Radar Data Research Articles

On the Multiscale Processes Leading to an Extreme Gust Wind Event in East China: Insights From Radar Wind Profiler Mesonet Observations

AbstractIn this study, a record‐breaking surface gust wind event of over 45 m s−1, which occurred in the coastal region of East China during the early evening hours of 30 April 2021, is examined. The dynamical characteristics of this event is explored by using a high‐resolution mesonet comprised of eight radar wind profilers (RWPs), surface observations, radar and satellite data. Observational analyses show the development of several cloud clusters ahead of the axis of a midlevel trough with pronounced baroclinicity, and the subsequent organization into a comma‐shaped squall system with a leading convective line over land and a trailing stratiform region moved offshore. The latter is embedded by a mesovortex with intense northerly rear inflows descending to the surface, accounting for the generation of the gusty winds. Results indicate the different roles of multi‐scale processes in accelerating the surface winds to extreme intensity. Specifically, the large‐scale baroclinic trough provides intense background rear inflows that are enhanced by the formation of the mesovortex, while moist downdrafts in the rear inflows account for the downward transport of horizontal momentum, leading to the generation of intense cold outflows and gusty winds close to the leading convective line. Despite the lack of sufficient observations for quantitative analysis, this study provides a qualitative analysis that offers valuable insights into the dynamics of extreme gusty winds. Moreover, the above results underscore the value of RWP mesonet observations in enhancing our understanding of extreme wind events and in improving the nowcasting and prediction efforts in the future.

Creating near real-time alerts of illegal gold mining in the Peruvian Amazon using Synthetic Aperture Radar

Abstract Peru’s Southeastern Amazon deforestation trends can be attributed to alluvial gold mining. Illegal mining occurring in forestry concessions, national parks, and the territories of Indigenous People Organizations is of particular concern. We present a methodology to create near real-time alerts of deforestation caused by alluvial gold mining. A time series of Sentinel-1 Synthetic Aperture Radar (SAR) data from February to December 2022 is created in Google Earth Engine (GEE) and assessed using Morton Canty’s Omnibus Q-test change detection algorithm. Resulting detections are validated with high-resolution optical imagery from Planet NICFI’s monthly basemaps and Planet Scope daily imagery. The alerts identify the location and timing of large areas (group pixels of <1 ha) of forest loss due to gold mining activities within buffer zones of indigenous territories and protected areas. The overall accuracy of the forest loss analysis conducted with this change detection method was 99.98%, based on an independent accuracy assessment. This effort has resulted in a public web platform that displays the location of near real time alerts, so Peruvian enforcement agencies can more effectively allocate resources and staff to addressing active illegal mining operations. These results demonstrate the applicability of open-source SAR data to monitor forest loss over areas where cloud cover is more persistent and to improve tools that deliver timely, critical information to decision-makers. Future applications of our method could expand this approach to other drivers of deforestation.

Learning Omni-Dimensional Spatio-Temporal Dependencies for Millimeter-Wave Radar Perception

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions.

Development of Four Component Scattering Power Decomposition Technique for Dual Polarization SAR Data

AbstractThe increasing availability of dual-polarimetric synthetic aperture radar (PolSAR) data has led to a significant rise in its applications over the past few decades. Model-based decompositions combined with polarimetric information extraction from PolSAR data play a crucial role in target identification and classification. In this context, the covariance matrix [C], composed of four independent parameters, was used as the input for dual-pol four-component scattering power decomposition (DP-4SD). A novel 4SD model was tested using dual polarimetric SAR data from the spaceborne ALOS-2/PALSAR-2, and its performance was evaluated against existing scattering power decomposition methods. The performance of the proposed 4SD model was assessed using dual-polarization data from the Haldwani Forest and San Francisco to evaluate its classification capabilities within a single class (forest) and across various land use and land cover classes in San Francisco. The overall classification accuracy achieved was 85.69% for the Haldwani forest and 93.66% for San Francisco, with fewer unclassified samples compared with the existing model. The 4SD model demonstrates superior classification accuracy and enhances the interpretation of polarimetric information, indicating its potential to significantly improve land-use and land-cover mapping using dual PolSAR data.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

RF sensing enabled tracking of human facial expressions using machine learning algorithms.

Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative emotions. Moreover, facial expressions provide insights into various aspects of mental activities, social connections, and physiological information. Currently, most facial expression detection systems rely on cameras and wearable devices. However, these methods have drawbacks, including privacy concerns, issues with poor lighting and line of sight blockage, difficulties in training with longer video sequences, computational complexities, and disruptions to daily routines. To address these challenges, this study proposes a novel and privacy-preserving human behavior recognition system that utilizes Frequency Modulated Continuous Wave (FMCW) radar combined with Machine Learning (ML) techniques for classifying facial expressions. Specifically, the study focuses on five common facial expressions: Happy, Sad, Fear, Surprise, and Neutral. The recorded data is obtained in the form of a Micro-Doppler signal, and state-of-the-art ML models such as Super Learner, Linear Discriminant Analysis, Random Forest, K-Nearest Neighbor, Long Short-Term Memory, and Logistic Regression are employed to extract relevant features. These extracted features from the radar data are then fed into ML models for classification. The results show a highly promising classification accuracy of 91%. The future applications of the proposed work will lead to advancements in technology, healthcare, security, and communication, thereby improving overall human well-being and societal functioning.

Sentinel-1 data reveals unprecedented reduction of open water extent due to 2023-2024 drought in the central Amazon basin

Abstract In 2023, an intense drought impacted the Amazon basin triggered by climate change and a strong El Niño event, with the Negro River reaching its lowest water level in 120 years. However, the spatiotemporal open water extent (OWE) during this drought remains unclear. This study comprehensively evaluates OWE variability in the central Amazon using Sentinel-1 synthetic aperture radar (SAR) data since 2017. Monthly OWE masks were generated through an empirical threshold classification with accuracy >95%. Overall, the central Amazon experienced a reduction of ∼8% in OWE in the 2023 dry season months (November and December) when compared to monthly-average. However, reductions of up to 80% in OWE were observed in several specific lakes. Our analysis underscores the unprecedented severity of the 2023/2024 drought on rivers and floodplains. Utilizing SAR remote sensing technologies, this study emphasizes the urgent need for proactive conservation measures to safeguard the Amazon’s ecological integrity amid escalating environmental challenges. Monthly water masks from January/2017 to September/2024 are available here: https://doi.org/10.5281/zenodo.12751783.

Throw distribution across the Dabbahu−Manda Hararo dike-induced fault array: Implications for rifting and faulting

Dike intrusion and formation of overlying dike-induced normal faults facilitate plate extension. The kinematics of these dike-induced normal faults provide an accessible record of subsurface diking. Here, we use high-resolution light detection and ranging (LiDAR) and interferometric synthetic aperture radar (InSAR) data to explore how strain was distributed across a preexisting dike-induced fault array during diking events in the Dabbahu−Manda Hararo magmatic segment (Afar, Ethiopia) in 2008 and 2010. By analyzing throw of the dike-induced normal faults, we show that only a small number of faults were reactivated during each diking event; the distribution of this reactivation likely reflects dike depth, opening, and inclination, as well as fault orientation. We also show fault throw favorably accrued toward fault centers, away from areas of soft- or hard-linkage. Our high-resolution data sets demonstrate the importance of reactivation to rifting, as it means extension can occur at lower extensional forces, and that fault slip (and seismic hazard) may not localize at sites of fault linkage.

Machine learning-based crop type detection and vegetation mon-itoring using Sentinel-1 SAR and Landsat 8

This study explores the use of machine learning for crop type detection and vegetation monitoring in arid and semi-arid regions, specifically in Biskra and Khenchela, Algeria. Traditional field-based agricultural monitoring is labor-intensive and limited in spatial coverage, prompting the need for more efficient and scalable methods. The research integrates Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Landsat 8 to enhance the classification of different crop types and track vegetation dynamics. Key metrics such as radar backscatter and Normalized Difference Vegetation Index (NDVI) are employed to distinguish between irrigated and rainfed crops. The Random Forest algorithm is utilized for classification, with training data derived from field surveys, high-resolution satellite imagery, and existing land cover maps. The Google Earth Engine (GEE) platform facilitates large-scale data processing, providing real-time agricultural analysis. Over the 12-month study period (January to December 2020), the integrated approach demonstrated high accuracy, achieving over 85% precision in classifying key crops such as date palms, cereals, and vegetables. Results indicate that SAR and NDVI data provide complementary insights into vegetation health, phenology, and agricultural practices in different climatic zones. This methodology offers a promising solution for improving agricultural monitoring and resource management in arid and semi-arid regions, enhancing food security and optimizing water usage. The study’s findings also underscore the value of satellite-based remote sensing for large-scale, real-time agricultural analysis.

