Thaw State Research Articles

Landsat data-driven identification of surface soil freeze and thaw states: a comparison of various target detection and semantic segmentation models

ABSTRACT Frozen soil is a vital component of the cryosphere and is sensitive to climate changes, accelerating thaw when the anthropogenic climate warms. Identifying the state of soil freeze/thaw is crucial for effective water resource management, cold region engineering construction, agriculture development, etc. In this study, we used Landsat remote sensing images data to identify the freeze/thaw state of frozen soil, focusing on the eastern Tibetan Plateau (ETP) as a case study. We compared the target detection models of two architectures and the semantic segmentation model framework of four models with different encoders. We used Mean Pixel Accuracy (MAP), Mean Intersection over Union (MIoU), Taylor diagrams, and surface borehole data as metrics to analyze the model’s accuracy. Our results showed that the YOLOv8-Large target detection model performed better than other detection models, with the MAP50 of 0.53. The ResNet34 encoder in the semantic segmentation model demonstrated the highest accuracy of 86.92%, effectively resisting interference, and could identify freeze–thaw with higher accuracy in noisy images. Our study provides a high-resolution pathway to identify freeze/thaw state of frozen soils, which can benefit studies in cryosphere science, hydrology, ecology and climate change.

Changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the Tibetan Plateau

AbstractChanges in the soil freeze–thaw status will inevitably affect the thermal conditions and properties of the Tibetan Plateau (TP), thereby affecting its upper atmosphere, and further afield in East Asia and even globally. In this study, using the soil temperature simulated by the Community Land Model Version 5.0 (CLM5.0), the timing and duration of the soil freeze–thaw status were divided into freeze start‐date, freeze end‐date and freeze duration. Then, using linear trend estimation, correlation analysis and other methods, the changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the TP were analysed, and the relationships between them and surface temperature, altitude and latitude were analysed. The results obtained were as follows: (1) The soil temperature simulated by CLM5.0 can reasonably reproduce the seasonal changes in multilayer soil temperature, and correlated well with observations. Simulated by CLM5.0 of the time and duration of the soil freeze–thaw status also correlated well with observations. (2) The spatial distribution of the soil freeze–thaw status is characterized by a trend of delayed freezing, advanced thawing and shortened freeze duration from northwest to southeast over the TP. From 1979 to 2018, the freeze start‐date postponed by 7.3 days and became delayed at a rate of 1.9 days per decade, while the freeze end‐date advanced by 6.4 days at a rate of 1.7 days per decade, and the freeze duration shortened by 13.7 days at a rate of 3.6 days per decade. The timing and duration of the soil freeze–thaw status vary across different regions of the TP. The freeze start‐date in all areas of the TP has been delayed in the past 39 years. Except for the subcold zone and arid regions of the TP, the freeze end‐date has occurred earlier and the freeze duration has shortened, with the most significant changes in the subcold zone and humid regions, while the freeze end‐date has advanced at a rate of 3.6 days per decade and the freeze duration has shortened at a rate of 6.3 days per decade. (3) The timing and duration of the soil freeze–thaw status are significantly correlated with surface air temperature, elevation and latitude, exceeding the 99% confidence level. The correlation between the timing and duration of the soil freeze–thaw status and surface temperature is strongest, followed by altitude, and correlation with latitude is weaker. The correlation between surface air temperature and the timing and duration of the soil freeze–thaw status in the western TP is stronger than that in the eastern TP. The rate of change in the soil freeze–thaw status increases with altitude to 3000 m above sea level, while this rate decreases with elevation above 3000 m. The rate of change in the soil freeze–thaw status is greatest at 29°N, while the rate of delay in the freeze start‐date is minimal at 33°N and the rates of advancement in the freeze end‐date and shortening of the freeze duration are minimal at 35°N.

