предыдущий|следующий

Near-surface freeze/thaw state mapping over Tibetan Plateau

  • role： First author第一作者
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing100049, China

  • Email：zhangziqian17@mails.ucas.ac.cn
  • Introduction：张子谦，1995年生，男，硕士研究生，研究方向为微波遥感理论及应用。E-mail: zhangziqian17@mails.ucas.ac.cn
ZHANG Ziqian123,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhaotj@aircas.ac.cn
  • Introduction：赵天杰，1985年生，男，副研究员，研究方向为微波遥感理论及应用。E-mail: zhaotj@aircas.ac.cn
ZHAO Tianjie2*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SHI Jiancheng2,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

LI Yulin1,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

RAN Youhua1,  
  • Affiliation：

    Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing100101, China

CHEN Yingying4,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

ZHAO Shaojie5,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

WANG Jian6,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

    University of Chinese Academy of Sciences, Beijing100049, China

NING Zhiying13,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

    University of Chinese Academy of Sciences, Beijing100049, China

YANG Hongling13,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou730000, China

    University of Chinese Academy of Sciences, Beijing100049, China

HAN Dan13

реферат

The Tibetan Plateau (TP) area is recognized as the “Water Tower of Asia” owing to its significant climatic and hydrological characteristics. However, land surface freeze/thaw transition can hardly be detected in this area because of its harsh and complex geographical environment. This study intends to establish an algorithm to identify near-surface freeze/thaw state using the AMSR-2 satellite data, including discrimination function and seasonal threshold. The 6.925 GHz horizontal polarization Quasi-emissivity with high sensitivity to near-surface freeze/thaw cycle should replace the relative Frost Factor (FFrel). To minimize the effect of small-scale threshold selection, Min-Max normalization can be replaced with a new normalization method, namely, standard deviation normalization method. The parameterization of the discrimination function algorithm to the TP area is proposed to improve algorithm accuracy using four soil moisture and temperature dense observation network data.Results reveal that the classification accuracy of the discriminant function algorithm exhibits the most advantage during ascend period. In addition, this algorithm reduces misclassification points due to complex changes of surface emissivity during summer. The seasonal threshold algorithm based on the standard deviation normalization method shows optimum performance during descend period. Moreover, the amplitude of surface emissivity (initial liquid water content) exhibits an important influence on the algorithm accuracy.

ключеви́че слова́

remote sensing;Tibetan Plateau;soil freeze/thaw;microwave remote sensing;AMSR-2;discriminant function algorithm;seasonal threshold algorithm

