Skip to main content

Retrieval and analysis of land surface temperature in permafrost regions in Northeast China based on AMSR2 data

Abstract

The land surface temperature (LST) in permafrost regions in the Northeast permafrost on March 22 (spring), June 24 (summer), September 21 (autumn), and December 24 (winter) in 2019 were retrieved based on the AMSR2 brightness temperature data. An in-depth analysis of the temperature retrieval accuracy between different types of frozen ground, vegetation cover, and during the four seasons of the day or night was conducted. The results show that: (1) The retrieval accuracy of the four seasons lowers in the seasonal order of summer > autumn > spring > winter, and the accuracy of data at the night was better than that of the day; (2) The retrieval accuracy of different vegetation cover types lowers in the order of grassland > agricultural land > forest land, and; (3) The retrieval accuracy of different frozen ground types lowers in the order of the zone of seasonal frost > zone of isolated patches of thawing permafrost > zone of island permafrost zone > continuous permafrost zone.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Agam N, Kustas WP, Anderson MC, Li F, Neale CMU (2007) A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens Environ 107:545–558. https://doi.org/10.1016/j.rse.2006.10.006

    Article  Google Scholar 

  2. Aires F, Prigent C, Rossow WB, Rothstein M (2001) A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. J Geophys Res Atmos 106:14887–14907. https://doi.org/10.1029/2001JD900085

    Article  Google Scholar 

  3. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23:1–26. https://doi.org/10.1002/joc.859

    Article  Google Scholar 

  4. Bento V, Carlos DC, Isabel T, João M, Anke DT (2017) Improving land surface temperature retrievals over mountainous regions. Remote Sens 9:38. https://doi.org/10.3390/rs9010038

    Article  Google Scholar 

  5. Biskaborn BK, Smith SL, Noetzli J, Matthes H, Vieira G, Streletskiy DA, Schoeneich P, Romanovsky VE, Lewkowicz AG, Abramov A, Allard M, Boike J, Cable WL, Christiansen HH, Delaloye R, Diekmann B, Drozdov D, Etzelmüller B, Grosse G, Guglielmin M, Ingeman-Nielsen T, Isaksen K, Ishikawa M, Johansson M, Johannsson H, Joo A, Kaverin D, Kholodov A, Konstantinov P, Kröger T, Lambiel C, Lanckman JP, Luo D, Malkova G, Meiklejohn I, Moskalenko N, Oliva M, Phillips M, Ramos M, Sannel ABK, Sergeev D, Seybold C, Skryabin P, Vasiliev A, Wu Q, Yoshikawa K, Zheleznyak M, Lantuit H (2019) Permafrost is warming at a global scale. Nat Commun 10:264. https://doi.org/10.1038/s41467-018-08240-4

    Article  Google Scholar 

  6. Dai FN (2016) Retrieval and validation of land surface temperature from AMSR2 data. Univ Electron Sci Technol China. https://doi.org/10.7666/d.D00988281

  7. Ding Y, Zhang S, Zhao L, Li Z, Kang S (2019) Global warming weakening the inherent stability of glaciers and permafrost. Sci Bull 64:245–253. https://doi.org/10.1016/j.scib.2018.12.028

    Article  Google Scholar 

  8. Dominguez A, Kleissl J, Luvall JC, Rickman DL (2011) High-resolution urban thermal sharpener (HUTS). Remote Sens Environ 115:1772–1780. https://doi.org/10.1016/j.rse.2011.03.008

    Article  Google Scholar 

  9. Duan SB, Li ZL, Leng P, Han XJ, Chen Y (2015) Generation of an all-weather land surface temperature product from MODIS and AMSR-E data. International Conference on Intelligent Earth Observing and Applications International Society for Optics and Photonics 10.1117/12.2207848

  10. Ermida SL, Jiménez C, Prigent C, Trigo FI, DaCamara CC (2017) Inversion of AMSR-E observations for land surface temperature estimation - part 2: global comparison with infrared satellite temperature. Journal of geophysical research, D. Atmospheres 122:3348–3360. https://doi.org/10.1002/2016JD026148

