Abstract
It is essential to monitor the ground temperature over large areas to understand and predict the effects of climate change on permafrost due to its rapid warming on the Qinghai-Tibet Plateau (QTP). Land surface temperature (LST) is an important parameter for the energy budget of permafrost environments. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are especially valuable for detecting permafrost thermal dynamics across the QTP. This study presents a comparison of MODIS-LST values with in situ near-surface air temperature (Ta), and ground surface temperature (GST) obtained from 2014 to 2016 at five sites in Beiluhe basin, a representative permafrost region on the QTP. Furthermore, the performance of the thermal permafrost model forced by MODIS-LSTs was studied. Averaged LSTs are found to strongly correlated with Ta and GST with R2 values being around 0.9. There is a significant warm bias (4.43–4.67 °C) between averaged LST and Ta, and a slight warm bias (0.67–2.66 °C) between averaged LST and GST. This study indicates that averaged MODIS-LST is supposed to be a useful data source for permafrost monitoring. The modeled ground temperatures and active-layer thickness have a good agreement with the measurements, with a difference of less than 1.0 °C and 0.4 m, respectively.
Highlights
Permafrost, which is defined as ground that remains frozen for two or more consecutive years, plays a crucial role in the energy and water cycle in cold regions [1,2,3]
Terra and Aqua (MODIS-land surface temperature (LST)) became available, the percentage of clear-sky condition decreases to about 20% to 23% during the study period
We compare the average LST acquired from different Moderate Resolution Imaging Spectroradiometer (MODIS) products to in situ daily mean ground surface temperature (GST) and Ta in diverse alpine ecosystems in Beiluhe basin, a permafrost region on the Qinghai-Tibet Plateau
Summary
Permafrost, which is defined as ground that remains frozen for two or more consecutive years, plays a crucial role in the energy and water cycle in cold regions [1,2,3]. Permafrost is thawing at the global scale in response to climate change [4] and, in turn, modify local and regional hydrological and ecological systems [3]. This trend will likely continue over the coming decades under the warming climate [5,6]. To assess its vulnerability related to climate change, it is essential to continuously map and monitor the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) [7]. To date predicting the ground surface temperature (GST) remains a challenge in permafrost modeling because of the typical approach of employing the simplistic n-factor [3]. GST is the in situ temperature at the Sensors 2019, 19, 4200; doi:10.3390/s19194200 www.mdpi.com/journal/sensors
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