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| 多源遥感反演大气水汽和地表温度研究 | |
| 其他题名 | Water vapor and land surface temperature inversion from multi-source Remotely Sensed data |
| 刘三超 | |
| 出版年 | 2007 |
| 学位类型 | 博士 |
| 导师 | 柳钦火 |
| 学位授予单位 | 中国科学院遥感与数字地球研究所 |
| 中文摘要 | 人口、资源、环境已成为影响中国经济可持续发展的重大问题。本文针对环境与减灾小卫星红外相机数据的定量研究和应用需求，以大气水汽和地表温度反演为主要研究内容，根据红外相机数据的特点，研究了利用多种数据源演提供大气和地表参数的方法，开发了适合中国区域的针对环境与减灾小卫星的宽通道地表温度反演算法。\n大气水汽在环境监测和气候变化研究中有重要意义，也是环境与减灾小卫星地表温度产品的重要输入参数。本文对多种数据源的大气水汽反演方法进行了研究：（1）利用改进的Langley法对地基光度计进行定标，反演了大气可降水量；（2）分析了Hyperion反演大气水汽的通道选择，提出了改进的多波段联合反演大气可降水量，误差分析表明，仪器特性对于高光谱数据反演大气水汽有重要影响。该方法可应用于环境与减灾小卫星超光谱成像仪的水汽反演；（3）MODIS分辨率较粗，地表反射率的非线性变化是影响水汽反演的主要误差源，本文在Terra-MODIS水汽算法的基础上，提出区分水体、植被和其他地物，考虑通道响应函数差异，得到了适合Terra和Aqua MODIS数据的水汽反演方法；（4）本文比较多种数据源反演水汽的结果，分析了水汽反演的误差源，表明大气模式对近红外水汽反演影响很小，MODIS水汽数据可以达到环境与减灾小卫星地表温度反演精度要求；（5）利用地基和MODIS反演结果，分析了水汽日变化特征以及北京市大气水汽空间分布特征。\n地表温度是研究陆面过程和地表能量平衡的关键参数。本文针对环境与减灾小卫星红外数据宽通道单波段以及宽视场的特点，提出利用地面台站和遥感数据结合提供大气参数，以及红外光谱库结合可见光近红外数据估算比辐射率，并改进了已有的单窗算法和普适性单通道算法，进而提出环境与减灾小卫星地表温度反演的宽通道算法。该方法利用标准大气、TIGR数据、中国平均大气模式和中国区域气象台站廓线数据进行辐射传输模拟，研究发现，如果考虑气温的影响可以提高热带和中纬度地区透过率和大气辐射计算的精度；算法改进了单窗算法，考虑大气上行辐射和下行辐射的差异，通过建立中国不同区域的大气下行和上行辐射的关系，对大气效应进行参数化，得到天底点地表温度反演的宽通道算法；然后分析了大气透过率和大气辐射的角度变化规律，解决了倾斜角度下大气校正问题；最后通过建立考虑光谱响应函数的宽通道辐亮度模型反演出地表温度。分析了传感器光谱响应特性对温度反演结果影响，发现仪器光谱特性测量和仪器定标对红外定量研究有重要意义。通过算法的参数敏感性分析，结合气象台站廓线数据验证，并以Landsat ETM+数据为例利用顺义实验同步大气探空数据和地面温度测量数据，与单窗算法和普适性单通道算法进行比较，表明本文提出的宽通道地表温度反演算法具有更高精度，更适合于中国地区地表温度反演。\n红外遥感数据在环境和灾害研究中有重要应用价值。MODIS数据通道较多，结合Terra和Aqua两颗卫星一天可以获取同一地区多景数据。本文提出了利用双星MODIS红外通道亮温差的方法来监测沙尘暴，对于研究中国北方沙尘暴的发生、起源以及动态变化有重要的意义。高分辨率红外数据对城市热环境研究有重要意义，本文以特大城市北京和西北干旱区的张掖绿洲为研究区，用TM/ETM+数据研究了城市热岛效应，分析了地表温度和下垫面的关系。通过本文的研究，表明环境与减灾卫星的红外相机数据产品在环境和减灾监测等应用领域具有广阔前景。 |
| 英文摘要 | Population, resources and environment are three main issues for every country. In China, environment and disaster also become the main obstacles to social sustainable development. In this article, we focus on the application of environment and disaster small satellite data. Our main work is to develop a land surface temperature (LST) algorithm to the satellite thermal data. According to the characteristics of data, we combine different sources data to supply atmospheric and land parameters. A broad band single channel algorithm is presented in this paper which is could be used in China.\nWater vapor is important to study climate change, and it is also a main input parameter in LST inversion algorithm. This paper proposes different methods to calculate multi-sources data water vapor. For ground based sun-photometer data，modified Langley method could be used to calibrate instrument and inverse precipitable water (PW). For Hyperion data, we analyze suitable channel selection and use DCIBR algorithm to obtain PW. MODIS spatial resolution is relative coarse, land surface reflectance may influence the precision of algorithm. Make land classify into water body, vegetation and non-vegetation and considering the different spectral response, three bands algorithm could be used to both Terra and Aqua MODIS data. We also compare the results of different sources data and found that MODIS near infrared images could supply high precision input data for LST inversion. Finally, we analyze daily variation and spatial distribution characteristic of PW in Beijing.\nLST is a key parameter in the study of land surface process and energy balance. To HJ-1B thermal data, we focus our work on two main problems, single band and broad field of view. Input atmospheric data could be obtain by combined ground meteorology data and above mentioned multiple sources water vapor data. Using infrared spectral database and VNIR remote sensed data, we can also obtain broad band emissity. By thermal radiative transfer simulation using standard atmospheric data, TIGR data and 20 stations sounding balloon data in China, we firstly put forward atmospheric radiation parameterized method in nadir view data. Then by analyzed the relationship between transmittance and atmospheric radiation view angle, the method solves the problem of atmospheric correction under different view angle. Sensitivity analysis indicates spectral response may greatly influence instrument signal. We validate our broad band LST algorithm by means of sounding data of Beijing in 2004, and also compare our algorithm with mono-window algorithm and general single channel algorithm for Landsat ETM+ in Shunyi, and find our algorithm can used in China.\nIn this paper, we monitor a dust storm process in north China using tri-spectral brightness temperature differences technique of MODIS data. By means of both Terra and Aqua satellite platform data, more useful information could be supplied to government. Using TM/ETM+ thermal data, we analyze urban heat island effect both in Beijing and Zhangye，and find the LST distribution has high relationship with ground characteristic. The results show the small satellite data has great potential in environment and disaster research. |
| 中文关键词 | 遥感 ; 热红外 ; 地表温度 ; 大气校正 ; 环境和灾害监测 |
| 英文关键词 | Remote sensing thermal infrared land surface temperature atmospheric correction environment and disaster research |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院遥感应用研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/286603 |
| 推荐引用方式 GB/T 7714 | 刘三超. 多源遥感反演大气水汽和地表温度研究[D]. 中国科学院遥感与数字地球研究所,2007. |
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