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Land surface temperature variability across India: a remote sensing satellite perspective

  • Satya PrakashEmail author
  • Hamid Norouzi
Original Paper
  • 48 Downloads

Abstract

Land surface temperature (LST) plays a key role in the surface energy budget computation and land surface process studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua and Terra satellites provide comprehensive global LST estimates at a fine spatial resolution. The MODIS products were recently upgraded to Collection 6, and shown to be more accurate than its predecessor Collection 5 products. In this study, LST and its variability have been examined across India from Collection 6 of the Aqua MODIS data at 0.05° spatial resolution for the period of 2003 to 2017. All-India mean LST characteristics show distinctive features as compared to the well-documented mean characteristics of near-surface air temperature. All land cover types except permanent snow and ice, and cold desert areas exhibit bimodal peaks in seasonal variations of daytime LST. The daytime LST over the coldest and high-altitude regions of northern India shows anomalous positive linear relationship with NDVI at a monthly scale. However, monthly domain-mean daytime LST of cropland regions is largely negatively correlated with NDVI as compared to other land cover types. Results reveal that about 17% of the Indian landmass received its hottest LST during 2010 followed by 2016. Linear trend analysis for the 15-year period of mean annual LST shows a decrease in diurnal temperature range over most parts of the country due to rather rapid increase in nighttime LST than daytime LST, similar as changes in near-surface air temperature across the country.

Notes

Acknowledgment

The authors would like to thank the editor and anonymous reviewer for their constructive comments. The MODIS data products were obtained from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.

