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地表发射率是地表温度遥感反演中不可缺少的参数之一。常规获取地表发射率的遥感方法中,存在温度和发射率病态反演的问题;而实验室实测方法在恒定的自然条件下测定,对于遥感实用有一定的局限性。为解决上述问题,本研究利用温度和发射率相互耦合的关系,剔除温度效应的影响,提高发射率的精度,进而提高遥感反演地表温度的精度。对于不同地表物质而言,发射率和温度耦合关系又不同,本研究针对典型的城市人造地表类型之一——水泥路面,基于合理的自然状态下水泥路面发射率和温度观测实验,筛选理想大气环境下实测数据;利用最优发射率与温度分离算法取代光谱仪自带算法,提取精度较高发射率数据;分析时序水泥路面温度和发射率数据的耦合关系,建立耦合模型,并进行验证。研究结果表明:水泥路面的发射率二阶导数与温度相关性最高,相关系数为-0.925 1。以发射率二阶导数为自变量的逐步回归模型为最佳关系模型,判定系数R2达到0.886,验证结果的均方根误差RMSE为0.292 1。水泥路面温度与其发射率耦合关系模型的建立为提高遥感反演地表温度的精度提供了一种途径。
Surface emissivity is one of the indispensable parameters of remote sensing of surface temperature. Conventional remote sensing methods for obtaining the emissivity of the surface have the problems of ill-posed inversion of temperature and emissivity. However, the laboratory measurement method is under constant natural conditions and has certain limitations for remote sensing applications. In order to solve the above problems, this study uses the mutual coupling of temperature and emissivity to eliminate the influence of temperature effect and improve the accuracy of emissivity, so as to improve the accuracy of surface temperature inversion by remote sensing. For different surface materials, the coupling relationship between emissivity and temperature is different. In this study, one of the typical urban man-made surface type - cement pavement, based on reasonable natural conditions of cement pavement emissivity and temperature observation experiments, screening the ideal atmosphere Environment. The optimal emissivity and temperature separation algorithms were used instead of the self-contained algorithm of the spectrometer to extract the emissivity data with high accuracy. The coupled relationship between time series of cement pavement temperature and emissivity data was analyzed, and the coupled model was established and verified. The results show that the second derivative of emissivity of cement pavement has the highest correlation with temperature, the correlation coefficient is -0.925 1. The stepwise regression model with the second derivative of the emissivity as the independent variable is the best correlation model, the coefficient of determination R2 reaches 0.886, and the root mean square error (RMSE) of the verification results is 0.292 1. The establishment of the coupling model between the cement pavement temperature and its emissivity provides a way to improve the precision of the surface temperature of the remote sensing inversion.