Accuracy Of Depth Measurements Research Articles

A binary coding method for half-period sinusoidal stripes in focused projection

Abstract During sinusoidal streak projection measurements, the nonlinear responses inherent in projectors and cameras can significantly alter the characteristics of projection and imaging. Traditional techniques require precise out-of-focus levels to achieve accurate depth measurements, which inherently limit the depth range that can be effectively measured. Addressing this limitation, this paper introduces an innovative binarization coding method for half-period sinusoidal stripes in focused projection. Our approach begins with a binarization process that encodes the half-period sinusoidal stripes. We then capture these stripes as they are modulated by the height of the object under test. The subsequent synthesis of the half-period sinusoidal stripes is achieved through a method of superposition calculation. To enhance the experimental procedure, we employ a four-step phase-shifting technique, complemented by an assisted phase unfolding using the gray code method. For the validation of our method, a standard ball with a diameter of 50.8140 mm was measured. The results are compelling, demonstrating a root mean square error of merely 0.0285 mm. Comparative experimental results prove that the proposed method can effectively avoid the influence of nonlinearity, and also obtain high-precision reconstruction results for discontinuous scenes.

Calibration of Marine Pressure Sensors with a Combination of Temperature and Pressure: A Case Study of SBE 37-SM

Accurate pressure measurement is crucial for understanding ocean dynamics in marine research. However, pressure sensors based on strain measurement principles are significantly affected by temperature variations, impacting the accuracy of depth measurements. This study investigates the SBE37-SM sensor and presents an improved calibration method based on a constant-pressure, variable-temperature scheme that effectively addresses temperature-induced deviations in pressure measurement. Experiments were conducted across a pressure range of 2000 dbar to 6000 dbar and a temperature range of 2 °C to 35 °C, establishing a comprehensive pressure–temperature calibration grid. The results show that, at a pressure of 6000 dbar, temperature-induced variations in readings for brand new SBE37-SM sensors can reach up to 9 dbar, while, for used sensors, they exceed 12 dbar, following a U-shaped trend. After applying a polynomial regression model for calibration, these variations were reduced to within ±0.5 dbar, significantly reducing the measurement uncertainty of the sensors in complex marine environments. This method underscores the necessity of further optimizing the CTD system’s temperature compensation mechanism during calibration and highlights the importance of regular calibration to minimize measurement uncertainty.

Optimization and application of renal depth measurement method in the cadmium-zinc-telluride‑based SPECT/CT renal dynamic imaging

PurposeThis study aims to evaluate the accuracy of four kidney depth measurement methods—nuclear medicine tomography, nuclear medicine lateral scanning, ultrasound, and Tonnesen’s formula-based estimation—using CT measurements as the reference standard. Additionally, it investigates the feasibility of utilizing nuclear medicine tomography and lateral scanning for measuring kidney depth in 99mTc-DTPA renal dynamic imaging.MethodsHollow kidney phantoms mimicking the shape and volume of adult kidneys were 3D printed and filled with 99mTcO4− solution. These phantoms were then subjected to lateral scanning and nuclear medicine tomography using CZT (cadmium-zinc-telluride) SPECT/CT to determine the optimal post-processing method. Forty patients who underwent renal dynamic imaging were recruited for the study. Renal depths were derived from ultrasound, lateral imaging, nuclear medicine tomography, formula-based estimation, and CT measurements. The renal depths obtained through these four methods were for correlation with CT-measured renal depths. Additionally, the absolute differences between renal depths obtained by each method and the CT standard were analyzed and compared across groups.ResultsUsing kidney phantoms, nuclear medicine tomography images were processed with a Butterworth filter (cutoff frequency = 0.6), and renal outlines in lateral images was manually delineated. In the clinical validation phase, correlation coefficients indicated strong associations between renal depths measured by nuclear medicine tomography (left kidney: R = 0.885, P < 0.05; right kidney: R = 0.927, P < 0.05) and lateral scanning (left kidney: R = 0.933, P < 0.05; right kidney: R = 0.956, P < 0.05) compared to CT measurements. The difference in kidney depth between nuclear medicine tomography and CT measurements were the smallest and statistically significant (left kidney: 0.69 ± 0.51; right kidney: 0.58 ± 0.41, P < 0.05).ConclusionUsing ordered subset expectation maximization (OSEM) in conjunction with a Butterworth filter (fc = 0.6) as the post-processing method, nuclear medicine tomography enables more accurate renal depth measurements without increasing the radiation dose to patients.

