Observed differences between near-surface air and skin temperatures using satellite and ground-based data

  • Satya PrakashEmail author
  • Farjana Shati
  • Hamid Norouzi
  • Reginald Blake
Original Paper


Accurate estimates of long-term land surface temperature (Ts) and near-surface air temperature (Ta) at finer spatio-temporal resolutions are crucial for surface energy budget studies, for environmental applications, for land surface model data assimilation, and for climate change assessment and its associated impacts. The Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua satellite provide a unique opportunity to estimate both temperatures twice daily at the global scale. In this study, differences between Ta and Ts were assessed locally over regions of North America from 2009 to 2013 using ground-based observations covering a wide range of geographical, topographical, and land cover types. The differences between Ta and Ts during non-precipitating conditions are generally 2–3 times larger than precipitating conditions. However, these differences show noticeable diurnal and seasonal variations. The differences between Ta and Ts were also investigated at the global scale using the AIRS estimates under clear-sky conditions for the period 2003–2015. The tropical regions showed about 5–20 °C warmer Ts than Ta during the day-time, whereas opposite characteristics (about 2–5 °C cooler Ts than Ta) are found over most parts of the globe during the night-time. Additionally, Ts estimates from the AIRS and the MODIS sensors were inter-compared. Although large-scale features of Ts were essentially similar for both sensors, considerable differences in magnitudes were observed (> 6 °C over mountainous regions). Finally, Ta and Ts estimates from the AIRS and MODIS sensors were validated against ground-based observations for the period of 2009–2013. The error characteristics notably varied with ground stations and no clear evidence of their dependency on land cover types or elevation was detected. However, the MODIS-derived Ts estimates generally showed larger biases and higher errors compared to the AIRS-derived estimates. The biases and errors increased steadily when the spatial resolution of the MODIS estimates changed from finer to coarser. These results suggest that representativeness error should be properly accounted for when validating satellite-based temperature estimates with point observations.



The authors would like to thank the editor and anonymous reviewers for their constructive comments. The statements contained within the manuscript are not the opinions of the funding agency or the US government; they reflect the authors’ opinions only. MODIS/Aqua land surface temperature data obtained from NASA EOSDID LP DAAC (, Aqua/AIRS data products obtained from the GES DISC (, USCRN observations obtained from the NOAA National Centers for Environmental Information (, and MODIS-based 0.5 km global land cover climatology obtained from the USGS Land Cover Institute ( are thankfully acknowledged.

Funding information

This study was supported by the National Science Foundation (NSF) Research Experiences for Undergraduates (REU) under Grant 1560050 and by the Center for Remote Sensing and Earth System Sciences at the New York City College of Technology. This study was also partially supported by the Department of Defense Army Research Office under Grant W911NF–15–1–0070 and by NASA under Grant NNH15ZDA001N.


