust discrimination. And then dust determination is conducted using above mentioned theories and methods. It is the key part of this algorithm. The last step is the production output including several types of dust remote sensing. This algorithm can obtain ideal result of dust storm detection and the product of IDDI. This algorithm has already been experimentally run in National Satellite Meteorological Center since 2001. It is not only used for dust detection from data of Japanese GMS-5, but also becomes a useful operational production of the new geostationary meteorological satellite FY-2C in orbit in 2005. In addition, it provides extended potential of quantitative or semi-quantitative remote sensing of dust storm.
Keywords:geostationary meteorological satellite  remote sensing of dust storm  infrared difference dust index(IDDI)  brightness temperature difference in split windows
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