High-resolution fully-polarimetric synthetic aperture radar dataset

Abstract Objectives Fully-polarimetric synthetic aperture radar (PolSAR) data contain a rich body of elementary scattering physics information that is critically valuable for a broad range of applications and scientific purposes. However, there is a lack of available high-resolution (< 0.3048-m) data available for PolSAR phenomenology research. This article introduces a high-resolution PolSAR data set collected and provided by Sandia National Laboratories (SNL). The data sets were collected to support studying high-resolution scattering physics from different types of clutter and applications such as polarimetric-based terrain classification. Data description The data set presented consists of 39 200 m × 200 m complex-valued PolSAR image sets with 0.12-m (~ 4.7-inch) resolution and is the result of two different collection campaigns, one campaign in 2013 and the other in 2015. The data sets were collected with SNL’s airborne fully-polarimetric X-band (9.6 GHz center frequency) radar system and contains a variety of natural and cultural clutter environments with scene content ranging from agricultural fields to urbanized areas collected throughout the greater Albuquerque, New Mexico, U.S.A. region.

Integration of Optical and Synthetic Aperture Radar Data with Different Synthetic Aperture Radar Image Processing Techniques and Development Stages to Improve Soybean Yield Prediction

Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH, and VV) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover.

Investigation of thunderstorm characteristics with severe lightning events over NE region of India

Abstract. The north eastern region (NER) of India is among the hotspot regions for thunderstorm events leading to substantial loss of lives and properties every year. A reliable thunderstorm early warning system can prevent these losses. As these events are highly dynamic, prediction becomes equally challenging. Diagnostic studies of such events could provide more understanding of lightning and storms over a region which could then be used as input for the prediction of such events. Improving the efficiency of the prediction of associated meteorological parameters could improve the prediction of thunderstorm. In this study, two thunderstorm events have been identified with high lightning flash counts, using observations from the INSAT-3D satellite and lightning detection sensors. These observations revealed that one of the events was caused by a cloud system that originated elsewhere and travelled over the area of impact while the other event was an isolated convective system that occurred over a small region. The characteristic features of thunderstorms, like vertical wind velocity, cloud ice water content, relative humidity, cloud fraction and radar reflectivity, etc. were simulated using the WRF model. These simulations were validated with the ERA-5 reanalysis data and observations from Radar and satellite data. Validation results suggest that the WRF model could predict these features associated with thunderstorm events quite reliably. The study helped to improve the severe weather warning over NER of India and the stakeholders are trained on effective utilization of such warning.

Mapping Rice Paddy Areas in Nueva Ecija Using Temporal Sentinel -1 SAR Imagery

Abstract. Remote Sensing is proven to be helpful in various ways, and for an agricultural country like the Philippines, mapping in farmlands is not that common. Using the Sentinel 1 Synthetic Aperture Radar (SAR) data, the study reveals rice paddy classification brought by supervised machine learning on the municipality of Guimba, in Nueva Ecija - the Rice Bowl Capital of the country. The study in Guimba, Nueva Ecija, addresses the absence of comprehensive rice classification studies, focusing on creating a classified rice paddy map and assessing spectral indices (NDMI, NDVI, NDWI) using synthetic aperture radar and optical imager. Results show successful differentiation between rice and non-rice paddies. Temporal analysis emphasizes monitoring water availability, soil moisture, and vegetation health. Despite signs of overprediction in the CART model, its effectiveness in mapping rice paddies is notable. The spatial distribution maps contribute to targeted monitoring, enabling efficient interventions and improved agricultural practices. This research highlights the inherent use of remote sensing in rice crop management, offering valuable insights for farmers and country's food security.