A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets

Abstract. Water depths of supraglacial lakes on the ice sheets are difficult to monitor continuously due the lakes' ephemeral nature and inaccessible locations. Supraglacial lakes have been linked to ice shelf collapse in Antarctica and accelerated flow of grounded ice in Greenland. However, the impact of supraglacial lakes on ice dynamics has not been quantified accurately enough to predict their contribution to future mass loss and sea level rise. This is largely because ice-sheet-wide assessments of meltwater volumes rely on models that are poorly constrained due to a lack of accurate depth measurements. Various recent case studies have demonstrated that accurate supraglacial lake depths can be obtained from NASA's Ice, Cloud and land Elevation Satellite (ICESat-2) ATL03 photon-level data product. ATL03 comprises hundreds of terabytes of unstructured point cloud data, which has made it challenging to use this bathymetric capability at scale. Here, we present two new algorithms – Flat Lake and Underlying Ice Detection (FLUID) and Surface Removal and Robust Fit (SuRRF) – which together provide a fully automated and scalable method for lake detection and along-track depth determination from ATL03 data and establish a framework for its large-scale implementation using distributed high-throughput computing. We report FLUID–SuRRF algorithm performance over two regions known to have significant surface melt – central West Greenland and the Amery Ice Shelf catchment in East Antarctica – during two melt seasons. FLUID–SuRRF reveals a total of 1249 ICESat-2 lake segments up to 25 m deep, with more water during higher-melt years. In the absence of ground-truth data, manual annotation of test data suggests that our method reliably detects melt lakes along ICESat-2's ground tracks whenever the lake bed is visible or partially visible and estimates water depths with a mean absolute error &lt;0.27 m. These results imply that our proposed framework has the potential to generate a comprehensive data product of accurate meltwater depths across both ice sheets.

Coastline Bathymetry Retrieval Based on the Combination of LiDAR and Remote Sensing Camera

This paper presents a Compact Integrated Water–Land Survey System (CIWS), which combines a remote sensing camera and a LiDAR module, and proposes an innovative underwater topography retrieval technique based on this system. This technique utilizes high-precision water depth points obtained from LiDAR measurements as control points, and integrating them with the grayscale values from aerial photogrammetry images to construct a bathymetry retrieval model. This model can achieve large-scale bathymetric retrieval in shallow waters. Calibration of the UAV-mounted LiDAR system was conducted using laboratory and Dongjiang Bay marine calibration fields, with the results showing a laser depth measurement accuracy of up to 10 cm. Experimental tests near Miaowan Island demonstrated the generation of high-precision 3D seabed topographic maps for the South China Sea area using LiDAR depth data and remote sensing images. The study validates the feasibility and accuracy of this integrated scanning method for producing detailed 3D seabed topography models.

Methodology for Performing Bathymetric and Photogrammetric Measurements Using UAV and USV Vehicles in the Coastal Zone

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth.

