基于被动微波的寒旱区地表温度反演

Land Surface Temperature Retrieved from Passive Microwave Data over Cold and Arid Regions

以各频段水平极化和垂直极化发射率间的相关关系为条件,利用被动微波数据反演地表温度.算法既解决了地表温度反演过程中发射率难以确定的问题,又克服了热红外遥感受大气影响较大的缺点,其物理意义清晰,计算简便.算法以MODIS温度产品为评价标准,对36.5 GHz和89 GHz反演结果进行分析.结果表明:89 GHz亮温数据反演精度高于36.5 GHz;与耕地和草场反演精度相比,裸土和山地反演精度较高.其原因在于高频数据穿透能力较低,能更好地表达地表温度.同时,低频数据相对高频更容易受到地表土壤水分变化的影响,发射率相对不够稳定,对反演结果有一定影响.

Land surface temperature(LST) is a critical parameter for global climate research.Many investigations have been made for LST retrieval using thermal remote sensing data.In fact,thermal images provided by satellite or aircraft platforms are deeply influenced by atmosphere condition.Thus accuracy of thermal remote sensing algorithms will be low if the weather is not clear.Microwave could penetrate cloud,and is hardly influenced by atmosphere.So it has the particular advantage for LST estimation. In this study...