云下遥感地表温度重构方法研究

Research on reconstructing missing remotely sensed land surface temperature data in cloudy sky

地表温度是研究地-气之间水热平衡的重要参数,对地表温度的全天候获取具有重要意义。热红外遥感可以获得较高分辨率空间全覆盖的地表温度产品,但是有云地区数据缺失问题制约了地表温度遥感产品的全天候应用。文章发展了2种对云下缺失的地表温度进行重构的方法,方法1是借助地表温度同化数据集发展了一种时空匹配的数据融合方法,方法2是将当前在海表参数重构研究中较为流行的经验正交函数插值法(data interpolating empirical orthogonal function,DINEOF)方法应用于地表温度的重构研究中。通过对2017年中国地区地表温度遥感数据的重构对比了2种方法的效果与精度,结果显示:2种方法在整个中国地区不同季节有云条件下精度在2.5~3.5 K之间。方法可为今后地表温度遥感数据的全天候获取提供有益帮助。

Land surface temperature is a key parameter in the study of the balance of water and heart between land surface and atmosphere.Obtainment of land surface temperature under all-weather conditions is very important.Although thermal infrared remote sensing technology can retrieve land surface temperature with high spatial resolution and full space coverage in cloud-free sky,the missing data in cloudy sky limit the all-weather applications of land surface temperature in some areas.This study develops two methods for reconstructing missing land surface temperature in cloudy skies.One of the methods is a space-time matched interpolation method helped with dataset of lands surface temperature assimilation.The other method is by data interpolating empirical orthogonal function(DINEOF),which is already popular in reconstruction of sea surface parameters but is rarely used in reconstruction of land surface parameters.The two methods are evaluated by both remotely sensed data and ground measured data in 2017,and the results demonstrate that both of them are adaptable in all seasons and all over China.The accuracies of two methods are very close and located between 2.5 and 3.5 K in cloudy conditions in four seasons in China.This study aims to give some useful references in the study of obtainment of land surface temperature under all-weather conditions.