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.

中文翻译：

---

通过结合多个环境因素和基于月度的建模，从 MODIS LST 高精度估计泛北极土壤表面温度


近几十年来，全球变暖显示出“北极放大效应”，导致泛北极土壤表面温度 （SST） 发生显着变化。SST 在土壤和大气之间的能量交换中起着直接作用，是陆地-大气能量平衡的指标。遥感地表温度 （LST） 数据能够指示近地表温度，但受环境因素（如植被和积雪）的影响可能会在 LST 和 SST 之间引入偏差。在本研究中，分析了 5 个环境因素 （植被、积雪、表层土壤成分、地形和太阳辐射） 对泛北极 MODIS LST 月平均 SST 估计的重要性。然后，提出了一种基于随机森林 （RF） 算法的基于 MODIS LST 的泛北极月平均 SST 估计方法。结果表明，所有选定的环境因素都有助于基于月度的建模，其中植被在 5 月至 10 月期间发挥最大作用，在 3 月和 4 月下雪。所提方法估计的 2003 年至 2022 年泛北极月度海温均方根误差 （RMSE） 范围为 0.89 至 1.88 °C，与广泛使用的基于季节的多元线性回归 （MLR） 模型相比，仅基于 LST 降低了 42.95–––53.35 %（RMSE 在 1.56 和 4.03 °C 之间）。在寒冷季节（9 月至次年 4 月），在植被较低且无植被的区域（草地林地、草原、永久湿地和贫瘠地区），以及在温暖季节（5 月至 8 月）植被较高的地区，精度显著提高。 所提出的方法有助于从 LST 生成高精度的月平均 SST 数据，估计永久冻土范围和活动层厚度，以及了解泛北极的陆地-大气能量平衡。