Synthetic Aperture Radar Data Research Articles

Repeat pass imaging-based velocity estimation of Siachen glacier in J&K Himalaya using Sentinel-2 and Landsat-8 data: A comparison with SAR observations

In this study optical data from satellites Sentinel-2 (S-2), Landsat-8 (L-8), and synthetic aperture radar (SAR) data from Sentinel-1(S-1) have been used to estimate the surface velocity of the Siachen Glacier in J&K Himalaya. Repeat-pass optical and SAR data have been processed employing sub-pixel offset tracking (SOT) approach to decipher surface velocity of glaciers during 2013–2018. It is observed that SOT derived 2-D surface velocity using S-2 and L-8 data pairs show more than 70 % correlation over the entire stretch of the glacier. High correlation among velocities from S-2 and L-8 over two years validates one sensor against another for glacier surface movement studies in the Himalaya. This study reveals that the velocity of the glacier varies from ∼120 ma−1 to ∼151 ma−1 during 2013–2018. This study results the movement of glacier using optical sensor and SAR sensor over same geographical zone and similar time zone to evaluate the feature extraction and sensor impact on feature extraction. We have found that all sensors have produced similar results, since data is collected near time frame.

Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes

The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model’s prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.

Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.

CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.

In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Multi-wave characteristics associated with January 15, 2022 Hunga-Tonga volcanic eruption: A global observation

The eruption of Hunga-Tonga Volcano on January 15, 2022 has stimulated a wide spectrum of atmospheric waves globally. To probe the surface deformation pattern, Sentinel-1 Synthetic Aperture Radar (SAR) data has been analyzed. It has been approximated that an overall area of about 2.47 square kilometres experienced deformation in conjunction with this event. To characterize the atmospheric wave propagation, barometric pressure data from 1814 stations distributed all around the globe have been examined. This study encompassed with the propagation characteristics of the waves over four zones including Indian and Polar regions for the first time using barometric data. Time-series observations indicate that the waves propagated globally multiple times. Within the Indian region, three minor arc passages and one major arc passage were identified. In Japan, two minor arc passages and one major arc were present. Conversely, in North America, both minor and major arc passages were detected, occurring a minimum of three times. Moreover, the attributes of these waves, such as their propagation speed and periodicity, were compared across these four regions. The estimated phase speed and periodicity fall within the ranges of approximately 291–314 m/s and 10–180 min, respectively including Polar Regions. These speed and periodicity measurements of the observed waves suggest that the dominant mode of wave propagation generated during the Tonga volcanic eruption is that of Lamb waves. In addition, a slower propagation phase speed of about 226.6 m/s was identified in Japan which corresponds to Pekeris mode of waves.

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated.

A Localized Particle Filtering Approach to Advance Flood Frequency Estimation at Large Scale Using Satellite Synthetic Aperture Radar Image Collection and Hydrodynamic Modelling

This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale.

Mapping high spatial resolution ionospheric total electron content by integrating Time Series InSAR with International Reference Ionosphere model

Total electron content (TEC) is a key parameter for characterizing the ionosphere. Conventional TEC measurement methods have low spatial resolution, hindering accurate representation of ionospheric spatial characteristics. Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) have shown their potential for high-spatial-resolution TEC estimation. However, SAR-based absolute TEC estimation typically relies on full-polarimetric SAR images, while the InSAR-based method can only extract differential TEC. This study proposes a novel TEC mapping method integrating Time Series InSAR (TS-InSAR) with the International Reference Ionospheric (IRI) model and does not leverage full-polarimetric SAR data. The Advanced Land Observing Satellite-1 (ALOS-1) SAR images covering the Chile and Greenland regions were collected to test the proposed method. The TECs derived from the IRI model and International GNSS Service (IGS) and the electron density observed by the Incoherent Scattering Radar (ISR) site of SONDRESTROM were acquired to evaluate the performance and validate the accuracy of the proposed method, respectively. The results show that the proposed method can achieve TEC mapping with resolutions of tens of meters, much higher than the tens or hundreds of kilometers of the IRI and IGS TECs. The spatial features of our method-derived TECs show overall good consistency with those of the IRI and IGS TECs, but the described spatial information is more detailed. Also, compared to the original IRI model, the improvement rates of root-mean-square errors (RMSEs) between the estimated electron density and ISR observations exceeded 21.67% after the update using the TEC obtained from the proposed method. These experiments demonstrate the proposed method’s feasibility and reliability for ionospheric TEC mapping.

