基于被动微波与时空联合算法的云下像元LST重建

Reconstruction of LST under the Cloud based on Passive Microwave Remote Sensing and Spatio-temporal Domain Algorithm

地表温度作为衡量地球表面水热平衡的关键参数,具有两大时空分布特征:第一,空间分布一致性,即属性相近的像元地表温度与其地表亮温间的相关关系相对稳定;第二,时间序列周期性,且同一地区时间越接近地表温度值越相似。基于这两大特征将空间统计模型与时间序列滤波相结合,提出了用于云下像元地表温度重建的时空联合算法。以2008年MODIS地表温度产品为研究对象,采用Landsat TM数据和AMSR_E地表亮温数据重建中国9个省份的地表温度值,并与基于MODIS地表分类产品的多通道统计模型重建结果进行对比。实验结果表明,所提算法实用性强,能有效实现大面积复杂下垫面区域的地表温度重建;平均重建误差约为1.2K,相较于基于下垫面分类的多通道统计模型下降了76%,算法精度明显提高。

As a key parameter to measure the water-heat balance of earth surface,land surface temperature has two spatio-temporal distribution characteristics：One is spatial distribution stability,that is,the correlation between the land surface temperature and the land surface bright temperature among those pixels whose properties are similar and relatively stable;the other is time series periodicity,and for one pixel,the time is closer,the temperatures are more similar.based on these characteristics,combined space statistical model with time series filtering,a spatio-temporal domain algorithm was used for the reconstruction of land surface temperature,which was proposed.In the paper,the temperatures were reconstructed in 9provinces（Xinjiang,Qinghai,Sichuan,Yunnan,Henan,Anhui,Hubei,Hunan,Jiangxi）of China with MODIS temperature products（MOD11A2）,Landsat TM data and AMSR_E brightness temperature data（AMSR_EL2A）in 2008.Then,the inversion precisions in 9provinces of the proposed algorithm were calculated based on arithmetic average method,and compared with the reconstruction results of multi-channel statistical model based on the surface classification products from MODIS（MOD12）.The results show that the proposed algorithm is practical,and that can be applied in any kind of LST images even there are lots of null values;and the average inversion error of this method for China with MOD11A2 products is about 1.2K,decreased by 76%compared with multi-channel statistical model,therefore the reconstruction accuracy is significantly improved.