The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

Predicting soil behavior under dynamic load due to earthquakes is pivotal for engineering structures and human life. Due to various limitations, such as insufficient computers and difficulties in generating models, the third-dimension effect is generally neglected in many studies. Conversely, the third-dimension effect in regions with high topographic differences, deep basins, three-dimensional heterogeneous and anisotropic environments, and alluvium is at a level that cannot be neglected. This study created a three-dimensional model of the northwest of Turkey for the first time by including surface topography. Soil properties were added to this model, and dynamic analysis was performed. This new model aims to increase the accuracy of ground motion predictions in Northwest Turkey. The accuracy of this model was analyzed using real earthquake data recorded in the study area. In addition, a new software (SiteEffect3D) with various features has been developed to create a three-dimensional mesh with topography using digital elevation model data and to perform dynamic analysis more effectively. This software has been tested comparatively with Plaxis 3D software using synthetic terrain models. The importance of this study is that in addition to its contributions to site response analysis and seismic hazard assessment, new software has been developed that can be used in similar studies. The findings will provide valuable information for seismic design and construction practices and facilitate the development of more effective strategies to reduce the potential damage from earthquakes in the region.

Soil directional emissivity plays a crucial role in canopy thermal-infrared (TIR) emissivity modeling over sparsely vegetated solo slopes. To our knowledge, the canopy emissivity model explicitly considers soil emissivity directionality, and topography does not exist. This study proposes a new canopy emissivity model under the framework of the four-stream approximation theory employed in the well-known 4SAIL model by incorporating soil directional emissivity and topography. The new model was validated by the discrete anisotropic radiative transfer (DART) model. The new model-simulated canopy emissivity data exhibited excellent consistency with the DART simulation data, and the bias, root mean square error (RMSE), and determination coefficient ( R-2 ) were -0.001, 0.003, and 0.97, respectively, under the different leaf area indices (LAIs), slopes, and view zenith angles (VZAs). Sensitivity analysis revealed that LAI and soil nadir emissivity explained most of the variance, with total sensitivity indices of 52.9% and 30.3%, respectively. The effects of soil directional emissivity, topography, and leaf angle distribution (LAD) on canopy emissivity were subsequently investigated, and the results indicated that the differences could reach more than 0.02 when soil directional emissivity and/or topography were neglected; moreover, the influence of LAD functions is not significant. The model proposed in this article provides a practical method for modeling mountainous area canopy emissivity and can improve estimates of surface broadband emissivity (BBE) and land surface temperature (LST).