Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

文献类型： 外文期刊

第一作者： Mao, Kebiao

作者： Mao, Kebiao;Zuo, Zhiyuan;Gao, Chunyu;Mao, Kebiao;Xu, Tongren;Mao, Kebiao;Xu, Tongren;Mao, Kebiao;Liu, Guang;Shen, Xinyi

作者机构：

关键词： radiometry; Advanced Microwave Scanning Radiometer 2 (AMSR2); passive remote sensing; inverse problem

期刊名称：CHINESE GEOGRAPHICAL SCIENCE （ 影响因子：2.892； 五年影响因子：2.721 ）

ISSN： 1002-0063

年卷期： 2018 年 28 卷 1 期

页码：

收录情况： SCI

摘要： It is more difficult to retrieve land surface temperature (LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4A degrees and 1.9A degrees when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8A degrees through a comparison between the retrieval results with ground measurement data from meteorological stations.

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