High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements

Landscape freeze–thaw (F/T) state parameters are an integral part of research on terrestrial hydrological processes, vegetation growth dynamics, and terrestrial–atmospheric trace gas exchange. Therefore, the development of large-scale, continuous, and rapid F/T observation records is essential. This study proposes a scheme to effectively retrieve quasi-global daily soil F/T states from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations. This method takes into account the GNSS-R surface reflectivity and its influence factor. Specifically, the method employed surface reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) data from 2018 to 2020, along with vegetation opacity data from Soil Moisture Active Passive (SMAP) as input layers in random forests for F/T retrieval. This approach provides F/T results on a quasi-global scale, does not rely on continuous F/T observations, and employs the least ancillary data. To validate the retrieval results, SMAP F/T data over the same period and temperature data from more than 100 ground stations were also used as reference. Results showed that the F/T retrieval accuracy of CYGNSS was 95.5% and 82.0% with respect to the SMAP data and data from independent in situ stations, respectively. Given the high temporal coverage of the CYGNSS F/T results, the temporal coverage improvement is 23.3% compared with the actual SMAP observations on a daily scale. Furthermore, as gap-filling data complemented SMAP, CYGNSS data can effectively improve the SMAP F/T sample interval (accuracy improvement of 26.0%). More importantly, the F/T accuracy of the CYGNSS and SMAP combination based on monthly performance is 85.1%, i.e., 20.5% better than that of the existing SMAP daily F/T product. Overall, the quasi-global daily F/T results obtained from CYGNSS and SMAP measurements can complement existing databases with well-defined accuracy.

Full Polarimetric GNSS-R Assessment of the Freeze and Thaw States of the Terrestrial Cryosphere

Full Polarimetric GNSS-R Assessment of the Freeze and Thaw States of the Terrestrial Cryosphere

Response of spring vegetation phenology to soil freeze–thaw state in the Northern Hemisphere from 2016 to 2022

IntroductionThe research on spring vegetation phenology is crucial to the investigation of terrestrial ecosystems and climate change. Changes in the soil freeze–thaw (F/T) lead to variations in soil moisture, directly impacting vegetation activity. The start of the season (SOS) is the initial and important phenophase for vegetation activity, and thus, this highlights the need to understand the response of spring vegetation phenology to soil F/T state.MethodsThis study first comprehensively investigates the consistency of the SOS and three soil F/T state indexes, i.e., the start day of the F/T state (SFT), the end day of the F/T state (EFT), and the length of days of the F/T state (LFT) via satellite data source.ResultsResults reveal that: (1) All 3 F/T state indexes impact SOS values, and the EFT outperforms others. The correlation coefficients between EFT and SOS gain around 3.07%. (2) A temporal overlap between SOS and EFT occurs in May, suggesting that parts of the plants begin active growth before average temperatures reach above 0°. (3) Small differences of SOS and EFT exist between savannas, and croplands, with an average difference of less than 10 days; in contrast, the largest differences occur in broadleaf evergreen forests. The results can fill the knowledge gap on the response of spring vegetation phenology to soil F/T state, and help to investigate the reasons for the nonlinear dynamics of SOS under global warming.

Discrimination and mapping ground surface freeze and thaw states over Northeastern China based on the improved dual-index algorithm

Accurate delineation of spatiotemporal variations in ground surface soil freeze and thaw (F/T) states is essential to appraise many geoscience issues, such as the hydrological circulation and land surface-atmosphere feedbacks. Recently, an Improved Dual-index algorithm (DIA) method was proposed by accounting for the influence of soil moisture variations on the discrimination accuracy with passive microwave remote sensing (RS) data products. Compared with the original DIA, the Improved DIA method has proven to be a more practical approach on surface soil F/T states discrimination. However, the method has only been applied and verified in cold regions of high-altitude (e.g., Tibetan Plateau), it's applicability and effectiveness in the cold areas in mid-high latitudes, where the geographic and climatic conditions are quite different, yet remained to be further explored. The present study investigated the feasibility of using AMSR-E (the Advanced Microwave Scanning Radiometer-EOS) and AMSR2 (the second Advance Microwave Scanning Radiometer) passive microwave RS data products to discriminate the F/T states of the ground surface for a long period from 2002 to 2019 by means of the Improved DIA method over a typical mid-high latitude cold region of Northeastern China. Seasonal variation characteristics of soil moisture in mid-high latitude areas were similar with those in high-altitude areas, even though the spatial heterogeneity of soil water content was significant in different regions. Discriminating surface soil F/T states with the Improved DIA method derived overall discriminating accuracy of about 91.6% in the study area, which demonstrated excellent feasibility of the Improved DIA method in mid-high latitude cold regions. The mapping results shown surface soil F/T cycle in Northeastern China responding to climate change was examined from the perspective of regional average, both the proportion of frozen soil area and frozen days showed significant decreasing trends continuously with differed quite spatially. The discriminating accuracy of the Improved DIA method was found to be lower in plain areas with dense populations and large farmland areas compared to mountainous areas when human activities were not taken into consideration, as quantifying human activities can be challenging. The Improved DIA method has been well verified in both high-altitude and high-latitude regions; it has great potential in global scale research.