References

  1. Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int J Appl Earth Obs Geoinf 67:30–42.  https://doi.org/10.1016/j.jag.2017.12.009 CrossRefGoogle Scholar
  2. Didan K (2015) MYD13C2 MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05Deg CMG V006. NASA EOSDIS LP DAAC.  https://doi.org/10.5067/MODIS/MYD13C2.006
  3. 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.  https://doi.org/10.1007/s00704-016-1945-0 CrossRefGoogle Scholar
  4. Duan S-B, Li Z-L, Li H, Gottsche F-M, Wu H, Zhao W, Leng P, Zhang X, Coll C (2019) Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sens Environ 225:16–29.  https://doi.org/10.1016/j.rse.2019.02.020 CrossRefGoogle Scholar
  5. Friedl M, Sulla-Menashe D (2015) MCD12C1 MODIS/Terra+Aqua Land cover type yearly L3 global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD12C1.006.
  6. Gesch DB, Larson KS (1996) Techniques for development of global 1-kilometer digital elevation models. In Pecora Thirteen, Human Interactions with the Environment – Perspectives from Space, Sioux Falls, South Dakota.Google Scholar
  7. Hao Z, AghaKouchak A, Phillips TJ (2013) Changes in concurrent monthly precipitation and temperature extremes. Environ Res Lett 8:034014.  https://doi.org/10.1088/1748-9326/8/3/034014 CrossRefGoogle Scholar
  8. Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Lewis P, Barnsley MJ (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36:1228–1249CrossRefGoogle Scholar
  9. 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 CrossRefGoogle Scholar
  10. Khandan R, Gholamnia M, Duan S-B, Ghadimi M, Alavipanah SK (2018) Characterization of maximum land surface temperatures in 16 years from MODIS in Iran. Environ Earth Sci 77:450–411.  https://doi.org/10.1007/s12665-018-7623-z CrossRefGoogle Scholar
  11. Li Z-L, Tang B-H, 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 CrossRefGoogle Scholar
  12. Li Y, Zhao M, Mildrexler DJ, Motesharrei S, Mu Q, Kalnay E, Zhao F, Li S, Wang K (2016) Potential and actual impacts of deforestation and afforestation on land surface temperature. J Geophys Res - Atmos 121:14372–14386.  https://doi.org/10.1002/2016JD024969 CrossRefGoogle Scholar
  13. Marzban F, Sodoudi S, Preusker R (2018) The influence of land-cover type on the relationship between NDVI-LST and LST-T air. Int J Remote Sens 39:1377–1398.  https://doi.org/10.1080/01431161.2017.1402386 CrossRefGoogle Scholar
  14. Mathew A, Khandelwal S, Kaul N (2017) Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters. Sustain Cities Soc 35:157–177.  https://doi.org/10.1016/j.scs.2017.07.013 CrossRefGoogle Scholar
  15. Mildrexler DJ, Zhao M, Running SW (2011) Satellite finds highest land skin temperatures on Earth. Bull Amer Meteor Soc 92:855–860.  https://doi.org/10.1175/2011BAMS3067.1 CrossRefGoogle Scholar
  16. Mildrexler DJ, Zhao M, Cohen WB, Running SW, Song XP, Jones MO (2018) Thermal anomalies detect critical global land surface changes. J Appl Meteorol Climatol 57:391–411.  https://doi.org/10.1175/JAMC-D-17-0093.1 CrossRefGoogle Scholar
  17. Norouzi H, Temimi M, AghaKouchak A, Azarderakhsh M, Khanbilvardi R, Shields G, Tesfagiorgis K (2015a) Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures. Phys Chem Earth 83-84:28–35.  https://doi.org/10.1016/j.pce.2015.01.007 CrossRefGoogle Scholar
  18. Norouzi H, Temimi M, Prigent C, Turk J, Khanbilvardi R, Tian Y, Furuzawa FA, Masunaga H (2015b) Assessment of the consistency among global microwave land surface emissivity products. Atmos Meas Tech 8:197–1205.  https://doi.org/10.5194/amt-8-1197-2015 CrossRefGoogle Scholar
  19. Parida BR, Oinam B, Patel NR, Sharma N, Kandwal R, Hazarika MK (2008) Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India. Int J Remote Sens 29:4219–4235.  https://doi.org/10.1080/01431160701871096 CrossRefGoogle Scholar
  20. Parkinson CL (2013) Summarizing the first ten years of NASA’s Aqua mission. IEEE J Sel Top Appl Earth Obs Remote Sens 6:1179–1188.  https://doi.org/10.1109/JSTARS.2013.2239608 CrossRefGoogle Scholar
  21. Patel S, Joshi JP, Bhatt B (2017) An assessment of spatio-temporal variability of land surface temperature using MODIS: a study of Gujarat state, India. Geogr Compass 11:e12312.  https://doi.org/10.1111/gec3.12312 CrossRefGoogle Scholar
  22. Phan TN, Kappas M (2018) Application of MODIS land surface temperature data: a systematic literature review and analysis. J Appl Remote Sens 12:041501.  https://doi.org/10.1117/1.JRS.12.041501 CrossRefGoogle Scholar
  23. Platnick S, Hubanks P, Meyer K, King MD (2015) MODIS Atmosphere L3 monthly product (08_L3). NASA MODIS adaptive processing system, Goddard Space Flight Center, USA.  https://doi.org/10.5067/MODIS/MYD08_M3.006.
  24. Prakash S (2018) Capabilities of satellite-derived datasets to detect consecutive Indian monsoon droughts of 2014 and 2015. Curr Sci 114:2362–2368.  https://doi.org/10.18520/cs/v114/i11/2362-2368 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.  https://doi.org/10.1109/LGRS.2016.2581140 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.  https://doi.org/10.1002/2017GL072560 CrossRefGoogle 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.  https://doi.org/10.1175/JAMC-D-17-0213.1 CrossRefGoogle Scholar
  28. Prakash S, Shati F, Norouzi H, Blake R (2019) Observed differences between near-surface air and skin temperatures using satellite and ground-based data. Theor Appl Climatol 137:587–600.  https://doi.org/10.1007/s00704-018-2623-1 CrossRefGoogle Scholar
  29. Prigent C, Jimenez C, Aires F (2016) Toward “all-weather”, long record, and real-time land surface temperature retrievals from microwave satellite observations. J Geophys Res - Atmos 121:5699–5717.  https://doi.org/10.1002/2015JD024402 CrossRefGoogle Scholar
  30. Roy PS et al (2015) Development of decadal (1985-1995-2005) land use and land cover database for India. Remote Sens 7:2401–2430.  https://doi.org/10.3390/rs70302401 CrossRefGoogle Scholar
  31. Schmidt GA, Shindell DT, Tsigaridis K (2014) Reconciling warming trends. Nat Geosci 7:158–160.  https://doi.org/10.1038/ngeo2105 CrossRefGoogle Scholar
  32. Sharifnezhadazizi Z, Norouzi H, Prakash S, Beale C, Khanbilvardi R (2019) A global analysis of land surface temperature diurnal cycle using MODIS observations. J Appl Meteorol Climatol 58:1279–1291.  https://doi.org/10.1175/JAMC-D-18-0256.1 CrossRefGoogle Scholar
  33. 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.  https://doi.org/10.1016/j.coldregions.2017.10.007 CrossRefGoogle Scholar
  34. Singh R, Singh C, Ojha SP, Kumar AS, Kishtawal CM, Kiran Kumar AS (2016) Land surface temperature from INSAT-3D imager data: Retrieval and assimilation in NWP model. J Geophys Res - Atmos 121:6909–6926.  https://doi.org/10.1002/2016JD024752 CrossRefGoogle Scholar
  35. Sulla-Menashe D, Gray JM, Abercrombie SP, Friedl MA (2019) Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 land cover product. Remote Sens Environ 222:183–194.  https://doi.org/10.1016/j.rse.2018.12.013 CrossRefGoogle Scholar
  36. Susskind J, Schmidt GA, Lee JN, Iredell L (2019) Recent global warming as confirmed by AIRS. Environ Res Lett 14:044030.  https://doi.org/10.1088/1748-9326/aafd4e CrossRefGoogle Scholar
  37. Turco M, Palazzi E, Hardenberg J, Provenzale A (2015) Observed climate change hotspots. Geophys Res Lett 42:3521–3528.  https://doi.org/10.1002/2015GL063891 CrossRefGoogle Scholar
  38. Vinnarasi R, Dhanya CT, Chakravorthy A, AghaKouchak A (2017) Unravelling diurnal asymmetry of surface temperature in different climate zones. Sci Rep 7:7350.  https://doi.org/10.1038/s41598-017-07627-5 CrossRefGoogle Scholar
  39. Wan Z (2014) New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens Environ 140:36–45.  https://doi.org/10.1016/j.rse.2013.08.027 CrossRefGoogle Scholar
  40. Wan Z, Hook S, Hulley G (2015) MYD11C1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006. NASA EOSDIS LP DAAC.  https://doi.org/10.5067/MODIS/MYD11C1.006.
  41. Wei N, Zhou L, Dai Y, Xia G, Hua W (2017) Observational evidence for desert amplification using multiple satellite datasets. Sci Rep 7:2043.  https://doi.org/10.1038/s41598-017-02064-w CrossRefGoogle Scholar
  42. Zhao W, He J, Wu Y, Xiong D, Wen F, Li A (2019) An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sens 11:900.  https://doi.org/10.3390/rs11080900 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Divecha Centre for Climate ChangeIndian Institute of ScienceBengaluruIndia
  2. 2.New York City College of TechnologyCity University of New YorkBrooklynUSA
  3. 3.Earth and Environmental SciencesThe Graduate Center, City University of New YorkNew YorkUSA

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