  1. Ayanlade A (2016) Seasonality in the daytime and night-time intensity of land surface temperature in a tropical city area. Sci Total Environ 557–558:415–424. CrossRefGoogle Scholar
  2. Bell JE, Palecki MA, Baker CB, Collins WG, Lawrimore JH, Leeper RD, Hall ME, Kochendorfer J, Meyers TP, Wilson T, Diamond HJ (2013) U.S. Climate Reference Network soil moisture and temperature observations. J Hydrometeorol 14:977–988. CrossRefGoogle Scholar
  3. Bechtel B (2015) A new global climatology of annual land surface temperature. Remote Sens 7(3):2850–2870. CrossRefGoogle Scholar
  4. Blackwell WJ (2012) Neural network Jacobian analysis for high-resolution profiling of the atmosphere. EURASIP J Adv Signal Process 71:1–11. Google Scholar
  5. Boylan P, Wang J, Cohn SA, Fetzer E, Maddy ES, Wong S (2015) Validation of AIRS version 6 temperature profiles and surface-based inversions over Antarctica using Concordiasi dropsonde data. J Geophys Res - Atmos 120:992–1007. CrossRefGoogle Scholar
  6. Broxton PD, Zeng X, Sulla-Menashe D, Troch PA (2014) A global land cover climatology using MODIS data. J Appl Meteorol Climatol 53:1593–1605. CrossRefGoogle Scholar
  7. Cheval S, Dumitrescu A (2017) Rapid daily and sub-daily temperature variations in an urban environment. Clim Res 73:233–246. CrossRefGoogle Scholar
  8. Diamond HJ, Karl TR, Palecki MA, Baker CB, Bell JE, Leeper RD, Easterling DR, Lawrimore JH, Meyers TP, Helfert MR, Goodge G, Thorne PW (2013) U.S. Climate Reference Network after one decade of operations: status and assessment. Bull Amer Meteorol Soc 94:489–498. CrossRefGoogle Scholar
  9. Didari S, Norouzi H, Zand-Parsa S, Khanbilvardi R (2017) Estimation of daily minimum land surface air temperature using MODIS data in southern Iran. Theor Appl Climatol 130:1149–1161. CrossRefGoogle Scholar
  10. Fabrizi R, Bonafoni S, Biondi R (2010) Satellite and ground-based sensors for the urban heat island analysis in the city of Rome. Remote Sens 2(5):1400–1415. CrossRefGoogle Scholar
  11. Gallo K, Hale R, Tarpley D, Yu Y (2011) Evaluation of the relationship between air and land surface temperature under clear- and cloudy-sky conditions. J Appl Meteorol Climatol 50:767–775. CrossRefGoogle Scholar
  12. Good EJ (2016) An in situ-based analysis of the relationship between land surface “skin” and screen-level air temperatures. J Geophys Res - Atmos 121:8801–8819. CrossRefGoogle Scholar
  13. Houser PR, De Lannoy GJ, Walker JP (2010) Land surface data assimilation. In: Lahoz W, Khattatov B, Menard R (eds) Data assimilation. Springer, Berlin, Heidelberg, pp 549–597. CrossRefGoogle Scholar
  14. Jang K, Kang S, Kimball JS, Hong SY (2014) Retrievals of all-weather daily air temperature using MODIS and AMSR-E data. Remote Sens 6(9):8387–8404. CrossRefGoogle Scholar
  15. Kang H-J, Yoo J-M, Jeong M-J, Won Y-I (2015) Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only. Atmos Meas Tech 8:4025–4041. CrossRefGoogle Scholar
  16. Lee Y-R, Yoo J-M, Jeong M-J, Won Y-I, Hearty T, Shin D-B (2013) Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures. Atmos Meas Tech 6:445–455. CrossRefGoogle Scholar
  17. Liu W, Chen S, Jiang H, Wang C, Li D (2017) Spatiotemporal analysis of MODIS land surface temperature with in situ meteorological observations and ERA-interim reanalysis: the option of model calibration. IEEE J Selected Topics Appl Earth Obs Remote Sens 10:1357–1371. CrossRefGoogle Scholar
  18. Mazdiyasni O, AghaKouchak A (2015) Substantial increase in concurrent droughts and heatwaves in the United States. Proc Nat Acad Sci 112:11484–11489. CrossRefGoogle Scholar
  19. Moncet J-L, Liang P, Lipton AE, Galantowicz JF, Prigent C (2011) Discrepancies between MODIS and ISCCP land surface temperature products analyzed with microwave measurements. J Geophys Res 116:D21105. CrossRefGoogle Scholar
  20. Noi PT, Kappas M, Degener J (2016) Estimating daily maximum and minimum land air surface temperature using MODIS land surface temperature data and ground truth data in northern Vietnam. Remote Sens 8(12):1002. CrossRefGoogle Scholar