Remote Sensing Soil Freeze‐Thaw Status and North American N2O Emissions From a Regional Inversion

AbstractNorth American nitrous oxide (N2O) emissions over 2011–2018 are estimated using the CarbonTracker‐Lagrange regional inversion framework. Emissions are strongest in the Midwestern corn/soybean belt and display a distinct dual maxima seasonal pattern. The first maximum occurs in late winter/early spring, suggestive of freeze‐thaw (FT) effects on denitrification rates and associated N2O emissions. The second maximum occurs in late spring/early summer, consistent with a growing season nitrogen fertilizer‐driven source, although fertilizer applied in late fall may contribute to the FT pulse as well. Interannual variability in the first maximum correlates significantly to soil freeze thaw status derived from remote sensing data. A requisite frozen period in the preceding early winter appears necessary to create conditions for the N2O pulse after thawing. The FT pulse is a prominent feature of the annual cycle in Canadian cropland, where it may be of comparable magnitude to growing season emissions. In contrast, the growing season peak in N2O dominates the FT peak in the Midwestern Corn‐Soybean region of the United States.

Characteristics of near‐surface soil freeze–thaw status using high resolution CLM5.0 simulations on the Tibetan Plateau

AbstractSoil freeze–thaw alternation is a natural characteristic of the Tibetan Plateau (TP), and plays an important role in surface energy balance and eco‐hydrological processes. The soil freeze–thaw process on the TP has changed significantly owing to global warming, affecting the alpine ecosystem structure and function. This study used high‐resolution atmospheric forcing datasets to drive the Community Land Model version 5.0 (CLM5.0) to simulate the near‐surface soil freeze–thaw status between 1979 and 2020. The simulated results were compared with in situ observations, and then the spatiotemporal distribution of the freeze start‐date (FSD), freeze end‐date (FED), freeze duration (FD), and thaw duration (TD) at a depth of 0.1 m were analyzed. The Nash–Sutcliffe efficiency coefficients (NSEs) of FSD, FED, FD, and TD between simulations and in situ observations were 0.77, 0.90, 0.98 and 0.92, and the correlation coefficients of FSD, FED, FD, TD were 0.97, 0.99, 0.99 and 0.98, respectively. The spatial distribution of FSD and TD was characterized by gradually increasing from northwest to southeast while FED and FD exhibited the opposite characteristics. FSD, FED, FD, and TD changed at an area‐mean rate of 1.1, −1.4, −2.5, and 2.5 days decade−1, respectively. This study provides an important reference for analyzing and predicting the changes in near surface soil freeze–thaw status on the TP under the warming climate.

A Hybrid Compact Polarimetry GNSS-R Analysis of the Earth’s Cryosphere

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

Reconstruct SMAP brightness temperature scanning gaps over Qinghai-Tibet Plateau

• SMAP brightness temperature ( TB ) scanning gaps were compensated. • Daily variation of TB was analyzed in function of influencing factors. • Seamless TB benefits the refinements of SMAP soil moisture products. Soil Moisture Active Passive (SMAP) satellite was used to monitor global soil moisture and freeze–thaw state using surface brightness temperature (TB). However, due to the limitation of scanning width and shifting track, the daily TB observation had stripe gaps in varying degrees, which imped scientific applications of SMAP official product. To solve this issue, we proposed a temporal fitting algorithm to produce daily seamless TB data over Qinghai-Tibet Plateau (QTP) from 2015 to 2021. This method was composed of two steps: (1) overall time trend fitting which used a spline function to fit the overall mean time trend for each pixel from 2015 to 2021; (2) daily variation correction which accounted for several influential factors including surface temperature, precipitation, and vegetation to establish an Ordinary Least Squares (OLS) model for predicting daily variation of TB in numerous time slices. Consequently, the missing TB observation was reconstructed by combining the overall temporal trend and daily variation. A 10-fold cross-validation and product comparison were carried out to validate the robustness of the proposed method. For cross-validation, we randomly removed available SMAP observations in certain periods as unavailable, and then compared the reconstructed values with the removed practical ones. The cross-validation results indicated the reconstructed TB agreed well with actual SMAP TB, with a high overall coefficient of determination (R 2 = 0.94) and low RMSE (RMSE = 3.7 K), implying the desirable performance of our proposed method. By comparison with Soil Moisture and Ocean Salinity (SMOS) TB, the reconstructed data had an approximate performance with actual SMAP TB. Furthermore, the effects of surface temperature and precipitation on TB in different periods (frozen and unfrozen) over QTP were also analyzed. Our method has the potential to generate seamless SMAP products.

