Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions

Liu, Shuai; Guo, Ying; Zhou, Shuhan; Qiu, Lisha; Zhang, Chengcheng; Shan, Wei

doi:10.3390/rs18101605

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Open AccessArticle

Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions

by

Shuai Liu

^1,†

,

Ying Guo

^1,2,3,†

,

Shuhan Zhou

¹,

Lisha Qiu

^1,2,3,

Chengcheng Zhang

^1,2,3 and

Wei Shan

^1,2,3,*

¹

Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150000, China

²

Ministry of Education Observation and Research Station of Permafrost Geo-Environment System in Northeast China, Harbin 150000, China

³

Collaborative Innovation Centre for Permafrost Environment and Road Construction and Maintenance in Northeast China, Harbin 150000, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Remote Sens. 2026, 18(10), 1605; https://doi.org/10.3390/rs18101605

Submission received: 9 April 2026 / Revised: 12 May 2026 / Accepted: 15 May 2026 / Published: 16 May 2026

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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Abstract

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.

Keywords: soil thermal conductivity; spatiotemporal dynamics; geospatial data; mechanistic model; Northeast China

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MDPI and ACS Style

Liu, S.; Guo, Y.; Zhou, S.; Qiu, L.; Zhang, C.; Shan, W. Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions. Remote Sens. 2026, 18, 1605. https://doi.org/10.3390/rs18101605

AMA Style

Liu S, Guo Y, Zhou S, Qiu L, Zhang C, Shan W. Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions. Remote Sensing. 2026; 18(10):1605. https://doi.org/10.3390/rs18101605

Chicago/Turabian Style

Liu, Shuai, Ying Guo, Shuhan Zhou, Lisha Qiu, Chengcheng Zhang, and Wei Shan. 2026. "Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions" Remote Sensing 18, no. 10: 1605. https://doi.org/10.3390/rs18101605

APA Style

Liu, S., Guo, Y., Zhou, S., Qiu, L., Zhang, C., & Shan, W. (2026). Simulating the Spatiotemporal Dynamics of Unfrozen Soil Thermal Conductivity in Northeast China Using Geospatial Data: Incorporating Vegetation to Adapt to Field Conditions. Remote Sensing, 18(10), 1605. https://doi.org/10.3390/rs18101605

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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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