    Article  Google Scholar 

  11. Gerace A, Kleynhans T, Eon R, Montanaro M (2020) Towards an operational, split window-derived surface temperature product for the thermal infrared sensors onboard Landsat 8 and 9. Remote Sens 12:224. https://doi.org/10.3390/rs12020224

    Article  Google Scholar 

  12. Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48:RG4004. https://doi.org/10.1029/2010RG000345

    Article  Google Scholar 

  13. Hollinger JP, Peirce JL, Poe GA (1991) SSM/I instrument evaluation. IEEE Trans Geosci Remote Sens 28:781–790. https://doi.org/10.1109/36.58964

    Article  Google Scholar 

  14. Huo WJ, Han Z (2013) Retrieval of sea surface temperature from AMSR-E and MODIS in the northern Indian ocean. J Shanghai Ocean Univ 22:439–445

    Google Scholar 

  15. Jiang LM, Cui HZ, Wang GX et al (2020) Progress on remote sensing of snow, surface soil frozen/thaw state and soil moisture. Remote Sens Technol Appl 35:1237–1262. https://doi.org/10.11873/j.issn.1004-0323.2020.6.123

    Article  Google Scholar 

  16. Jiménez C, Prigent C, Ermida SL, Moncet JL (2017) Inversion of AMSR-E observations for land surface temperature estimation - part 1: methodology and evaluation with station temperature: AMSR-E land surface temperature. J Geophys Res Atmos 122:3330–3347. https://doi.org/10.1002/2016JD026144

    Article  Google Scholar 

  17. Jin HJ, Yu SP, Lu LZ, Guo DX, Li YW (2006) Degradation of permafrost in the Da and Xiao Hinggan Mountains, Northeast China, and preliminary assessment of its trend. J Glaciol Geocryol 28:467–476. https://doi.org/10.3969/j.issn.1000-0240.2006.04.002

    Article  Google Scholar 

  18. Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Clim 23:618–633. https://doi.org/10.1175/2009jcli2900.1

    Article  Google Scholar 

  19. Kawanishi T, Sezai T, Ito Y, Imaoka K, Takeshima T, Ishido Y, Shibata A, Miura M, Inahata H, Spencer RW (2003) The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA’S contribution to the EOS for global energy and water cycle studies. IEEE Trans Geosci Remote Sens 41:184–194. https://doi.org/10.1109/TGRS.2002.808331

    Article  Google Scholar 

  20. Kurylyk BL, MacQuarrie KTB, McKenzie JM (2014) Climate change impacts on groundwater and soil temperatures in cold and temperate regions: implications, mathematical theory, and emerging simulation tools. Earth-Sci Rev 138:313–334. https://doi.org/10.1016/j.earscirev.2014.06.006

    Article  Google Scholar 

  21. Kustas WP, Norman JM, Anderson MC, French AN (2003) Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sens Environ 85:429–440. https://doi.org/10.1016/S0034-4257(03)00036-1

    Article  Google Scholar 

  22. Li B, Wang HM, Qin MZ, Zhang PY (2017) Comparative study on the correlations between NDVI, NDMI and LST. Prog Geography 36:585–596. https://doi.org/10.18306/dlkxjz.2017.05.006

    Article  Google Scholar 

  23. Li L, Shi J, Du J (2009) Land surface temperature retrieval from MODIS and AMSR-E on the Tibet Plateau. 2009 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2009.5417851

  24. Li ZL, Tang BH, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA (2013) Satellite-derived land surface temperature: current status and perspectives. Remote Sens Environ 131:14–37. https://doi.org/10.1016/j.rse.2012.12.008

    Article  Google Scholar 

  25. Liang SL, Bai R, Chen XN et al (2020) Review of China’s land surface quantitative remote sensing development in 2019. J Remote Sens (in Chinese) 24:618–671. https://doi.org/10.11834/jrs.20209476

    Article  Google Scholar 

  26. Liu SB, Zang SY, Zhang LJ, Na XD (2017) Estimation of land surface temperature from MODIS in Northeast China. Geograph Res 36:2251–2260. https://doi.org/10.11821/dlyj201711017

    Article  Google Scholar 

  27. Liua ZL, Wua H, Qiuc S, Jiad YY, Lia ZL (2010) Determination of land surface temperature from AMSR-E data for bare surfaces. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010, July 25–30, 2010, Honolulu, Hawaii, USA, proceedings. IEEE. https://doi.org/10.1109/IGARSS.2010.5654439