References

  1. 1.
    Chai L N, Zhang L X, Zhang Y Y, Hao Z G, Jiang L M and Zhao S J. 2014. Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery. International Journal of Remote Sensing, 35(22): 7631-7649
  2. 2.
    Chen Y Y, Yang K, Qin J, Cui Q, Lu H, La Z, Han M L and Tang W J. 2017. Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau. Journal of Geophysical Research, 122(11): 5780-5792
  3. 3.
    Derksen C, Xu X L, Dunbar R S, Colliander A, Kim Y, Kimball J S, Black T A, Euskirchen E, Langlois A, Loranty M M, Marsh P, Rautiainen K, Roy A, Royer A and Stephens J. 2017. Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements. Remote Sensing of Environment, 194: 48-62
  4. 4.
    England A W. 1974. The effect upon microwave emissivity of volume scattering in snow, in ice, and in frozen soil//Proceedings of URSI Conference on Emission and Scattering from the Earth. Berne, Switzerland: [s.n.]: 273-287
  5. 5.
    Entekhabi D, Njoku E, O'Neill P, Spencer M, Jackson T, Entin J, Im E and Kellogg K. 2008. The Soil Moisture Active/Passive Mission (SMAP)//Proceedings of 20008 IEEE International Geoscience and Remote Sensing Symposium. Boston, MA, USA: IEEE
  6. 6.
    Han M L, Yang K, Qin J, Jin R, Ma Y M, Wen J, Chen Y Y, Zhao L, Lazhu and Tang W J. 2015. An algorithm based on the standard deviation of passive microwave brightness temperatures for monitoring soil surface freeze/thaw state on the Tibetan plateau. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2775-2783
  7. 7.
    Hu T X, Zhao T J, Shi J C, Wu S L, Liu D, Qin H M and Zhao K G. 2017. High-resolution mapping of freeze/thaw status in china via fusion of MODIS and AMSR2 data. Remote Sensing, 9(12): 1339
  8. 8.
    Hu T X, Zhao T J, Zhao K G and Shi J C. 2019. A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data. International Journal of Remote Sensing, 40(18): 6993-7016
  9. 9.
    Hu W X, Chai L N, Zhao S J and Zhao T J. 2017. Improvementon soil freeze/thaw discriminant algorithm under complex surface conditions in cold regions. Remote Sensing Technology and Application, 32(3): 395-405
  10. 10.
    Imaoka K, Kachi M, Fujii H, Murakami H, Hori M, Ono A, Igarashi T, Nakagawa K, Oki T, Honda Y and Shimoda H. 2010. Global Change Observation Mission (GCOM) for monitoring carbon, water cycles, and climate change. Proceedings of the IEEE, 98(5): 717-734
  11. 11.
    Jin R, Li X and Che T. 2009. A decision tree algorithm for surface soil freeze/thaw classification over China using SSM/I brightness temperature. Remote Sensing of Environment, 113(12): 2651-2660
  12. 12.
    Judge J, Galantowicz J F, England A W and Dahl P. 1996. Freeze/thaw classification for prairie soils using SSM/I radiobrightnesses. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 827-832
  13. 13.
    Kerr Y H, Waldteufel P, Wigneron J P, Martinuzzi J, Font J and Berger M. 2001. Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. IEEE transactions on Geoscience and remote sensing, 39(8): 1729-1735
  14. 14.
    Kim Y, Kimball J S, Glassy J and Du J Y. 2017. An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing. Earth System Science Data, 9(1): 133-147
  15. 15.
    Kou X K, Jiang L M, Yan S, Zhao T J, Lu H and Cui H Z. 2017. Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data. Remote Sensing of Environment, 199: 291-301
  16. 16.
    Mao K B, Shi J C, Li Z L, Qin Z H and Jia Y Y. 2005. The land surface temperature and emissivity retrieved from the AMSR passive microwave data. Remote Sensing For Land and Resources, 17(3): 14-17
  17. 17.
    Mika S, Ratsch G, Weston J, Scholkopf B and Mullers K R1999. Fisher discriminant analysis with kernels//Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing IX. Madison, WI, USA: IEEE: 41-48
  18. 18.
    Prince M, Roy A, Brucker L, Royer A, Kim Y and Zhao T J. 2018. Northern Hemisphere surface freeze–thaw product from Aquarius L-band radiometers. Earth System Science Data, 10(4): 2055-2067
  19. 19.
    Shi J C, Du Y, Du J Y, Jiang L M, Chai L N, Mao K B, Xu P, Ni W J, Xiong C, Liu Q, Liu C Z, Guo P, Cui Q, Li Y Q, Chen J, Wang A Q, Luo H J and Wang Y H. 2012. Progresses on microwave remote sensing of land surface parameters. Science China Earth Sciences, 55(7): 1052-1078
  20. 20.
    Su Z, Wen J, Dente L, van der Velde R, Wang L, Ma Y, Yang K and Hu Z. 2011. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrology and Earth System Sciences, 15(7): 2303-2316
  21. 21.
    Tang Y, Zhang W J, Liu L and Li G C. 2019. Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau. International Journal of Remote Sensing, 40(17): 6542-6552
  22. 22.
    Wang J, Jiang L M, Kou X K, Cui H Z and Yang J W. 2019. Downscaling method for near-surface freeze/thaw state monitoring in Genhe area of China. Journal of Remote Sensing, 23(6): 1209-1222
  23. 23.
    Wang P K, Zhao T J, Shi J C, Hu T X, Roy A, Qiu Y B and Lu H. 2019. Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data. International Journal of Digital Earth, 12: 980-994
  24. 24.
    Yang K, Qin J, Zhao L, Chen Y Y, Tang W J, Han M L, Lazhu, Chen Z Q, Lv N, Ding B H, Wu H and Lin C G. 2013. A multiscale soil moisture and freeze–thaw monitoring network on the third pole. Bulletin of the American Meteorological Society, 94(12): 1907-1916
  25. 25.
    Ye Q Y, Chai L N, Jiang L M and Zhao T J. 2014. A disaggregation approach for soil phase transition water content using AMSR2 and MODIS products. Journal of Remote Sensing, 18(6): 1147-1157
  26. 26.
    Zhang L X, Jiang L M, Chai L N, Zhao S J, Zhao T J and Li X X. 2011. Research advances in passive microwave remote sensing of freeze-thaw processes over complex landscapes. Advances in Earth Sciences, 26(10): 1023-1029
  27. 27.
    Zhang L X, Zhao T J, Jiang L M and Zhao S J. 2010. Estimate of phase transition water content in freeze–thaw process using microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing, 48(12): 4248-4255
  28. 28.
    Zhao T J, Shi J C, Hu T X, Zhao L, Zou D F, Wang T X, Ji D B, Li R and Wang P K. 2017. Estimation of high-resolution near-surface freeze/thaw state by the integration of microwave and thermal infrared remote sensing data on the Tibetan Plateau. Earth and Space Science, 4(8): 472-484
  29. 29.
    Zhao T J, Zhang L X, Jiang L M, Zhao S J, Chai L N and Jin R. 2011. A new soil freeze/thaw discriminant algorithm using AMSR-E passive microwave imagery. Hydrological Processes, 25(11): 1704-1716
  30. 30.
    Zuemdorfer B, England A W and Ulaby F T. 1990. An optimized approach to mapping freezing terrain with SMMR data//Proceedings of the 10th Annual International Symposium on Geoscience and Remote Sensing. College Park, Maryland, USA: IEEE

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