通过遥感获取地表温度(地温，LST)可以弥补气象站地温数据局地性的不足。但受某些因素影响，遥感MODIS LST标准产品中的影像存在“云污染”等噪音像元以及空缺值，影响了LST数据应用。本文在利用LST产品的质量标记信息(QA)和直方图极值去除法过滤低质量、不可靠LST像元的基础上，提出了一种基于DEM-LST回归关系的滑动窗口空间重建算法，对2008 年青藏高原东北部TERRA/AQUA 上共4 个温度通道的MODIS LST进行了重建，得到空间完整的LST 时间序列。将重建后的LST 与研究区11 个气象站地表温度数据(T)的比较表明，在8 day 合成序列上LST-T 一致性很好，平均相关系数达0.96，平均绝对误差为2.02℃；LST与T在月、年的尺度上更没有显著差异(平均绝对误差分别为1.55℃和0.60℃)。LST与T存在的差异与两者的时空定义的不一致性有必然的联系，但是仍然存在的低水平噪音表明若需要更高精度的LST数据需要更细致的去云处理。
Land surface temperature (RS-LST) derived from remotely sensed data is a good alternative because traditional LST data from meteorological stations have limitations in terms of locality, accessbility and cost. Yet MODIS standard LST products from NASA may suffer from noises from various sources including‘cloud contamination’, which greatly degrade the LST quality and hamper its efficient applications. The paper presents a novel algorithm which can reconstruct complete LST image based on regression analysis of LST with elevation in each sliding window of the original image, after filtering low-quality and unreliable pixels with Quality Asessment (QA) information and Histogram Outliers Removing method. Terra/Aqua MODIS-LST with overall four temperature channels in the northeast Qinghai-Tibet Plateau in 2008 are reconstructed using this method. Comparison of reconstructed 8-day composite LST data with corresponding landsurface temperature (T) observations from eleven meteorological stations in the study region indicates that LST is significantly correlated with T with an average correlation coefficient of 0.96 and a mean abosolute difference (MAE) of 2.02℃. When aggregated to monthly serials and yearly serials, LSTs and Ts show no significant differences in Paired-T-Test (with MAE of 1.55℃ and 0.60℃, respectively). The differences between LSTs and Ts have certain correlations with their different spatial and temporal definations; however some residual noises existing in the reconstructed LSTs indicate more meticulous algorithem needed to work out more accurate RS-LST data.
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