2026-03-18 中国科学院（CAS）
An aerial drone photo taken on Jan. 15, 2026 shows the scenery of the Genheyuan National Wetland Park in Genhe, north China’s Inner Mongolia Autonomous Region. (Xinhua/Ma Jinrui)
＜関連情報＞
- https://english.cas.cn/newsroom/cas-in-media/202603/t20260318_1152855.shtml
- https://www.sciencedirect.com/science/article/pii/S0016706126000571
- https://www.sciencedirect.com/science/article/pii/S1569843226000634
青海チベット高原北部における土壌熱伝導率に対する水分閾値と構造の影響 Moisture-threshold and structure controls on soil thermal conductivity on the northern Qinghai–Tibet Plateau
Ren Li, Yao Xiao, Tonghua Wu, Shenning Wang, Wenhao Liu, Junjie Ma, Xiaodong Wu, Guojie Hu, Yongliang Jiao, Shengfeng Tang, Xiaofan Zhu, Jianzong Shi, Yongping Qiao
Geoderma Available online: 16 February 2026
DOI:https://doi.org/10.1016/j.geoderma.2026.117729
Highlights
- A moisture threshold governs when unfrozen STC exceeds frozen STC.
- Crossover Sr* predicted from measured properties matches field diagnostics.
- Calibrated Ke (Sr) schemes outperform the original Johansen scheme.
- Bulk density, porosity, and quartz fraction shift Sr* across sites.
Abstract
Soil thermal conductivity (STC) governs near-surface heat exchange and constrains simulations of active-layer evolution and permafrost change. Using a 10-year record from four Qinghai–Tibet Plateau sites (0–10 cm), laboratory Kersten number (Ke)–saturation (Sr) calibrations, and a structure-aware Johansen implementation, we identify a moisture-threshold reversal: under low antecedent moisture the frozen state conducts less heat than the unfrozen state, while at higher moisture the conventional ordering returns. The crossover saturation Sr* is traceable in calibrated Ke–Sr relations and observable from pre-freeze moisture, linking field diagnosis to model parameters. A compact, deployable correction follows: taper the frozen branch for Sr < Sr*, compute endmembers from measured bulk density, porosity, and quartz fraction (BD–n–q), and select the unfrozen Ke(Sr) form by soil class and dryness tendency. The scheme reduces unfrozen-season errors across the core sites and generalizes at an independent hold-out station (TGL) without site-specific tuning. The approach is transparent—inputs are observable and decisions are tied to Sr*—and is most impactful in dry, coarse, and sparsely monitored regions.
永久凍土の影響を受ける流域における、0～1mの土壌水分を10cm間隔でマッピングする地球観測および機械学習に基づく手法 Earth observation and machine-learning–based mapping of 0–1 m soil moisture at 10-cm intervals in a permafrost-affected basin
Yao Xiao, Guojie Hu, Lin Zhao, Erji Du, Zanpin Xing, Ren Li, Tonghua Wu, Xiaodong Wu, Guangyue Liu, Defu Zou, Yonghua Zhao, Nan Zhou, Yifan Wu
International Journal of Applied Earth Observation and Geoinformation Available online: 10 February 2026
DOI:https://doi.org/10.1016/j.jag.2026.105147
Highlights
- Maps 0–1 m soil moisture at 10-cm layers from limited field profiles.
- Data-efficient, EO-driven workflow scales scarce observations basin-wide.
- SHAP-guided ML yields a compact 12-predictor model with stable CV accuracy.
Abstract
Depth-resolved soil moisture is hard to obtain at basin scale because satellites sense only the upper few centimeters and field profiles are sparse. We use Earth-observation (EO) predictors with limited profile data to map volumetric water content (VWC) at 10-cm intervals from 0–1 m in a permafrost-affected basin. The pipeline benchmarks tree ensembles and then applies Shapley Additive Explanations (SHAP)-guided selection to build a compact 12-predictor Extra Trees model, followed by block-wise mapping on 30-m grids. On an external 10% hold-out, the model achieves R2 = 0.85 and RMSE = 0.08 L/L, with > 80% of residuals within ± 0.10 L/L. Skill is stable through most layers but degrades at 90–100 cm, reflecting limited test support and weaker surface constraint at depth. SHAP attribution identifies permafrost presence, wetland extent, vegetation greenness, and terrain metrics as leading controls, with surface influences weaken below 50 cm. Residual diagnostics show no pronounced distance-to-river shift in residual centroids and no significant spatial clustering in site-mean residuals (Moran’s I). The resulting GIS-ready GeoTIFFs provide depth-layered, climatology-consistent thaw-season VWC patterns that complement coarse satellite soil-moisture products by resolving subsurface heterogeneity.
