本文關(guān)鍵詞： MODIS圖像 AOT PM2.5 云量 云檢測(cè)　出處：《安徽師范大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：對(duì)地觀測(cè)衛(wèi)星可以對(duì)地球進(jìn)行長(zhǎng)時(shí)間大范圍的監(jiān)測(cè),為人們提供大量重要的觀測(cè)資料。利用衛(wèi)星數(shù)據(jù)研究云主要從兩個(gè)方面考慮,一方面,云是遙感應(yīng)用領(lǐng)域中一個(gè)主要干擾因素,影響對(duì)地表的監(jiān)測(cè);另一方面,云在地氣系統(tǒng)中起到重要的調(diào)節(jié)作用,為精確預(yù)測(cè)氣候變化,構(gòu)建數(shù)值預(yù)測(cè)模型時(shí)需要大量的云參數(shù)資料。本論文的研究?jī)?nèi)容有:首先,利用去除云像元的MODIS L1B影像反演北京市上空的氣溶膠光學(xué)厚度,并與地面監(jiān)測(cè)PM2.5質(zhì)量濃度數(shù)據(jù)進(jìn)行回歸擬合出四個(gè)經(jīng)驗(yàn)預(yù)測(cè)模型,從而完成對(duì)城市空氣質(zhì)量評(píng)價(jià)模型的構(gòu)建這一遙感應(yīng)用;其次,通過(guò)MODIS云檢測(cè)產(chǎn)品反演高分辨率的云量資料,并與地面觀測(cè)云量進(jìn)行對(duì)比分析,從而一方面為氣候變化研究提供高分辨率的云量資料,另一方面對(duì)云檢測(cè)產(chǎn)品精度進(jìn)行分析評(píng)價(jià),為改進(jìn)云檢測(cè)算法模型提供方向;最后對(duì)MODIS云檢測(cè)方法進(jìn)行探究。本論文結(jié)論主要有以下幾點(diǎn):1.構(gòu)建的PM2.5質(zhì)量濃度的四個(gè)經(jīng)驗(yàn)?zāi)Ｐ偷木确謩e為R2=0.818,R2=0.750,R2=0.699和R2=0.629。其中,二次模型效果最好,能夠提供快速而較經(jīng)濟(jì)的PM2.5空間分布信息。2.利用四個(gè)模型和2012年10月11日的MODIS影像反演了PM2.5質(zhì)量濃度,并與地面監(jiān)測(cè)的PM2.5進(jìn)行對(duì)比,有50%,46.4%,46.4%和39.3%的站點(diǎn)分別在四個(gè)預(yù)測(cè)模型的誤差范圍內(nèi)。3.利用MODIS云檢測(cè)產(chǎn)品反演了中國(guó)區(qū)域近十年的上午星和下午星的高分辨率云量,統(tǒng)計(jì)分析其時(shí)間序列發(fā)現(xiàn):總云量變化趨勢(shì)為略有下降,并且下午星云量比上午星要多,與地面觀測(cè)的日均云量的相關(guān)性較好,例如2012年的相關(guān)系數(shù)為0.878。4.對(duì)比衛(wèi)星反演和地面觀測(cè)的月平均云量發(fā)現(xiàn):在寒季兩者相差較大,可能是植被覆蓋較少或冰雪覆蓋導(dǎo)致地表反射率較大,被誤判為云。5.經(jīng)過(guò)在6個(gè)不同下墊面區(qū)域的統(tǒng)計(jì)分析實(shí)驗(yàn)表明:去除寒季數(shù)據(jù)后,中國(guó)北方的幾個(gè)研究區(qū)域的兩種云量的相關(guān)性增加明顯,特別是東北森林區(qū)域,而在南方研究區(qū)域沒(méi)有明顯變化,可能是積雪造成云的誤判。6.通過(guò)云檢測(cè)測(cè)試,發(fā)現(xiàn)利用亮溫差9.37.3BTBT?檢測(cè)能夠有效抑制將荒漠或植被稀疏的亮地表區(qū)域誤判為云。
[Abstract]:Earth observation satellites can monitor the Earth for a long time and on a large scale, providing people with a large amount of important observation data. The use of satellite data to study clouds is mainly considered from two aspects: on the one hand, Clouds are a major interference factor in remote sensing applications, affecting the monitoring of the surface. On the other hand, clouds play an important role in regulating the earth and atmosphere systems to accurately predict climate change. A large amount of cloud parameter data are needed to construct a numerical prediction model. Firstly, the aerosol optical thickness over Beijing is retrieved by using MODIS L1B image with cloud pixel removal. Four empirical prediction models were fitted by regression with ground monitoring PM2.5 mass concentration data, thus the remote sensing application of urban air quality evaluation model was completed. Secondly, high resolution cloud data were retrieved through MODIS cloud detection products. And compared with the cloud amount observed on the ground, on the one hand to provide high-resolution cloud data for the study of climate change, on the other hand, to analyze and evaluate the accuracy of cloud detection products, so as to provide a direction for improving the cloud detection algorithm model. Finally, the MODIS cloud detection method is explored. The main conclusions of this paper are as follows: 1. The accuracy of four empirical models of PM2.5 mass concentration are R2O0.818R2O0.750R2O0.699 and R2O0.629. among them, the quadratic model has the best effect. It can provide fast and economical spatial distribution information of PM2.5. Using four models and MODIS image of October 11th 2012, the mass concentration of PM2.5 is retrieved and compared with PM2.5 monitored on the ground. 46.4% and 39.3% of the stations are within the error range of four prediction models, respectively. Using the MODIS cloud detection products, the high resolution cloud amounts of the morning and afternoon stars in the last ten years in the Chinese region have been inversed. The statistical analysis of the time series shows that the change trend of the total cloud amount is a slight decrease, and the number of nebula in the afternoon is more than that of the morning star, and the correlation with the daily average cloud amount observed on the ground is better. For example, in 2012, the correlation coefficient was 0.878. 4. By comparing the monthly average cloud cover of satellite inversion and ground observation, it was found that in cold season, the difference between the two is large, which may be caused by less vegetation cover or greater surface reflectivity due to snow and ice cover. The results of statistical analysis in six different underlying areas show that after removing cold season data, the correlation between the two types of cloud cover in several study areas in northern China has increased significantly, especially in the northeast forest region. However, there is no obvious change in the southern study area, which may be caused by snow. 6. Through the cloud detection test, it is found that 9.37.3 BTT BTT can be used as a result of bright temperature difference. The detection can effectively restrain the misjudgment of the desert or vegetation sparse bright surface area into cloud.
【學(xué)位授予單位】：安徽師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：X823;P426.5

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