中国地理科学 ›› 2018, Vol. 28 ›› Issue (1): 1-11.doi: 10.1007/s11769-018-0930-1

• 论文 •    下一篇

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

MAO Kebiao1,2,3, ZUO Zhiyuan1, SHEN Xinyi4, XU Tongren2, GAO Chunyu1, LIU Guang3   

  1. 1. National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Plan-ning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    2. State Key Laboratory of Remote Sensing Science, Institute of Remote sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100086, China;
    3. College of Resources and Environments, Hunan Agricultural University, Changsha 410128, China;
    4. Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman 73072, USA
  • 收稿日期:2017-07-20 修回日期:2017-11-07 出版日期:2018-02-27 发布日期:2018-01-04
  • 通讯作者: MAO Kebiao E-mail:maokebiao@caas.cn
  • 基金资助:

    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)

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

MAO Kebiao1,2,3, ZUO Zhiyuan1, SHEN Xinyi4, XU Tongren2, GAO Chunyu1, LIU Guang3   

  1. 1. National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Plan-ning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    2. State Key Laboratory of Remote Sensing Science, Institute of Remote sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100086, China;
    3. College of Resources and Environments, Hunan Agricultural University, Changsha 410128, China;
    4. Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman 73072, USA
  • Received:2017-07-20 Revised:2017-11-07 Online:2018-02-27 Published:2018-01-04
  • Contact: MAO Kebiao E-mail:maokebiao@caas.cn
  • Supported by:

    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)

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.

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

Abstract:

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.

Key words: radiometry, Advanced Microwave Scanning Radiometer 2 (AMSR2), passive remote sensing, inverse problem