Review of soil moisture content measurement and ice-water phase identification methods in frozen soils

Soil moisture content acts as a fundamental physical parameter to characterize multi-phase media consisting of soil solids, gas, liquid water, and ice, and it dominates freeze-thaw phase transition processes in frozen ground. Accurate quantification of unfrozen water and ice contents is essential for hydrological cycle simulation, farmland irrigation regulation, ecological environment assessment, and stability evaluation of geotechnical infrastructures such as frozen soil subgrades, slopes, and landslides. Existing review papers on soil moisture monitoring mostly focus on single measurement technology or single spatial scale, while few studies systematically compare the applicability of various techniques for differentiating ice and liquid water under freeze-thaw conditions. This research gap restricts the precise assessment of frost heave and thaw settlement risks in cold-region engineering, which necessitates a comprehensive systematic review to clarify the full technical system and key bottlenecks of ice-water differentiation.

Based on bibliometric analysis of literature published from 2005 to 2026, this paper retrieves 766 valid Chinese core papers from CNKI and 6 025 international articles from the Web of Science Core Collection. Statistical results of annual publication number, core research institutions, and keyword bursts reveal that the research hotspot has shifted from simple single-point soil moisture monitoring to multi-source data fusion and artificial intelligence inversion over recent decades. All prevailing soil moisture measurement technologies are systematically classified into contact measurement and non-contact measurement categories. The contact category includes reference oven-drying method, in-situ dielectric sensors (TDR, FDR), thermal response probes, nuclear magnetic resonance (NMR), and actively heated fiber Bragg grating (AH-FBG) sensing. The non-contact category covers shallow geophysical methods (GPR, ERT, electromagnetic induction, shallow seismic) and multi-type remote sensing inversion. Each technique is comprehensively evaluated from four dimensions: working principle, applicable spatial scale, capacity of unfrozen water-ice differentiation, and inherent error sources. Furthermore, this review elaborates on the distortion mechanism of monitoring signals triggered by phase transition: frozen soil exhibits unique thermal, dielectric, NMR, and elastic wave discrepancies between liquid water and ice, and the coexistence of bound water, capillary water, and ice crystals further aggravates the non-uniqueness of sensor response. Three major categories of interference factors affecting measurement precision are summarized, including soil physicochemical properties (texture, salinity, organic matter, bulk density), external environmental conditions (temperature fluctuation, vegetation coverage, freeze-thaw cycles) and inherent limitations of monitoring equipment, with targeted calibration and error correction strategies proposed correspondingly. In addition, this paper compares three mainstream inversion frameworks: pure empirical physical models, data-driven machine learning algorithms, and physics-data hybrid inversion constrained by hydrothermal coupling theories.

The analytical results demonstrate distinct complementary characteristics among different monitoring technologies. The oven-drying method can only serve as a calibration benchmark and fails to realize long-term continuous field monitoring. In-situ sensors enable real-time point monitoring but are highly susceptible to soil-sensor contact state and soil physicochemical properties. Shallow geophysics and satellite remote sensing expand monitoring coverage but suffer from ambiguous physical response and scale mismatch problems. A single monitoring method cannot reliably distinguish between unfrozen water and ice contents in frozen soils, and cross-validation combining laboratory tests, field sensing, geophysical prospecting, and remote sensing data is indispensable to improve phase identification accuracy. Among all inversion frameworks, the physics-data hybrid model balances physical interpretability and prediction accuracy and outperforms single-model methods. In future research, integrated space-air-ground collaborative observation networks, multi-sensor fusion algorithms, and physics-informed intelligent inversion models will become core technical approaches for achieving cross-scale, simultaneous high-precision measurement of soil moisture and ice content, providing theoretical support for hazard prevention in cold-region geotechnical engineering.