Responses of Soil Freeze–Thaw Processes to Climate on the Tibetan Plateau from 1980 to 2016

Soil freeze–thaw processes are remarkable features of the land surface across the Tibetan Plateau (TP). Soil moisture and temperature fluctuate during the freeze–thaw cycle, affecting the soil water and energy exchange between the land and atmosphere. This study investigates variations in the soil temperature, humidity, and freeze–thaw state and their responses to air temperature and precipitation on the TP from 1981 to 2016. Regional simulations of the TP using Community Land Model version 4.5 demonstrate that the climate of the TP has become warmer and wetter over the past 37 years, with increases in both regional average temperature and precipitation. Using empirical orthogonal function analysis and the Mann–Kendall trend test of air temperature, we show that 1980–1998 was relatively cold, and 1999–2016 was relatively warm. Soil temperature and moisture in most areas of the TP were affected by air temperature and precipitation, and both showed an upward trend during the past 37 years. Overall, from 1981 to 2016, the freezing date of the TP has become delayed, the thawing date has been hastened, and the duration of the freeze–thaw state has shortened. The surface soil freezes and thaws first, and these processes pervade deeper soil with the passage of time; freeze–thaw processes have an obvious hysteresis. Precipitation and air temperature had marked effects on the freeze–thaw processes. Higher air temperatures delay the freezing date, hasten the thawing date, and shorten the freeze–thaw period. Areas with the highest precipitation saw late soil freeze, early thaw, and the shortest freeze–thaw duration. Areas with less vegetation froze earlier and thawed later. The freeze–thaw duration increased in the northwest of the plateau and decreased on the rest of the plateau. This article informs research on frozen soil change in the context of global warming.

Impact of profile-averaged soil ice fraction on passive microwave brightness temperature Diurnal Amplitude Variations (DAV) at L-band

The dynamic change of frozen soil is crucial to land-surface modeling, carbon feedback studies, ground engineering (e.g., constructions), and microwave remote sensing. L-Band satellite missions Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) are currently exploited to characterize soil into freeze or thaw (FT) states. However, brightness temperatures (TB) at L-band contain more information besides the FT state, particularly over permafrost or seasonally frozen soil, which has not been explored via current retrieval algorithms. To examine the potential for L-band TB observations, we define an index called Profile-Averaged Frozen Soil Fraction (Ff) related to Diurnal Amplitude Variation (DAV) of TB (i.e., ΔTB) based on the optical depth of the frozen soil column. We evaluated Ff inferred from the 0th-order microwave transfer model with the SMAP L1c product, the ground-based European Space Agency L-Band Radiometer III (ELBARA-III) TB observations, and temperature profiles collected at the Maqu station in northeastern Tibet. While there is a clear relationship between Ff and ΔTB, no apparent link exists with the ice content fraction (Ffi) within a fixed-depth soil column. The proposed model certifies that the profile-averaged soil ice content Ff relates to the dynamic microwave penetration depth by math and field measurement. The model reproduces well ΔTB in Period Freezing but has problems in Period Thawing when melted surface water obstruct the microwave signals. Our findings can be used to exploit ΔTB between 6 am and 6 pm, as a typically overpassing time by the SMOS and SMAP satellites, for estimating Ff, which can be further applied in weather/climate forecasting and for improving land-surface modeling.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

Freeze/thaw onset detection combining SMAP and ASCAT data over Alaska: A machine learning approach

In the context of global warming, permafrost degrades gradually. To cope with the instability of the cryosphere, it is very important to strengthen the monitoring of the seasonal freeze–thaw cycle. At present, active and passive microwave remote sensing data are widely used in freeze/thaw (F/T) onset detection. There is some potential to improve accuracy through the combination of active and passive microwave data. Compared with the traditional method for combination, the machine learning algorithm has a stronger nonlinear expression ability. Therefore, it is advisable to use machine learning to combine multi-source data for freeze/thaw onset detection. In this study, the temporal change detection method is applied to SMAP data and ASCAT data respectively for preliminary detection. Then the Random Forest algorithm (RF) is used to combine the preliminary results of active and passive microwave data with site observation to estimate the freeze/thaw onsets more accurately. The method is validated with data obtained in Alaska from 2015 to 2019. The accuracy evaluation shows that the proposed method can effectively improve the accuracy of freeze/thaw onset detection. The predicted distribution of the freeze/thaw cycle indicates that the variation of the freeze–thaw cycle is closely related to latitude. In general, the proposed method based on machine learning is promising in the research of freeze–thaw state monitoring.