  28. Luan HJ, Tian QJ, Zhang XX, Nie Q, Zhu XL (2018) Trends on scaling research for land surface parameters in quantitative remote sensing. Adv Earth Sci 33:483–492. https://doi.org/10.11867/j.issn.1001-8166.2018.05.0483

  29. Mao KB, Shi JC, Li ZL, Qin ZH, Jia YY (2005) The land surface temperature and emissivity retrieval from the AMSR passive microwave data. Remote Sens Land Resour 17:14–17. https://doi.org/10.3969/j.issn.1001-070X.2005.03.004

    Article  Google Scholar 

  30. Mao KB, Shi JC, Li ZL, Qin ZH, Li MC, Xu B (2006b) A physical statistical algorithm for inversion of ground surface temperature based on passive microwave AMSR-E data. Science in China, Series D: Earth Sciences 36:1170–1176. https://doi.org/10.3321/j.issn:1006-9267.2006.12.012

  31. Mao KB, Shi JC, Qin ZH, Gong P, Xu B, Jiang LM (2006a) A four-channel algorithm for retrieval land surface temperature and emissivity from ASTER data. J Remote Sens 10:593–599. https://doi.org/10.3321/j.issn:1007-4619.2006.04.023

    Article  Google Scholar 

  32. Mcfarland MJ, Miller RL, Neale CMU (1990) Land surface temperature derived from the SSM/I passive microwave brightness temperatures. IEEE Trans Geosci Remote Sens 28:839–845. https://doi.org/10.1109/36.58971

    Article  Google Scholar 

  33. Nichol J (2009) An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis. Photogram Eng Remote Sens 75:547–556. https://doi.org/10.14358/pers.75.5.547

    Article  Google Scholar 

  34. Pu R, Gong P, Michishita R, Sasagawa T (2006) Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sens Environ 104:211–225. https://doi.org/10.1016/j.rse.2005.09.022

    Article  Google Scholar 

  35. Sekertekin A, Bonafoni S (2020) Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens 12:294. https://doi.org/10.3390/rs12020294

    Article  Google Scholar 

  36. Shi J, Du Y, Du J et al (2012) Progresses on microwave remote sensing of land surface parameters. Sci China Ser D Earth Sci 42:814–842. https://doi.org/10.1007/s11430-012-4444-x

    Article  Google Scholar 

  37. Shwetha HR, Kumar DN (2016) Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN. ISPRS J Photogramm Remote Sens 117:40–55. https://doi.org/10.1016/j.isprsjprs.2016.03.011

    Article  Google Scholar 

  38. Wang L, Lu Y, Yao Y (2019) Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors 19:5049. https://doi.org/10.3390/s19225049

    Article  Google Scholar 

  39. Wang MY, Lu DR (2005) Diurnal and seasonal variation of clear-sky land surface temperature of several representative land surface types in China retrieved by GMS 5. Acta Meteorologica Sinica 63:957–968. https://doi.org/10.3321/j.issn:0577-6619.2005.06.012

    Article  Google Scholar 

  40. Wei Z, Jin H, Zhang J, Yu SP, Han XJ, Ji YJ, He RX, Chang XL (2011) Prediction of permafrost changes in Northeast China under a changing climate. Sci China: Earth Sci 41:74–84. https://doi.org/10.1007/s11430-010-4109-6

    Article  Google Scholar 

  41. Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483. https://doi.org/10.1016/j.rse.2003.11.005

    Article  Google Scholar 

  42. Wu M, Niu Z, Wang C, Wu C, Wang L (2012) Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. J Appl Remote Sens 6:063507-1–063507-13. https://doi.org/10.1117/1.JRS.6.063507

    Article  Google Scholar 

  43. Yang JX, Su H, Wang YP (2010b) DisTrad model for thermal sub-pixel mapping in high vegetation area. Remote Sens Technol Appl 25:346–352. https://doi.org/10.11873/j.issn.1004-0323.2010.3.346

    Article  Google Scholar 

  44. Yang M, Nelson FE, Shiklomanov NI, Guo D, Wan G (2010a) Permafrost degradation and its environmental effects on the Tibetan plateau: a review of recent research. Earth Sci Rev 103:31–44. https://doi.org/10.1016/j.earscirev.2010.07.002