  21. Norouzi H, Temimi M, Rossow W, Pearl C, Azarderakhsh M, Khanbilvardi R (2011) The sensitivity of land surface emissivity estimates from AMSR-E at C and X bands to surface properties. Hydrol Earth Syst Sci 15:3577–3589. CrossRefGoogle Scholar
  22. Norouzi H, Temimi M, AghaKouchak A, Azarderakhsh M, Khanbilvardi R, Shields G, Tesfagiorgis K (2015) Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures. Phys Chem Earth 83–84:28–35. CrossRefGoogle Scholar
  23. Oyler JW, Dobrowski SZ, Holden ZA, Running SW (2016) Remotely sensed land skin temperature as a spatial predictor of air temperature across the conterminous United States. J Appl Meteorol Climatol 55:1441–1457. CrossRefGoogle Scholar
  24. Parkinson CL (2013) Summarizing the first ten years of NASA’s Aqua mission. IEEE J Selected Topics Appl Earth Obs Remote Sens 6:1179–1188. CrossRefGoogle Scholar
  25. Prakash S, Norouzi H, Azarderakhsh M, Blake R, Tesfagiorgis K (2016) Global land surface emissivity estimation from AMSR2 observations. IEEE Geosci Remote Sens Lett 13:1270–1274. CrossRefGoogle Scholar
  26. Prakash S, Norouzi H, Azarderakhsh M, Blake R, Khanbilvardi R (2017) Potential of satellite-based land emissivity estimates for the detection of high-latitude freeze and thaw states. Geophys Res Lett 44:2336–2342. Google Scholar
  27. Prakash S, Norouzi H, Azarderakhsh M, Blake R, Prigent C, Khanbilvardi R (2018) Estimation of consistent global microwave land surface emissivity from AMSR-E and AMSR2 observations. J Appl Meteorol Climatol 57:907–919. CrossRefGoogle Scholar
  28. Prigent C, Jimenez C, Aires F (2016) Towards all weather, long record, and real-time land surface temperature retrievals from microwave satellite observations. J Geophys Res - Atmos 121:5699–5717. CrossRefGoogle Scholar
  29. Rahmstorf S, Foster G, Cahill N (2017) Global temperature evolution: recent trends and some pitfalls. Environ Res Lett 12:054001. CrossRefGoogle Scholar
  30. Ramamurthy P, Sangobanwo M (2016) Inter-annual variability in urban heat island intensity over 10 major cities in the United States. Sustainable Cities and Society 26:65–75. CrossRefGoogle Scholar
  31. Ruzmaikin A, Aumann HH, Lee J, Susskind J (2017) Diurnal cycle variability of surface temperature inferred from AIRS data. J Geophys Res - Atmos 122:10928–20938. CrossRefGoogle Scholar
  32. Shati F, Prakash S, Norouzi H, Blake R (2018) Assessment of differences between near-surface air and soil temperatures for reliable detection of high-latitude freeze and thaw states. Cold Reg Sci Technol 145:86–92. CrossRefGoogle Scholar
  33. Sheng Y, Liu X, Yang X, Xin Q, Deng C, Li X (2017) Quantifying the spatial and temporal relationship between air and land surface temperatures of different land-cover types in southeastern China. Int J Remote Sens 38:1114–1136. CrossRefGoogle Scholar
  34. Stephens GL, L’Ecuyer T (2015) The Earth’s energy balance. Atmos Res 166:195–203. CrossRefGoogle Scholar
  35. Susskind J, Blaisdell JM, Iredell L (2014) Improved methodology for surface and atmospheric soundings, error estimates, and quality control procedures: the atmospheric infrared sounder science team version-6 retrieval algorithm. J Appl Remote Sens 8:084994. CrossRefGoogle Scholar
  36. Urban M, Eberle J, Huttich C, Schmullius C, Herold M (2013) Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the Pan-Arctic scale. Remote Sens 5:2348–2367. CrossRefGoogle Scholar
  37. Wan Z (2014) New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens Environ 140:36–45. CrossRefGoogle Scholar
  38. Yang YZ, Cai WH, Yang J (2017) Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in Northeast China. Remote Sens 9(5):410. CrossRefGoogle Scholar
  39. Zhou C, Wang K (2016) Land surface temperature over global deserts: mean, variability, and trends. J Geophys Res - Atmos 121:14344–14357. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.New York City College of TechnologyCity University of New YorkBrooklynUSA
  2. 2.Divecha Centre for Climate Change, Indian Institute of ScienceBengaluruIndia
  3. 3.Earth and Environmental Sciences, The Graduate CenterCity University of New YorkNew YorkUSA

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