High-precision estimation of pan-Arctic soil surface temperature from MODIS LST by incorporating multiple environment factors and monthly-based modeling

• Published in: International Journal of Applied Earth Observation and Geoinformation
• Main Authors: Hongxiang Guo, Wenquan Zhu, Cunde Xiao, Cenliang Zhao, Liyuan Chen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2024
• Subjects: Soil surface temperature; Pan-arctic; LST; Environment factors; Monthly-based modeling; Physical geography; GB3-5030; Environmental sciences; GE1-350; Arctic; Active layer thickness; Global warming; permafrost
• Online Access: https://doi.org/10.1016/j.jag.2024.104114; https://doaj.org/article/2a83d6c02592468abf3e1df65772e098

Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.