    Article  Google Scholar 

  45. Yang Y, Cao C, Pan X, Li X, Zhu X (2017) Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sens 9:789. https://doi.org/10.3390/rs9080789

    Article  Google Scholar 

  46. Yin GA, Zheng H, Niu FJ, Luo J, Lin ZJ, Liu MH (2018) Numerical mapping and modeling permafrost thermal dynamics across the Qinghai-Tibet engineering corridor, China integrated with remote sensing. Remote Sens 10:2069–2069. https://doi.org/10.3390/rs10122069

    Article  Google Scholar 

  47. Zhang LJ, Wen XP, Wang J, Zhou Y (2014a) Research on diurnal surface temperature retrieval based on MODIS images. Acta Agriculturae Jiangxi 26:121–124

    Google Scholar 

  48. Zhang X, Susan Moran M, Zhao X, Liu S, Zhou T, Ponce-Campos GE, Liu F (2014b) Impact of prolonged drought on rainfall use efficiency using MODIS data across China in the early 21st century. Remote Sens Environ 150:188–197. https://doi.org/10.1016/j.rse.2014.05.003

    Article  Google Scholar 

  49. Zhang Z, Wu Q, Xun X, Li Y (2019) Spatial distribution and changes of Xing’an permafrost in China over the past three decades. Quat Int 523:16–24. https://doi.org/10.1016/j.quaint.2019.06.007

    Article  Google Scholar 

  50. Zheng G, Yang Y, Yang D, Dafflon B, Yi Y, Zhang S, Chen D, Gao B, Wang T, Shi R, Wu Q (2020) Remote sensing spatiotemporal patterns of frozen soil and the environmental controls over the Tibetan plateau during 2002-2016. Remote Sens Environ 247:111927. https://doi.org/10.1016/j.rse.2020.111927

    Article  Google Scholar 

  51. Zheng X, Li X, Jiang T, Ding Y, Wu L, Zhang S, Zhao K (2016) Retrieving soil surface temperature under snowpack using special sensor microwave/imager brightness temperature in forested areas of Heilongjiang, China: an improved method. J Appl Remote Sens 10:026016. https://doi.org/10.1117/1.jrs.10.026016

    Article  Google Scholar 

  52. Zhou J, Dai F, Zhang X, Zhao S, Li M (2015) Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass. Intl J Appl Earth Observ Geoinform 34:35–50. https://doi.org/10.1016/j.jag.2014.07.001

    Article  Google Scholar 

  53. Zhou J, Liu S, Li M, Zhan W, Xu Z, Xu T (2016) Quantification of the scale effect in downscaling remotely sensed land surface temperature. Remote Sens 8:975. https://doi.org/10.3390/rs8120975

    Article  Google Scholar 

  54. Zhou YW, Wang YX, Gao YW, Yue HS (1996) Ground temperature, permafrost distribution and cIimate warming in northeastern China s1:139-147

  55. Zou D, Zhao L, Sheng Y, Chen J, Hu G, Wu T, Wu J, Xie C, Wu X, Pang Q, Wang W, du E, Li W, Liu G, Li J, Qin Y, Qiao Y, Wang Z, Shi J, Cheng G (2017) A new map of permafrost distribution on the Tibetan plateau. Cryosphere 11:2527–2542. https://doi.org/10.5194/tc-11-2527-2017

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the MODIS teams for their work. The AMSR2 L3 TB data used for this study were provided courtesy of JAXA. This research was financially funded by the National Natural Science Foundation of China (NSFC) (Grant Nos. 41901072 and 41971151), and Joint Key Program of the NSFC and Heilongjiang Province of China (Grant No. U20A2082).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Miao Li.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by: H. Babaie

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yin, H., Li, M., Man, H. et al. Retrieval and analysis of land surface temperature in permafrost regions in Northeast China based on AMSR2 data. Earth Sci Inform 14, 1245–1260 (2021). https://doi.org/10.1007/s12145-021-00666-7

Download citation

Keywords

  • Land surface temperature
  • AMSR2
  • Downscaling
  • Frozen ground
  • Northeast China