Towards assessing the synthetic-to-measured adversarial vulnerability of SAR ATR

Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with imperceptible but aggressive perturbations. This paper studies the synthetic-to-measured (S2M) transfer setting, where an attacker generates adversarial perturbation based solely on synthetic data and transfers it against victim models trained with measured data. Compared with the current measured-to-measured (M2M) transfer setting, our approach does not need direct access to the victim model or the measured SAR data. We also propose the transferability estimation attack (TEA) to uncover the adversarial risks in this more challenging and practical scenario. The TEA makes full use of the limited similarity between the synthetic and measured data pairs for blind estimation and optimization of S2M transferability, leading to feasible surrogate model enhancement without mastering the victim model and data. Comprehensive evaluations based on the publicly available synthetic and measured paired labeled experiment (SAMPLE) dataset demonstrate that the TEA outperforms state-of-the-art methods and can significantly enhance various attack algorithms in computer vision and remote sensing applications. Codes and data are available at https://github.com/scenarri/S2M-TEA.

Extracting winter wheat based on multi-feature optimization of short-time series synthetic aperture radar data with dual polarizations

Extracting winter wheat based on multi-feature optimization of short-time series synthetic aperture radar data with dual polarizations

Analyzing recent deformation in Wadi Hagul, Eastern Desert, Egypt, via advanced remote sensing and geodetic data processing

Abstract The Wadi Hagul region in the eastern desert of Egypt is facing seismic hazards and increased human activity. This study uses remote sensing and geodetic methods to monitor and analyze recent deformation in the area. Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1A satellite and Global Navigation Satellite System (GNSS) data were combined to track surface movements and deformations accurately. The study analyzed InSAR data from February 4, 2020, to February 07, 2024, and GNSS data from the Wadi Hagul geodetic network established in July 2022 and monitored until January 2024. Despite the relatively short GNSS monitoring period, it provided valuable insights into recent deformation trends. By integrating data from ten GNSS stations, including International Geodetic stations (IGS), and InSAR scenes from the Sentinel-1A mission, the study estimated recent ground deformation in the region. The main objectives were to analyze recent crustal movements by identifying spatial and temporal patterns of deformation and assess implications for geological processes. In Key Findings, horizontal movement fluctuates between 0.5 and 2.5 ± 0.1 mm annually across the geodetic network. The estimated velocity of the area was 1.5–2 ± 0.5 mm per year. Integrating GNSS and InSAR data helped calculate movement rates along fault lines and create a fault map. In conclusion, the results suggest that while current deformation rates are moderate, they could increase significantly due to human activity, leading to higher seismic activity and potential earthquakes. Limiting human activity in the region is advisable to prevent negative impacts on nearby populated areas.

Deep Learning-based DSM Generation from Dual-Aspect SAR Data

Abstract. Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study’s findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically.

Satellite Geodesy Unveils a Decade of Summit Subsidence at Ol Doinyo Lengai Volcano, Tanzania

AbstractThe processing of hundreds of Synthetic Aperture Radar (SAR) images acquired by two satellite systems: Sentinel‐1 and COSMO‐SkyMed reveals a decade of ground deformation for a ∼0.5 km diameter area around the summit crater of the only active carbonatitic volcano on Earth: Ol Doinyo Lengai in Tanzania. Further decomposing ascending and descending orbits when the appropriate SAR data sets overlap allow us to interpret the imaged deformation as ground subsidence with a significant rate of ∼3.6 cm/yr for the pixels located just north of the summit crater. Using geodetic modeling and inverting the highest spatial resolution COSMO‐SkyMed data set, we show that the mechanism explaining this subsidence is most likely a deflating very shallow (≤1 km depth below the summit crater at the 95% confidence level) magma reservoir, consistent with geochemical‐petrological and seismo‐acoustic studies.

