基于空间化模拟方法修复遥感地表温度图像的数据缺失

项目名称： 基于空间化模拟方法修复遥感地表温度图像的数据缺失

项目编号： No.41201099

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 地理学

项目作者： 戚鹏程

作者单位： 南阳师范学院

项目金额： 23万元

中文摘要： 由于仪器和成像条件等原因，遥感地表温度(LST)图像往往存在部分区域的数据缺失，严重影响了其使用效果。当前主流的"利用外源图像来填补缺失区"的思路不能很好的适用于这类数据缺失的修复。我们认为，既然LST是地表热量平衡的结果，它包含了太阳辐射、土壤、植被等多方面环境因子的作用，则可利用LST与这些环境因子的关系来模拟数据缺失区的LST。基于此思路，先利用DEM和可见光\近红外遥感图像等资料对可能会影响LST的环境因子（如净短波辐射、地形湿度指数等）进行空间化表达，继而利用待修复图像中数据完好区的LST数据为反应变量，环境因子为预测变量，利用机器归纳学习技术确定反应变量和预测变量之间的非随机关系，然后将此关系推广到预测变量已知而LST未知的空间上，从而模拟出数据缺失区的LST，使图像得以被修复。本研究除具有实用意义外，也是地表参数空间化模拟的一次新探索，具有一定的理论意义。

中文关键词： 地表温度；统计模型；预测变量；修复；尺度转换

英文摘要： The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While much attention has been paid to recovering the optically multispectral bands, few researches have been done to reconstruct the thermal band. Based on the opinion that the land surface temperature is controlled by topography, incoming radiation and atmospheric processes, as well as soil moisture distribution, different land covers and vegetation types, we consider that the land surface temperature could be modeled based on relationships between it and those environmental factors. In this proposal, we propose a new method to restore the land surface temperature images with gaps. Firstly, to spatially distribute the environmental variables, we generate the raster-data layers for net solar radiation, topographic wetness index, precipitation, normalized difference vegetation index, land use and land cover type, soil type and five simple topographic variables including elevation, slope aspect, slope degree, slope shape and slope position. Secondly, we use a non-parametric data mining technique, Regression Tree Analysis, to explore relationships between the response varia

英文关键词： land surface temperature；statistical model；predictive variable；repair；downscaling