作为对地探测的遥感技术所获取的遥感图像包含了地面和大气信息,并且主要是地面信息.利用地面反射率分布图像将遥感数字图像中弱的大气信息与强的地面目标信息分离,生成仅仅包含大气信息的大气程辐射遥感图像.利用多(高)光谱遥感数字图像中不同波段的大气程辐射遥感图像可以不受地面干扰地对城市大气污染的类型和程度(气溶胶粒径和含量)进行遥感监测.在大气污染地面观测数据(可吸入颗粒物PM10浓度)的支持下,利用MODIS多光谱遥感数字图像生成的对应多光谱大气程辐射遥感数字图像,以上海市为例进行了城市大气污染遥感监测的原理方法研究.

The state of the atmospheric environment is related to the values of atmospheric path radiation in remote\nsensingdata. As an mi portant information resource, an atmospheric path radiation mi age derived from remotely sensed data\ncan be used in many research fields. These include the research in the transmission properties of the atmosphere, the\natmosphericmodification of remote sensing mi age, the study ofboth regional and urban atmospheric environment, and the\nmonitoring of the atmospheric environmental qualities.\nThe remote sensing data collected by earth observation technology includes predominantly ground information and\nweak atmospheric information. The atmospheric path radiation remote sensing mi age which includes atmospheric\ninformation can be generated by separating the weak atmospheric signals from the strong ground returns in the remote\nsensing data. The key restriction on atmospheric environmental remote sensing lies both on the above process and\nextracting atmospheric information from the remote sensing data. Therefore, it is mi portant to derive the data of\nquantitative atmospheric environmentalpollution and precise atmosphericmodification ofthe remotely sensed digital mi age.\nAftermany years ofprevious research, a scheme of generating the mi age of atmospheric path radiation by computing the\nvalue of atmospheric path radiation of each pixelusing land reflectance is demonstrated in this paper. The principle and\nmethodology for generating the heterogeneous atmospheric path radiation mi age are based on known surface reflectance.\nThe atmospheric path radiation remote sensing mi agewith differentwaveband from multi-spectral orhyper-spectral remote\nsensing mi age canmonitorurban atmospheric pollutionwith different type and degree (size and contentofaerosol) without\nground interference. In thispaper, the research ofurban atmospheric pollutionmonitoring isdeveloped from ShanghaiCity\nas a demonstration, with using the atmospheric path radiation remote sensing mi ages from MODISmulti-spectral remote\nsensing mi age, based on the atmospheric pollution observational data (PM10thickness).\nUsing fuzzy neuralnetworkmethod, the input neuron altogether includesMODIS atmospheric path radiation remote\nsensing mi ages (4wave bands), the traffic density mi ages, and theNDVIgrid mi ages. The output is the single neuron of\nthe synthetic evaluation classification mi ages of atmospheric environmentquality, and the networkmembership function is\nGaussian function and sigmoid excitation function. According to the synthetic evaluation classification mi ages, Shanghai\natmospheric environmentquality is basically the first and the second standard classification. In 6 day-long research tmi e\nintervals, the atmospheric environmentquality spatial and temporal pattern has changed obviously. On 3rd, 9th and on\n10th the atmospheric environment quality are the second level ofmajorities, and on13th themass of atmosphere quality\nachieved the highest level standard, on 21~22nd by gradually drops, the pollutant atmospheric environment quality are\nthe second level gradually.\nDuring the transmission in the atmosphere, electromagnetic wave is influenced by the atmospheric path radiation\n(that is, the upwards dispersion along the opticalpath) which is related to atmospheric environmental quality parameters\nsuch as the atmospheric aerosol optical depth, the total absorbing particulates and contents of the gaseous pollutants. At\nthe same tmi e, the atmospheric path radiation is very useful information in the remote sensing retrieval of regional\natmospheric environmental qualities. It is also a supplement to the technology development of traditional atmospheric\nenvironmental remote sensing. In the future, wewould expect this technique to be used in the retrievalof the atmospheric\nvapor contentof the globe from GPS signals.