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

MAO Kebiao ZUO Zhiyuan SHEN Xinyi XU Tongren GAO Chunyu LIU Guang

MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. 中国地理科学, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
引用本文: MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. 中国地理科学, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. Chinese Geographical Science, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
Citation: MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. Chinese Geographical Science, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1

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

doi: 10.1007/s11769-018-0930-1
基金项目: Under the auspices of National Natural Science Foundation of China (No. 41571427), National Key Project of China (No. 2016YFC0500203), Open Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS 201515)
详细信息
    通讯作者:

    MAO Kebiao

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

Funds: Under the auspices of National Natural Science Foundation of China (No. 41571427), National Key Project of China (No. 2016YFC0500203), Open Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS 201515)
More Information
    Corresponding author: MAO Kebiao
  • 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 1.4° and 1.9° 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 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.
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Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

doi: 10.1007/s11769-018-0930-1
    基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41571427), National Key Project of China (No. 2016YFC0500203), Open Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS 201515)
    通讯作者: MAO Kebiao

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 1.4° and 1.9° 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 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

English Abstract

MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. 中国地理科学, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
引用本文: MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. 中国地理科学, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. Chinese Geographical Science, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
Citation: MAO Kebiao, ZUO Zhiyuan, SHEN Xinyi, XU Tongren, GAO Chunyu, LIU Guang. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network[J]. Chinese Geographical Science, 2018, 28(1): 1-11. doi: 10.1007/s11769-018-0930-1
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