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.
周芳成, 唐世浩, 韩秀珍, 宋小宁, 曹广真. 云下遥感地表温度重构方法研究[J]. 国土资源遥感, 2021, 33(1): 78-85.
ZHOU Fangcheng, TANG Shihao, HAN Xiuzhen, SONG Xiaoning, CAO Guangzhen. Research on reconstructing missing remotely sensed land surface temperature data in cloudy sky. Remote Sensing for Land & Resources, 2021, 33(1): 78-85.
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2021, Vol. 33 
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