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Chinese Geographical Science

, Volume 28, Issue 1, pp 1–11 | Cite as

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

  • Kebiao Mao
  • Zhiyuan Zuo
  • Xinyi Shen
  • Tongren Xu
  • Chunyu Gao
  • Guang Liu
Article

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.

Keywords

radiometry Advanced Microwave Scanning Radiometer 2 (AMSR2) passive remote sensing inverse problem 

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Notes

Acknowledgements

The author would like to thank for Ashcroft, P., and F. Wentz. 2003, updated daily. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001, September to October 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media, NASA provides MODIS land surface temperature product.

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Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Kebiao Mao
    • 1
    • 2
    • 3
  • Zhiyuan Zuo
    • 1
  • Xinyi Shen
    • 4
  • Tongren Xu
    • 2
  • Chunyu Gao
    • 1
  • Guang Liu
    • 3
  1. 1.National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina
  2. 2.State Key Laboratory of Remote Sensing Science, Institute of Remote sensing and Digital Earth ResearchChinese Academy of Science and Beijing Normal UniversityBeijingChina
  3. 3.College of Resources and EnvironmentsHunan Agricultural UniversityChangshaChina
  4. 4.Hydrometeorology and Remote Sensing LaboratoryUniversity of OklahomaNormanUSA

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