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首页> 外文期刊>European Journal of Remote Sensing >Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data
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Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data

机译：进行气候分析并与气候数据关联的地表温度反演

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The aim of this study is to demonstrate the relationship between the long years' monthly average (LYMA) land surface temperature (LST) and the LYMA air temperature (T a ), the total precipitation (P t ), and the relative humidity (RH). Data from 27 meteorological stations in the Eastern Thrace region and corresponding thermal infrared images from Landsat-5 (TM) and Landsat-7 (ETM+) were used in this study. Simple regression models were developed for each meteorological station to predict the LYMA T a , P t and RH based on the LST values. The resulting LST-based prediction models were judged based on the correlation coefficient (r) and root mean square (RMSE). The average correlation and RMSE for the LST-based T a were r = 0.959 and RMSE = 1.771 deg; C. The average correlation and RMSE for the LST-based P t were r = -0.863 and RMSE = 10.098 mm. The average correlation and RMSE for the LST-based RH were r = -0.932 and RMSE = 1.875%. The results indicate that LST can be a good estimator for LYMA T a , P t and RH, and LYMA T a is positively, LYMA P t and LYMA RH are negatively correlated with LYMA LST.
机译：这项研究的目的是证明多年月平均温度（LYMA）地表温度（LST）与LYMA空气温度（T a），总降水量（P t）和相对湿度（RH）之间的关系。 ）。在这项研究中，使用了东色雷斯地区27个气象站的数据以及相应的Landsat-5（TM）和Landsat-7（ETM +）的热红外图像。为每个气象站开发了简单的回归模型，以根据LST值预测LYMA T a，P t和RH。基于相关系数（r）和均方根（RMSE）判断基于LST的预测模型。基于LST的Ta的平均相关度和RMSE为r = 0.959和RMSE = 1.771度; C.基于LST的P t的平均相关性和RMSE为r = -0.863和RMSE = 10.098 mm。基于LST的RH的平均相关性和RMSE为r = -0.932和RMSE = 1.875％。结果表明，LST可以很好地估计LYMA T a，P t和RH，而LYMA T a呈正相关，LYMA P t和LYMA RH与LYMA LST呈负相关。

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