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Enhancing Quality Management of PHC Piles through Improved Driving Formulas: A Comprehensive Review and Analysis

In construction projects, conducting dynamic load tests on all piles proves impractical. Selective testing estimates bearing capacity, while the remaining piles rely on penetration depth for management. This approach, however, faces reliability issues due to varying conditions among piles. Technological advancements, such as non-contact hammers and sensors, have enhanced the accuracy of penetration depth measurements during final driving. Nonetheless, relying solely on penetration depth for construction and quality management remains problematic. This study, therefore, focuses on enhancing the use of driving formulas to improve pile quality management, particularly for the widely used pre-stressed high-strength concrete (PHC) piles. To improve pile quality management, existing driving formulas underwent review and refinement. Utilizing 258 dynamic load test data from various sites, the Hiley, Gates, and Danish formulas underwent validation through statistical analysis and graphical comparison. Enhancements to the Gates formula, achieved through curve fitting with actual data and the application of segment-based coefficients, demonstrated increased accuracy in bearing capacity estimation. These improvements offer a more reliable approach to pile quality management in construction projects.

DETERMINING SHALLOW WATER BATHYMETRY BY STEREO PHOTOGRAMMETRY TECHNIQUE USING WORLDVIEW-2 IMAGERY

Bathymetry in shallow-water areas plays an important role in marine environment management, marine economic resource development, and national defense and security. The echo sounding method is currently highly accurate; however, it has to deal with a variety of challenges as a result of the high cost, terrain influence, and meteorological conditions at sea. As remote sensing technology progresses, estimating bathymetry using satellite imagery will help lower measuring costs and expand monitoring coverage, particularly in shallow water, offshore areas, and difficult-to-access areas. In this article, the authors present a technique for determining bathymetry in shallow water regions by utilizing WorldView-2 stereoscopic images. The accuracy of stereoscopic depth measurement using WorldView-2 images is assessed by field depth measurement using the single-beam echo sounder. The study area is Nam Yet Island. The coefficient of determination (R2) between the two datasets (field measurement data and using satellite images) is 0.9. The stereoscopic satellite image method of depth measurement is highly accurate for regions with depths below 5 meters. The accuracy of the stereoscopic measurement decreases as the depth increases by more than 5 meters.

Optimising collection geometry for long‐range synthetic aperture sonar interferometry

AbstractInterferometric synthetic aperture sonar (SAS) is a technique to image and map the seabed in very high resolution. For large area coverage rate systems with hundreds of metres swath width, the achievable performance varies significantly over the swath. The performance is a function of system, collection geometry, and seabed type. A model is suggested to optimise the collection geometry for maximising area coverage rate given certain optimisation criteria, such as observation geometry, signal‐to‐noise ratio, depth measurement accuracy, and coverage within swath. The model is fitted to measurements (or calibrated) through a simple procedure. Specifically, the effect of vehicle altitude during the interferometric SAS data collection is studied. A novel model on data collected by a HUGIN Superior autonomous underwater vehicle carrying a HISAS 1032 Dual Rx interferometric SAS is demonstrated. The authors show that optimising the collection geometry, and in this case specifically the vehicle altitude, significantly improves the overall performance.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

Quantitative Evaluation of the Batwing Effect on Depth Measurement in Coherence Scanning Interferometry for Rectangular Gratings

Coherence scanning interferometry (CSI) is a widely used non-contact method for measuring areal surface topography. The calculation of groove depth typically employs the ‘W/3 rule’ specified in ISO 5436-1. However, the batwing effect causes overshoots near the groove edges, which can introduce noise singularities in the selected W/3 region for depth calculation, thereby affecting the measurement of rectangular grating depth. This paper introduces the definition of batwing height and width and proposes a simulation model that considers various factors, such as the center wavelength, spectrum of the illuminating light, shadow effect, and numerical aperture, to analyze their influence on depth measurement. The simulation results demonstrate that the batwing width is the primary factor influencing depth measurement for small grating periods. Specifically, a decrease in numerical aperture increases the batwing width, leading to larger depth measurement errors, while a decrease in the center wavelength reduces the batwing width, resulting in smaller depth measurement errors. The influence of the illumination spectrum and shadow effect on depth measurement is found to be minor. Experimental validation using a step standard consisting of rectangular gratings with different periods and depths confirms the agreement between the experimental and simulated results. The proposed method provides a quantitative evaluation of depth measurement accuracy in CSI.

