Skip to main content

Advertisement

SpringerLink
Log in

Preliminary verification of instantaneous air temperature estimation for clear sky conditions based on SEBAL

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

Spatially distributed near surface air temperature at the height of 2 m is an important input parameter for the land surface models. It is of great significance in both theoretical research and practical applications to retrieve instantaneous air temperature data from remote sensing observations. An approach based on Surface Energy Balance Algorithm for Land (SEBAL) to retrieve air temperature under clear sky conditions is presented. Taking the meteorological measurement data at one station as the reference and remotely sensed data as the model input, the research estimates the air temperature by using an iterative computation. The method was applied to the area of Jiangsu province for nine scenes by using MODIS data products, as well as part of Fujian province, China based on four scenes of Landsat 8 imagery. Comparing the air temperature estimated from the proposed method with that of the meteorological station measurement, results show that the root mean square error is 1.7 and 2.6 °C at 1000 and 30 m spatial resolution respectively. Sensitivity analysis of influencing factors reveals that land surface temperature is the most sensitive to the estimation precision. Research results indicate that the method has great potentiality to be used to estimate instantaneous air temperature distribution under clear sky conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements. Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome

  • Bastiaanssen WGM (2000) SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J Hydrol 229:87–100. doi:10.1016/S0022-1694(99)00202-4

    Article  Google Scholar 

  • Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, New York

    Google Scholar 

  • Cheng KE, Su YF, Kuo FT, Hung WC, Chiang JL (2008) Assessing the effect of landcover changes on air temperature using remote sensing images: a pilot study in northern Taiwan. Landsc Urban Plan 85(2):85–96. doi:10.1016/j.landurbplan.2007.09.014

    Article  Google Scholar 

  • Cresswell MP, Morse AP, Thomson MC, Connor SJ (1999) Estimating surface air temperatures, from Meteosat land surface temperatures, using an empirical solar zenith angle model. Int J Remote Sens 20:1125–1132. doi:10.1080/014311699212885

    Article  Google Scholar 

  • Florio EN, Lele SR, Chang YC, Sterner R, Glass GE (2004) Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: a statistical approach. Int J Remote Sens 25(15):2979–2994. doi:10.1080/01431160310001624593

    Article  Google Scholar 

  • Green RM, Hay SI (2002) The potential of pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications. Remote Sens Environ 79:166–175. doi:10.1016/S0034-4257(01)00270-X

    Article  Google Scholar 

  • He HL, Yu GR, Niu D (2003) Method of global solar radiation calculation on complex territories. Resour Sci 25(1):78–85 (In Chinese with English abstract)

    Google Scholar 

  • Ishida T, Kawashima S (1993) Use of cokriging to estimate surface air temperature from elevation. Theor Appl Climatol 47:147–157. doi:10.1007/BF00867447

    Article  Google Scholar 

  • Jang JD, Viau AA, Anctil F (2004) Neural network estimation of air temperatures from AVHRR data. Int J Remote Sens 25:4541–4554. doi:10.1080/01431160310001657533

    Article  Google Scholar 

  • Jiménez-muñoz JC, Sobrino JA, Skokovic D, Mattar C, Cristóbal J (2014) Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor Data. IEEE Geosci Remote Sens 11(10):1840–1843. doi:10.1109/LGRS.2014.2312032

    Article  Google Scholar 

  • Kawashima S, Ishida T, Minomura M, Miwa T (2000) Relations between surface temperature and air temperature on a local scale during winter nights. J Appl Meteorol 39:1570–1579. doi:10.1175/1520-0450(2000)039<1570:RBSTAA>2.0.CO;2

    Article  Google Scholar 

  • Klemen Z, Marion SH (2009) Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS J Photogramm 64(4):414–421. doi:10.1016/j.isprsjprs.2009.02.006

    Article  Google Scholar 

  • Kreith F, Kreider JF (1978) Principles of solar engineering. McGraw-Hill, New York

    Google Scholar 

  • Lakshmi V, Czajkowski K, Dubayah R, Susskind J (2001) Land surface air temperature mapping using TOVS and AVHRR. Int J Remote Sens 22(4):643–662. doi:10.1080/01431160050505900

    Article  Google Scholar 

  • Liang SL (2000) Narrowband to broadband conversions of land surface albedo I algorithms. Remote Sens Environ 76:213–238. doi:10.1016/S0034-4257(00)00205-4

    Article  Google Scholar 

  • Mao KB, Tang HJ, Wang XF, Zhou QB, Wang DL (2008) Near-surface air temperature estimation from ASTER data based on neural network algorithm. Int J Remote Sens 29(20):6021–6028. doi:10.1080/01431160802192160

    Article  Google Scholar 

  • Monteith JL (1973) Principles of environmental physics. Whitstable Litho Ltd., Whitstable, Kent, Great Britain

    Google Scholar 

  • Nichol JE, Wong MS (2008) Spatial variability of air temperature and appropriate resolution for satellite-derived air temperature estimation. Int J Remote Sens 29:7213–7223. doi:10.1080/01431160802192178

    Article  Google Scholar 

  • Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9:857–861. doi:10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2

    Article  Google Scholar 

  • Rozenstein O, Qin ZH, Derimian Y, Karnieli A (2014) Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors 14:5768–5780. doi:10.3390/s140405768

    Article  Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martínez P (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE T Geosci Remote 46:316–327. doi:10.1109/TGRS.2007.904834

    Article  Google Scholar 

  • Stisen S, Sandholt I, Nørgaard A, Fensholt R, Eklundh L (2007) Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sens Environ 110:262–274. doi:10.1016/j.rse.2007.02.025

    Article  Google Scholar 

  • Sun YJ, Wang JF, Zhang RH, Gillies RR, Xue Y, Bo YC (2005) Air temperature retrieval from remote sensing data based on thermodynamics. Theor Appl Climatol 80:37–48. doi:10.1007/s00704-004-0079-y

    Article  Google Scholar 

  • Wang F, Qin ZH, Song CY, Tu LL, Karnieli A, Zhao SH (2015) An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens 7:4268–4289. doi:10.3390/rs70404268

    Article  Google Scholar 

  • Waters R, Allen R, Tasumi M, Trezza R, Bastiaanssen W (2002) SEBAL advanced training and users manual. Water Consulting, University of Idaho, Water Watch Inc., Version 1.0

  • Xu YM, Qin ZH, Shen Y (2012) Study on the estimation of near-surface air temperature from MODIS data by statistical methods. Int J Remote Sens 33(24):7629–7643. doi:10.1080/01431161.2012.701351

    Article  Google Scholar 

  • Zaksek K, Schroedter-Homscheidt M (2009) Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS J Photogramm 64:414–421. doi:10.1016/j.isprsjprs.2009.02.006

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by Major Project of China High-resolution Earth Observation System (CHEOS, No. 32-Y30B08-9001-13/15), the Natural Science Foundation of China (No. 41571418 and 41401471), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing, 210044, Jiangsu, China

    Shanyou Zhu, Chuxuan Zhou, Guixin Zhang & Junwei Hua

  2. School of Marine Science, Nanjing University of Information Science & Technology, Nanjing, 210044, Jiangsu, China

    Hailong Zhang

Authors
  1. Shanyou Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuxuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guixin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hailong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junwei Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shanyou Zhu.

Additional information

Responsible Editor: S. Hong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, S., Zhou, C., Zhang, G. et al. Preliminary verification of instantaneous air temperature estimation for clear sky conditions based on SEBAL. Meteorol Atmos Phys 129, 71–81 (2017). https://doi.org/10.1007/s00703-016-0451-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00703-016-0451-3

Keywords

Search

Navigation