Simulations and Analysis of GNSS Multipath Observables for Frozen and Thawed Soil under Complex Surface Conditions

The transition of the freeze–thaw state of the land surface soil occurs every year with the season and is closely related to the human living environment. The freezing and thawing changes of the ground surface have important effects on hydrological activities, meteorological conditions, and ecological gas dynamics. Traditional monitoring methods have their limitations. In the past two decades, the emerging GNSS-R/IR (Global Navigation Satellite System-Reflectometry/Interference Reflectometry) technology has provided a new method for monitoring the surface f state; however, fewer works have paid attention to the scattering mechanism models in the current study. In this paper, a forward GNSS multipath model suitable for a complex cold surface is developed. The dielectric constant model with different surface parameters is added. The calculation of snow layer attenuation is employed to take the snow cover into consideration. Based on the first-order radiation transfer equation model, a polarization synthesis method is used to obtain the circularly and linearly polarized vegetation specular scattering characteristics. The surface characteristics and antenna model are coupled. A more detailed forward GNSS multipath model of frozen and thawed soil under complex surface conditions is established. The model is used to simulate and analyze the forward GNSS multipath (Signal to Noise Ratio (SNR), phase and pseudorange) responses of frozen and thawed soil under complex surface conditions (soil salinity, snow and vegetation coverage). Studies have shown that when the soil changes from freezing to thawing due to the change in the phase of the water in the soil, the dielectric constant and BRCS (bi-static radar cross-section) increase, causing the increase in the amplitude of the multipath observation. The higher the salinity content, the larger the amplitude of the multipath observation. The attenuation of the snow cover and the vegetation layer will lead to the reduction of the multipath observation amplitude. For the first time, the model developed by this paper reveals the GNSS multipath observation response of frozen and thawed soil under complex surface conditions in detail, which can provide some theoretical support for subsequent experimental design and data analysis.

On the freeze–thaw cycles of shallow soil and connections with environmental factors over the Tibetan Plateau

Changes in the freeze–thaw cycles of shallow soil have important consequences for surface and subsurface hydrology, land–atmosphere energy and moisture interaction, carbon exchange, and ecosystem diversity and productivity. This work examines the shallow soil freeze–thaw cycle on the Tibetan Plateau (TP) using in–situ soil temperature observations in 0–20 cm soil layer during July 1982–June 2017. The domain and layer averaged beginning frozen day is November 18 and delays by 2.2 days per decade; the ending frozen day is March 9 and advances by 3.2 days per decade; the number of frozen days is 109 and shortens by 5.2 days per decade. Altitude and latitude combined could explain the spatial patterns of annual mean freeze–thaw status well. Stations located near 0 °C contour line experienced dramatic changes in freeze–thaw cycles as seen from subtropical mountain coniferous forest in the southern TP. Soil completely freezes from surface to 20-cm depth in 15 days while completely thaws in 10 days on average. Near-surface soil displays more pronounced changes than deeper soil. Surface air temperature strongly influences the shallow soil freeze–thaw status but snow exerts limited effects. Different thresholds in freeze–thaw status definition lead to differences in the shallow soil freeze–thaw status and multiple-consecutive-day approach appears to be more robust and reliable. Gridded soil temperature products could resolve the spatial pattern of the observed shallow soil freeze–thaw status to some extent but further improvement is needed.

Volumetric behaviour of clays under freeze–thaw cycles in a mesoscopically uniform element

The volume change behaviour of fine-grained soils under repeated freeze–thaw cycles was investigated in context of artificial ground freezing, in which relatively quick freezing is achieved under confinement. Laboratory cyclic freeze–thaw tests with large freezing rates were performed on two clays in two different laboratory set-ups, either with isotropic pressure or with one-dimensional configuration. After freeze–thaw cycles, soils appear to reach residual states unique to each stress level, and the freeze–thaw residual line (FTRL) is defined to represent them. The current soil state's distance to the FTRL, defined as the freeze–thaw state parameter, is well correlated to subsequent volume changes. The one-dimensional freezing set-up provides a correlation more representative of the net effect of freeze–thaw, as all-around freezing under isotropic pressure subjects the specimen to unnatural shear straining. The different nature of freeze–thaw-induced volume changes was observed for high-plasticity Kasaoka clay and low-plasticity kaolin, as characterised by different configurations of the FTRL to the normal compression line. X-ray computed tomography confirmed largely uniform deformation in most of the adopted test conditions, while lower stress and slower freezing led to partial vertical cracking. This type of non-uniformity is shown not to be a main cause of the observed overall freeze–thaw-induced volume changes.