A Survey of Monocular Depth Estimation based on Deep Learning

Depth information is very important for machines to perceive the environment and estimate their own state. Significant advances in robotics engineering and self-driving cars in recent decades have increased the demand for accurate depth measurements. Traditional depth estimation methods include motion structure and stereo vision matching, but these are based on the feature correspondence of multiple viewpoints, and at the same time, the predicted depth map is sparse. Depth estimation is a traditional task in computer vision that can be properly predicted by applying a variety of procedures, whereas inferring depth information from a single image is an ill-posed problem. The main objective of this paper is to provide a brief overview of the development of monocular depth estimation techniques based on deep learning. This article attempts to give an overview of supervised, unsupervised, and datasets and evaluation metrics. We conclude with a brief analysis of future developments.

Moving event detection from LiDAR point streams

In dynamic environments, robots require instantaneous detection of moving events with microseconds of latency. This task, known as moving event detection, is typically achieved using event cameras. While light detection and ranging (LiDAR) sensors are essential for robots due to their dense and accurate depth measurements, their use in event detection has not been thoroughly explored. Current approaches involve accumulating LiDAR points into frames and detecting object-level motions, resulting in a latency of tens to hundreds of milliseconds. We present a different approach called M-detector, which determines if a point is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors. Our experiments demonstrate the effectiveness of M-detector on various datasets and applications, showcasing its superior accuracy, computational efficiency, detection latency, and generalization ability.

A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy

PurposeSurface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers. The proposed SGRT application was tested at multiple institutions, and validated using phantoms and volunteers against various commercial systems to demonstrate feasibility. Methods and MaterialsAn iOS application was developed in Xcode and written in Swift using the Augmented-Reality (AR) Kit and implemented on an Apple iPhone 13 Pro with a built-in LiDAR camera. The application contains multiple features: 1) visualization of both the camera and depth video feeds (at a ∼60Hz sample-frequency), 2) region-of-interest (ROI) selection over the patient's anatomy where motion is measured, 3) chart displaying the average motion over time in the ROI, and 4) saving/exporting the motion traces and surface map over the ROI for further analysis. The iOS application was tested to evaluate depth measurement accuracy for: 1) different angled surfaces, 2) different field-of-views over different distances, and 3) similarity to a commercially available SGRT systems (Vision RT AlignRT® and Varian IDENTIFY™) with motion phantoms and healthy volunteers across three institutions. Measurements were analyzed using linear-regressions and Bland-Altman analysis. ResultsCompared to the clinical system measurements (reference), the iOS application showed excellent agreement for depth (r=1.000,p<0.0001; bias=-0.07±0.24cm) and angle (r=1.000,p<0.0001; bias=0.02±0.69°) measurements. For free-breathing traces, the iOS application was significantly correlated to phantom motion (institute 1: r=0.99,p<0.0001; bias=-0.003±0.03cm; institute 2: r=0.98,p<0.0001; bias=-0.001±0.10cm; institute 3: r=0.97,p<0.0001; bias=0.04±0.06cm) and healthy volunteer motion (institute 1: r=0.98,p<0.0001; bias=-0.008±0.06cm; institute 2: r=0.99,p<0.0001; bias=-0.007±0.12cm; institute 3: r=0.99,p<0.0001; bias=-0.001±0.04cm). ConclusionThe proposed approach using a smartphone-based application provides a low-cost platform that could improve access to surface-guided radiation therapy accounting for motion.

