Monitoring Land Surface Temperature (LST) Changes in Northern Alaska's Permafrost Regions Using MODIS Data

Memis, Metehan Alp; Ulus Memis, Sevval

doi:10.48309/jeires.2026.561167.1325

Monitoring Land Surface Temperature (LST) Changes in Northern Alaska's Permafrost Regions Using MODIS Data

Document Type : Original Research Article

Authors

Metehan Alp Memis ¹

Sevval Ulus Memis ²

¹ Department of Civil Engineering, Southern Illinois University Edwardsville (SIUE), Edwardsville, IL, USA

² Department of Data Science, Maryville University, St. Louis, MO, USA

https://doi.org/10.48309/jeires.2026.561167.1325

Abstract

This study investigates multiannual variations in land surface temperature (LST) across four representative permafrost regions in northern Alaska—Prudhoe Bay, Barrow, the Brooks Range, and the Yukon Flats—to identify short-term surface warming trends. MODIS Terra MOD11A2 V6.1 data were processed using the Google Earth Engine (GEE) platform to compute annual mean LST values for the period 2020–2025. Warming rates (°C/year) were quantified through linear regression analysis. The results reveal a consistent regional warming pattern, with positive temperature trends observed at all sites. Among the studied regions, the Yukon Flats exhibited the most pronounced warming, reaching positive LST values of approximately +1 °C by 2025. Gradual warming was also detected in the coastal regions of Barrow (approximately −8 °C in 2025) and Prudhoe Bay (approximately −6 °C in 2025). These findings demonstrate that the integration of MODIS data with GEE provides an accurate and efficient framework for monitoring regional permafrost thermal dynamics and underscores the importance of remote sensing in evaluating climate change impacts on ground stability in Alaska.

Graphical Abstract

Keywords

MODIS

Land surface Temperature

Google earth engine

Permafrost

Remote sensing

Subjects

Engineering

OPEN ACCESS

©2026 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

[1] AMAP, E. Arctic climate change update 2021: Key trends and impacts. Summary for policy-makers. Arctic Monitoring and Assessment Programme (AMAP), 2021, 16.

[2] Auger, J. Past, present, and future arctic climate and national/community risk assessment. 2019.

[3] Screen, J.A., Simmonds, I. The central role of diminishing sea ice in recent arctic temperature amplification. Nature, 2010, 464(7293), 1334-1337.

[4] Serreze, M.C., Barry, R.G. Processes and impacts of arctic amplification: A research synthesis. Global and Planetary Change, 2011, 77(1-2), 85-96.

[5] Biskaborn, B.K., Smith, S.L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D.A., Schoeneich, P., Romanovsky, V.E., Lewkowicz, A.G., Abramov, A. Permafrost is warming at a global scale. Nature Communications, 2019, 10(1), 264.

[6] Schuur, E.A., McGuire, A.D., Schädel, C., Grosse, G., Harden, J.W., Hayes, D.J., Hugelius, G., Koven, C.D., Kuhry, P., Lawrence, D.M. Climate change and the permafrost carbon feedback. Nature, 2015, 520(7546), 171-179.

[7] Schwoerer, T., Schmidt, J.I., Berman, M., Bieniek, P., Farquharson, L.M., Nicolsky, D., Powell, J., Roberts, R., Thoman, R., Ziel, R. Increasing multi-hazard climate risk and financial and health impacts on northern homeowners. Ambio, 2024, 53(3), 389-405.

[8] Turetsky, M.R., Abbott, B.W., Jones, M.C., Anthony, K.W., Olefeldt, D., Schuur, E.A., Grosse, G., Kuhry, P., Hugelius, G., Koven, C. Carbon release through abrupt permafrost thaw. Nature Geoscience, 2020, 13(2), 138-143.

[9] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017, 202, 18-27.

[10] Hulley, G., Hook, S. Mod21a2 modis/terra land surface temperature/3-band emissivity 8-day l3 global 1km sin grid v006. NASA EOSDIS Land Processes Distributed Active Archive Center (DAAC) data set, 2017, MOD21A22. 006.

[11] Phan, T.N., Kappas, M. Application of modis land surface temperature data: A systematic literature review and analysis. Journal of Applied Remote Sensing, 2018, 12(4), 041501-041501.

[12] Philipp, M., Dietz, A., Buchelt, S., Kuenzer, C. Trends in satellite earth observation for permafrost related analyses—a review. Remote Sensing, 2021, 13(6), 1217.

[13] Qi, Y., Li, S., Ran, Y., Wang, H., Wu, J., Lian, X., Luo, D. Mapping frozen ground in the qilian mountains in 2004–2019 using google earth engine cloud computing. Remote Sensing, 2021, 13(1), 149.

[14] Melvin, A.M., Larsen, P., Boehlert, B., Neumann, J.E., Chinowsky, P., Espinet, X., Martinich, J., Baumann, M.S., Rennels, L., Bothner, A. Climate change damages to alaska public infrastructure and the economics of proactive adaptation. Proceedings of the National Academy of Sciences, 2017, 114(2), E122-E131.

[15] Tedesche, M.E., Trochim, E.D., Fassnacht, S.R., Wolken, G.J. Brooks range perennial snowfields: Extent detection from the field and via satellite. The Cryosphere Discussions, 2022, 2022, 1-29.

[16] Edwards, M., Grosse, G., Jones, B.M., McDowell, P. The evolution of a thermokarst-lake landscape: Late quaternary permafrost degradation and stabilization in interior Alaska. Sedimentary Geology, 2016, 340, 3-14.

[17] Wan, Z., Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4), 892-905.

[18] Wan, Z. Modis land surface temperature products users’ guide. Institute for Computational Earth System Science, University of California: Santa Barbara, CA, USA, 2006, 805, 26.

[19] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 2011, 12, 2825-2830.

[20] Yi, Y., Kimball, J.S., Chen, R.H., Moghaddam, M., Reichle, R.H., Mishra, U., Zona, D., Oechel, W.C. Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska. The Cryosphere, 2018, 12(1), 145-161.

[21] Rantanen, M., Karpechko, A.Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., Laaksonen, A. The arctic has warmed nearly four times faster than the globe since 1979. Communications Earth & Environment, 2022, 3(1), 168.

[22] Jones, B.M., Amundson, C.L., Koch, J.C., Grosse, G. Thermokarst and thaw-related landscape dynamics--an annotated bibliography with an emphasis on potential effects on habitat and wildlife. USGS, 2013.

[23] Zhang, T. Influence of the seasonal snow cover on the ground thermal regime: An overview. Reviews of Geophysics, 2005, 43(4).

[24] Cheng, G., Wu, T. Responses of permafrost to climate change and their environmental significance, qinghai‐tibet plateau. Journal of Geophysical Research: Earth Surface, 2007, 112(F2).

[25] Gubler, S. Measurement variability and model uncertainty in mountain permafrost research. 2013.