A novel method to monitor soft soil strength development in artificial ground freezing projects based on electromechanical impedance technique: Theoretical modeling and experimental validation

The artificial ground freezing is an important technique for soft soil reinforcement and underground water sealing carried out by continuously refrigerating ground. It is of great significance to monitor the soil strength development in artificial ground freezing projects not only for better evaluation of the soil freeze–thaw status but also for predicting and controlling the concurrent adverse effects which may cause serious engineering accidents. In this study, the electromechanical impedance method was explored in monitoring the soil strength development in the freeze–thaw process. The lead zirconate titanate transducer was embedded inside the soil specimen, and changes in the conductance signatures were monitored throughout the soil freeze–thaw process. The experimental results indicate that the resonant frequency of the embedded lead zirconate titanate transducer can serve as a reliable index for assessment of the soil’s dynamic elastic modulus in the freeze–thaw process. More importantly, an analytical model was developed based on the piezo-elasticity theory to characterize the correlation between the electromechanical impedance of the lead zirconate titanate transducer and the soil’s mechanical properties, and its validity was further confirmed by the experimental research. Based on the proposed model, the development of the soil’s strength can be well predicted from the measured conductance signatures. As a nondestructive testing method, the proposed soil testing technique will help save considerable time and resources by avoiding the conventional sampling, specimen preparation, and testing of soil. The theoretical and experimental research will contribute to the future application of the electromechanical impedance method in real-life artificial ground freezing engineering projects.

Potential Use of NASA’s Soil Moisture Active Passive Freeze–Thaw Tool to Assist in Minnesota Seasonal Load Restriction Timing

The U.S. encompasses over 4 million roadway miles, with about half of them located in seasonal frost areas. Roads are especially susceptible to damage when the subsurface is saturated with water (i.e., spring thaw). Spring load restrictions (SLR) are important for maintaining the integrity of roads. Routine determinations of road freeze–thaw (FT) state are limited to vertically embedded temperature data probes (TDP). Although TDPs are valuable to departments of transportation for determining SLRs, TDPs only represent individual points within the road network and are costly to install and maintain. Recent updates to spaceborne technology and algorithms made physically based retrievals of FT conditions possible at improved accuracy and temporal resolution (every 3 days) and have potential use for assisting with SLRs. Although instruments such as NASA’s Soil Moisture Active Passive (SMAP) platform cannot resolve individual roads, past comparisons have shown good correspondence with TDPs. The main objective of this study is to provide information on the potential value of NASA’s SMAP FT tool to supplement other methods for making seasonal load restriction decisions. Results are compared against data and protocols by the Minnesota Department of Transportation (MnDOT) at 10 sites in Minnesota, over four winter seasons (2016–2019). Results show that even when a simple criterion is used—the date of the third consecutive thaw from the SMAP afternoon retrieval—those dates typically fell within a week of MnDOT road postings (61% of the time). In addition, SMAP FT typically matches TDP FT states throughout the year (79% of the time).

Hydrological impacts of interannual variations in surface soil freezing processes in the upper Nu–Salween River basin

ABSTRACT The upper Nu–Salween River basin in the Tibetan Plateau is mainly covered with seasonal frozen soils. We used daily surface freeze–thaw states, detected from Special Sensor Microwave/Imager (SSM/I) daily brightness temperature data, to analyze the variations in surface freeze–thaw states and the relationship with air temperature. We also examined baseflow to explore the influences of interannual variations in the start time of soil freezing on hydrological processes. The results showed that (1) interannual air temperature fluctuations led to differences in the area and start time of surface freezing. When surface soil froze, flow was mainly dependent on existing groundwater storage. (2) The interannual variation in the surface freezing time directly affected the flow generation processes. When soil water froze and remained in the frozen layer, it was hard to generate surface flow, so flow mainly consisted of baseflow, causing the proportion of the baseflow in the total flow to gradually increase. (3) The surface freeze–thaw states obtained from the passive microwave remote sensing data may be applied to support further research on the hydrological impacts of freeze–thaw cycle variations in plateau mountain basins.