Polarization structured light 3D depth image sensor for scenes with reflective surfaces

Highly reflective surfaces are notorious in the field of depth sensing and three-dimensional (3D) imaging because they can cause severe errors in perception of the depth. Despite recent progress in addressing this challenge, there are still no robust and error-free solutions. Here, we devise a polarization structured light 3D sensor for solving these problems, in which high-contrast-grating (HCG) vertical-cavity surface-emitting lasers (VCSELs) are used to exploit the polarization property. We demonstrate accurate depth measurements of the reflective surfaces and objects behind them in various imaging situations. In addition, the absolute error and effective measurement range are measured to prove the applicability for a wide range of 3D applications. Our work innovatively combines polarization and depth information, opening the way for fully understanding and applying polarization properties in the 3D domain.

Advanced Ultrasonic Inspection of Thick-Section Composite Structures for In-Field Asset Maintenance.

An investigation into the inspection capabilities of in-field advanced ultrasound detection for use on ultra-thick (20 to 100 mm) glass fibre-reinforced polyester composites is presented. Plates were manufactured using custom moulding techniques, such that delamination flaws were created at calibrated depths. The full matrix capture technique with an on-board total focussing method was used to detect flaws scanned by a 0.5 MHz linear array probe. Flaw through-thickness dimensions were altered to assess the threshold for crack face separation at which delaminations could be identified. Furthermore, part thickness and in-plane flaw dimensions were varied to identify the inspection capability limitations of advanced ultrasonics for thick composites. The results presented in this study demonstrate an inverse relationship between the ability to find delaminations and plate thicknesses, with inspections successful at depths up to 74 mm. When the delamination thickness exhibited surface-to-surface contact, the inspection capability was reduced to 35 mm. There was an exponential decay relationship between the accuracy of the flaw depth measurement and plate thickness, likely due to the necessity of low probe frequencies. The effective inspection depth was determined to be in the range of 1 to 20 times the wavelength. It is speculated that the accuracy of measurements could be improved using probes with novel coupling solutions, and detectors with optimised signal processing/filtration algorithms.

Validation Technique for Heavy Vehicle Simulator Rut Measurement with Florida Laser Rut Meter

Abstract The performance of a pavement system is typically measured in structural and functional terms. Agencies often prioritize minimizing rutting as it indicates the mixture and structural inadequacy and can lead to safety concerns such as hydroplaning. Therefore, accurate rut depth measurement is critical to pavement management as well as to assess the performance of materials designed to resist rutting. To that measure, Florida Department of Transportation (FDOT) uses two heavy vehicle simulators (HVSs) as part of an accelerated pavement testing (APT) program to advance pavement research and implement new pavement materials, designs, and construction methods. Each HVS device is equipped with an on-board laser profiling system for rut depth measurement. To ensure accurate and reproducible rut depth measurements, a laser-based reference device was developed and constructed by FDOT to verify rut depth measurements made by both HVS devices during APT research studies. This article describes the rut depth reference device, the verification process, and data analysis methods used to validate the accuracy of the HVS devices.

Accurate Measurement of Frozen Soil Depth Based on I-TDR

In this study, a new method for determining the depth of frozen soil, Impulse Response Time Domain Reflectometry, is discussed. This method uses the principle of impedance measurement and the law of time–frequency domain convolution to convert the frequency-domain reflection signal into a time-domain signal and accurately determines the soil freezing front by measuring the difference between the impedance of frozen soil and unfrozen soil. The advantage of this method is that it solves the problems of small bandwidth, long rising edge time, and large measurement errors in the traditional TDR method to effectively improve the measurement accuracy of the soil-freezing front. Under laboratory conditions, soils of different textures (sand, loess, black soil, and red soil) were selected for experimental determination, and the results showed that compared with the traditional TDR method, the RMSE of the I-TDR method was small, and the method was applicable under different soil texture conditions, which could provide a new method for monitoring frozen soil in cold areas. In addition, the application of this method has important guiding significance for improving the efficiency of winter irrigation water, especially for guiding agricultural production, farmland irrigation, drainage engineering construction, meteorological frozen soil monitoring, and other aspects in cold and arid areas.