Abstract
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.
Highlights
Arctic landscapes have experienced rapidly increasing air temperatures of 0.6 ◦C per decade over the last 30 years, which is in an order of magnitude twice as high as the global average [1]
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 Land Surface Temperatures (LSTs)
This study investigated Landsat derived summer LST and multispectral indices between 1985 and 2018 and presents an overview of the mean summer LST, NDVI, and NDWI for the Arctic Mackenzie Delta Region, Northern Canada
Summary
Arctic landscapes have experienced rapidly increasing air temperatures of 0.6 ◦C per decade over the last 30 years, which is in an order of magnitude twice as high as the global average [1]. Arctic river deltas are considered to be majorly affected by rising temperatures, as they are located at the interface of the marine and terrestrial ecosystems [2]. These regions are extensively underlain by permafrost and are sensitive to alterations in the thermal regime. The rising temperatures affect the state of the permafrost and contribute to hydrological alterations, as well as vast land cover and vegetation changes [8]. Satellite-derived Land Surface Temperature (LST) characterizes the land–atmosphere exchange of energy and depends on a variety of surficial properties, such as vegetation type, soil, and plant moisture, or surface roughness [13]. LST may change with the alteration of the surficial properties, allowing environmental change to be characterized by means of time series remote sensing
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