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Open AccessArticle

Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

1
Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany
2
Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bussestr. 24, 27570 Bremerhaven, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2329; https://doi.org/10.3390/rs11192329
Received: 25 July 2019 / Revised: 26 September 2019 / Accepted: 6 October 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Remote Sensing of Permafrost Environment Dynamics)
Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings. View Full-Text
Keywords: LST; thermal remote sensing; Landsat time series; arctic greening; Google Earth Engine LST; thermal remote sensing; Landsat time series; arctic greening; Google Earth Engine
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Nill, L.; Ullmann, T.; Kneisel, C.; Sobiech-Wolf, J.; Baumhauer, R. Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018. Remote Sens. 2019, 11, 2329.

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