We present a method to characterize soil moisture freeze‐thaw events and freezing/melting point depression using permittivity and temperature measurements, readily available from in situ sources. In cold regions soil freeze‐thaw processes play a critical role in the surface energy and water balance, with implications ranging from agricultural yields to natural disasters. Although monitoring of the soil moisture phase state is of critical importance, there is an inability to interpret soil moisture instrumentation in frozen conditions. To address this gap, we investigated the freeze‐thaw response of a widely used soil moisture probe, the HydraProbe, in the laboratory. Soil freezing curves (SFCs) and soil thawing curves (STCs) were identified using the relationship between soil permittivity and temperature. The permittivity SFC/STC was fit using a logistic growth model to estimate the freezing/melting point depression (T_f/m) and its spread (s). Laboratory results showed that the fitting routine requires permittivity changes greater than 3.8 to provide robust estimates and suggested that a temperature bias is inherent in horizontally placed HydraProbes. We tested the method using field measurements collected over the last 7 years from the Environment and Climate Change Canada and the University of Guelph's Kenaston Soil Moisture Network in Saskatchewan, Canada. By dividing the time series into freeze‐thaw events and then into individual transitions, the permittivity SFC/STC was identified. The freezing and melting point depression for the network was estimated as T_f/m = − 0.35 ± 0.2,with T_f = − 0.41 ± 0.22 °C and T_m = − 0.29 ± 0.16 °C, respectively.

Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (${\mathit{\epsilon }}_r=\mathit{\epsilon }/{\mathit{\epsilon }}_{\mathrm{0}}={\mathit{\epsilon }}^{\prime }-j{\mathit{\epsilon }}^{\prime \prime }$ ; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (${\mathit{\epsilon }}_{\mathrm{frozen}}^{\prime }=[\mathrm{3.5}$ ; 6.0], ${\mathit{\epsilon }}_{\mathrm{frozen}}^{\prime \prime }=[\mathrm{0.46}$ ; 1.2]) and thawed (${\mathit{\epsilon }}_{\mathrm{thawed}}^{\prime }=[\mathrm{6.5}$ ; 22.8], ${\mathit{\epsilon }}_{\mathrm{thawed}}^{\prime \prime }=[\mathrm{1.43}$ ; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R² = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.

Seasonally frozen ground (SFG) is a critical component of the cryosphere, yet its freezing dynamics are often oversimplified in large-scale monitoring frameworks – particularly in remote sensing (RS) and land surface modeling – through the use of binary 0 °C thresholds. This approach overlooks the physically significant “transitional” state where liquid water and ice coexist, leading to systematic errors in quantifying the timing and duration of the frozen season. To address this, we recast the Soil Freezing Characteristic Curve (SFCC) framework directly into permittivity–temperature space. By operating in dielectric space, we bypass the high uncertainty associated with soil-specific liquid water content calibrations and enable a robust categorization of soil into unfrozen, transitional, and frozen states. We fitted this model to in-situ measurements from eight monitoring networks (87 sites) across Canadian boreal forest, prairie, and tundra ecozones. Using Bayesian hierarchical partial pooling, we derived stabilized estimates of the freezing onset (T_f) and transition sharpness (b). Network-level T_f ranged from 0.15–0.44 °C, while b varied from 0.92–3.47 °C⁻¹, reflecting distinct freezing regimes. We found that the transitional state is a dominant seasonal feature at these sites, challenging binary 0 °C assumptions used in RS evaluation. In high-moisture sites characterized by thick organic insulation (e.g., within the observed eastern boreal forest networks), this state persisted for over 100 d – effectively the entire winter – despite persistent subzero air temperatures. In contrast, sites in the western boreal and prairie networks, which generally lack thick surface organic layers and have lower soil moisture, exhibited shorter but still significant transitional periods (30 and 60 d, respectively). Even in the extreme cold of the tundra network sites, the transitional phase persisted for over 40 d. These results confirm that surface insulation and soil moisture, rather than air temperature alone, govern the SFG regime at the observed locations, providing a reproducible, physically-based reference framework for the next generation of freeze–thaw products.