Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions.
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| Title: | Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions. |
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| Authors: | Liu, Shuai1 (AUTHOR), Guo, Ying1,2,3 (AUTHOR), Zhou, Shuhan1,3 (AUTHOR), Qiu, Lisha1,2,3 (AUTHOR), Zhang, Chengcheng1,2,3 (AUTHOR), Shan, Wei1,2,3 (AUTHOR) shanwei@nefu.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1605. 29p. |
| Subjects: | Geospatial data, Thermal conductivity, Plant ecology, Spatiotemporal processes, Frozen ground |
| Geographic Terms: | Manchuria (China) |
| Abstract: | Highlights: What are the main findings? Incorporating vegetation types into the Johansen–Tarnawski framework (JT-V model) significantly enhances simulation accuracy, reducing the RMSE by 53% compared to traditional models. In Northeast China, soil thermal conductivity is lower in the Khingan Mountains and Inner Mongolia Plateau but higher in the Northeast Plain, and follows a three-phase thawing-season dynamic (stable, rapid increase, gradual decline). What are the implications of the main findings? The JT-V model and its high-resolution (1 km) spatiotemporal maps provide a robust parameterization tool for improving the reliability of land surface, hydrological, and cryosphere models in cold regions. The identified regulatory role of surface soil thermal conductivity as an insulating barrier offers practical insights for infrastructure engineering and the preservation of permafrost under climate warming. Soil thermal conductivity (STC) is vital for environmental and engineering modeling, yet traditional unfrozen STC estimates often perform poorly under field conditions. This study develops an enhanced Johansen–Tarnawski model incorporating vegetation parameters (JT-V) and applies geospatial data for regional simulation. Residuals from mechanistic predictions were analyzed using Geodetector and Random Forest, revealing strong vegetation-type effects. Validation with 88 samples from 18 sites across five vegetation types showed the JT-V model significantly improved accuracy: R2 rose from 0.426 to 0.716, and RMSE decreased by 53%. The best performance occurred at the surface layer (RMSE = 0.074 W·m−1·K−1), with errors increasing with depth. Over 83% of sites achieved R2 > 0.7, and most linear regression slopes fell between 0.8 and 1.1. Applying JT-V to simulate thawing-season STC in Northeast China, it was found that lower values predominated in the Khingan Mountains and the Inner Mongolia Plateau, while higher values occurred across the Northeast Plain. Temporal dynamics exhibited three stages: stability (May–mid-July), rapid rise (mid-July–mid-August), and gradual decline (mid-August–September). The improved model advances regional land surface simulations and supports agricultural and engineering applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Incorporating vegetation types into the Johansen–Tarnawski framework (JT-V model) significantly enhances simulation accuracy, reducing the RMSE by 53% compared to traditional models. In Northeast China, soil thermal conductivity is lower in the Khingan Mountains and Inner Mongolia Plateau but higher in the Northeast Plain, and follows a three-phase thawing-season dynamic (stable, rapid increase, gradual decline). What are the implications of the main findings? The JT-V model and its high-resolution (1 km) spatiotemporal maps provide a robust parameterization tool for improving the reliability of land surface, hydrological, and cryosphere models in cold regions. The identified regulatory role of surface soil thermal conductivity as an insulating barrier offers practical insights for infrastructure engineering and the preservation of permafrost under climate warming. Soil thermal conductivity (STC) is vital for environmental and engineering modeling, yet traditional unfrozen STC estimates often perform poorly under field conditions. This study develops an enhanced Johansen–Tarnawski model incorporating vegetation parameters (JT-V) and applies geospatial data for regional simulation. Residuals from mechanistic predictions were analyzed using Geodetector and Random Forest, revealing strong vegetation-type effects. Validation with 88 samples from 18 sites across five vegetation types showed the JT-V model significantly improved accuracy: R2 rose from 0.426 to 0.716, and RMSE decreased by 53%. The best performance occurred at the surface layer (RMSE = 0.074 W·m−1·K−1), with errors increasing with depth. Over 83% of sites achieved R2 > 0.7, and most linear regression slopes fell between 0.8 and 1.1. Applying JT-V to simulate thawing-season STC in Northeast China, it was found that lower values predominated in the Khingan Mountains and the Inner Mongolia Plateau, while higher values occurred across the Northeast Plain. Temporal dynamics exhibited three stages: stability (May–mid-July), rapid rise (mid-July–mid-August), and gradual decline (mid-August–September). The improved model advances regional land surface simulations and supports agricultural and engineering applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101605 |
