空气温度是一个非常重要的气候变量，通常由气象台站观测获得。对其时空特征的精确估算是很多模型的基础，但是由于台站分布密度的不均和研究区复杂的地形，往往使其空间化的结果较差。目前，随着遥感技术的发展，使用热红外遥感数据估算的地表温度，结合地面观测数据，建立回归模型可以提高区域空气温度估算的精度。由于云和其它大气因素会影响遥感反演的地表温度数据结果，因此本研究本文将2001-2010年的LST历史数据作为先验知识，用以建立LST背景库，并提出了基于LST背景库的Savitzky-Golay(SG)滤波算法来实现LST时间序列数据的重建工作。将重建后的 LST 与研究区12个气象站空气温度数据进行了时序分析和回归分析，结果表明在月尺度合成序列上LST-TA的一致性较好，且具有非常好的线性相关关系，80%的台站的决定系数高于0.5。通过对比分析发现，加入植被指数（NDVI）的各月空气温度回归模型比直接用LST建立的回归模型精度更高。因此，本研究使用LST-NDVI模型对澜沧江流域2010年12个月份的空气温度进行空间化制图，并分析了其年内时空格局特征。%Air temperature is an important climatological variable and is usually measured in meteorological stations. Accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines, butlow station density and complexity of the terrain usually lead to significant errors and unrepresentative spatial patterns over large areas. Fortunately the current studies have shown that the regression models can help overcome the problem with the help of time series remote sensing data. However, noise induced by cloud contamination and other atmospheric disturbances variability impedes the application of LST data. An improved Savizky-Golay (SG) algorithm based on the LST background library is used in this paper to reconstruct MODIS LST product. Data statistical analysis included 12 meteorological stations and 120 reconstructed MODIS LST images of the period from 2001 to 2010. The coefficient of correlations (R2) for 80% of the stations was higher than 0.5 (below 0.5 for only 2 stations) which ilustrated that there is a considerably close agreement between monthly mean TA (air temperature) and the reconstructed LST in the Lancang River basin. Comparing to the regression model for every month with only LST data, the regression model with LST and NDVI had higherR2 and RMSE. Finally, the LST-NDVI regression method was applied as an estimate model to produce distributed maps of air temperature with month intervals and 1 km spatial in the Lancang River basin of 2010.
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