Exploring the Relationship Between Time Series of Sentinel-1 Interferometric Coherence Data and Wild Edible Mushroom Yields in Mediterranean Forests

Edible wild mushrooms constitute a valuable marketable non-wood forest product with high relevance worldwide. There is growing interest in developing tools for estimation of mushroom yields and to evaluate the effects that global change may have on them. Remote sensing is a powerful technology for characterization of forest structure and condition, both essential factors in triggering mushroom production, together with meteo-climatic factors. In this work, we explore options to apply synthetic aperture radar (SAR) data from C-band Sentinel-1 to characterize, at the plot level, wild mushroom productive forests in the Mediterranean region, which provide saprotroph and ectomycorrhizal mushrooms. Seventeen permanent plots with mushroom yield data collected weekly during the productive season are characterized with dense time series of Sentinel-1 backscatter intensity (VV and VH polarizations) and 6-day interval interferometric VV coherence during the 2018–2021 period. Weekly-regularized series of SAR data are decomposed with a LOESS approach into trend, seasonality, and remainder. Trends are explored with the Theil-Sen test, and periodicity is characterized by the Discrete Fast Fourier transform. Seasonal patterns of SAR time-series are described and related to mycorrhizal and saprotroph guilds separately. Our results indicate that time series of interferometric coherence show cyclic patterns which might be related with annual mushroom yields and may constitute an indicator of triggering factors in mushroom production, whereas backscatter intensity is strongly correlated with precipitation, making noisy signals without a clear interpretable pattern. Exploring the potential of remotely sensed data for prediction and quantification of mushroom yields contributes to improve our understanding of fungal biological cycles and opens new ways to develop tools that improve its sustainable, efficient, and effective management.

Visual and Radar Data Fusion: Complementary or Alternative Sensors in Intelligent Driving Systems

This article explores the role and advantages and disadvantages of visual and radar data fusion in intelligent driving systems. With the rapid development of intelligent driving technology, the comprehensive perception of the environment by car perception systems has become crucial, usually requiring the combination of low, medium, and high-precision sensors. Visual sensors perform excellently in acquiring high-resolution images and recognizing detailed information, while radar sensors have higher stability and penetration in harsh weather conditions. Due to the limitations of a single sensor in complex driving environments, data fusion has become a key strategy for improving the performance of perception systems. By comparing different perception schemes and application scenarios, this article analyzes the actual effects and potential advantages of visual and radar data fusion, revealing its important role in intelligent driving technology. Fusion technology can compensate for the shortcomings of various sensors, and improve the accuracy and reliability of environmental perception, but still faces challenges in data synchronization, algorithm selection, and real-time performance. This article aims to provide valuable insights for the development of future intelligent driving technology.

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Mathematical Modelling of Segmentation Synthetic Aperture Radar Data for Military Purposes

Mathematical Modelling of Segmentation Synthetic Aperture Radar Data for Military Purposes

Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Investigation of the Effect of Integrated Offset, GPS, and InSAR Data in the Stochastic Source Modeling of the 2002 Denali Earthquake

This study investigates the effect of geological field measurement (offset), global positioning system (GPS), and interferometric synthetic aperture radar (InSAR) data on the estimation of the co-seismic earthquake displacements of the 2002 Denali earthquake. The analysis is conducted using stochastic source modeling. Uncertainties associated with each dataset limit their effectiveness in source model selection and raise questions about the adequate number of datasets and their type for reliable source estimation. To address these questions, stochastic source models with heterogeneous earthquake slip distributions are synthesized using the von Kármán wavenumber spectrum and statistical scaling relationships. The surface displacements of the generated stochastic sources are obtained using the Okada method. The surface displacements are compared with the available datasets (i.e., offset, GPS, and InSAR) individually and in an integrated form. The results indicate that the performance of stochastic source generation can be significantly improved in the case of using GPS data and in the integrated case. Overall, based on the case study of the 2002 Denali earthquake, the combined use of all available datasets increases the robustness of the stochastic source modeling method in characterizing surface displacement. However, GPS data contribute more than InSAR and offset data in producing reliable source models.