The 2022 Har Lake earthquake sequence highlights a complex fault system in the western Qilian Shan, northeastern Tibetan Plateau

SUMMARY The 2022 Har Lake earthquake sequence, which began in 2022 January and lasted for ∼70 d, jolted the Har Lake area, which is located in the western Qilian Shan, northeastern Tibetan Plateau. Two Mw > 5.5 earthquakes occurred during the earthquake sequence, among which the March 25 Mw 5.8 event is considered the largest event recorded in the area. However, determining the seismogenic faults of the earthquake sequence, as well as the detailed rupture features, is difficult due to the lack of geological data and near-field seismological observations. In this study, we use Sentinel-1 synthetic aperture radar (SAR) data to obtain the coseismic deformation field, identify possible ruptured faults and associated fault geometries, and further estimate detailed coseismic slip models of the two Mw > 5.5 earthquakes. The results show that the January 23 Mw 5.6 earthquake (Earthquake A) occurred on a N15° W-trending dextral-slip fault with a dip angle of ∼61°. For the March 25 Mw 5.8 earthquake (Earthquake B), the interferometric synthetic aperture radar (InSAR) data can be described by either an ∼N–S-trending dextral-slip fault or an ∼E–W-trending sinistral-slip fault. The ∼N–S-trending fault better describes the aftershock distribution, while the ∼E–W-trending model is more consistent with the regional geological setting. We suggest that the complex coseismic ruptures in the multiple-fault system are driven by widespread NE–SW-trending compression in the western Qilian Shan. This study demonstrates the importance of integrating geodetic and seismological observations to capture the full complexity of moderate earthquakes and further suggests potential seismic hazards in the Har Lake area.

The 2013 Mw 6.2 Shonbeh (Kaki) earthquake (Zagros-Iran): seismo-tectonic implications for the Kazerun shear transition zone from high-resolution aftershock and InSAR data analysis

SUMMARY The aim of this study is to investigate the aftershock sequence data recorded by a dense temporary seismological network deployed in the epicentral area of the 2013 April 9 Shonbeh (Kaki) earthquake, located in the south of the Simply Folded Belt of the Zagros (Iran). For a comprehensive understanding, coseismic displacements of the Shonbeh earthquake have been investigated using Interferometric Synthetic Aperture Radar (InSAR) data. The epicentral distribution of high-resolution relocated aftershocks shows NW–SE and N–S trending of seismicity. The aftershocks are confined between ∼3 and ∼14 km depth, which implies that the rupture occurred mostly within the sedimentary cover beside the fault parameters retrieved from InSAR modelling. Projection of precisely located aftershocks on NE-oriented section and InSAR ground displacement data are consistent with both NW-trending NE- and SW-dipping fault segments. We observe a NNW–SSE right-lateral strike-slip motions that accommodate oblique convergence and differential motion between the North and Central Zagros. The spatial pattern and focal mechanisms of aftershocks are consistent with a distributed deformation between NW–SE trending reverse and N–S trending right-lateral strike-slip fault segments in the south of the Kazerun transition zone that accommodates a wide shear zone.

The ABoVE L-band and P-band airborne synthetic aperture radar surveys

Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

The utility of TerraSAR-X, TanDEM-X, and PAZ for studying global volcanic activity: Successes, challenges, and future prospects

TerraSAR-X (TSX), TanDEM-X (TDX), and PAZ Synthetic Aperture Radar data have been used at over 120 volcanoes to assess surface characteristics and change over time. We examine previous work, adding additional examples to understand where and when these data are most useful for volcanology. We focus on volcanoes as part of the Committee on Earth Observation Satellites (CEOS) Volcano Demonstrator Project. TSX/TDX/PAZ data provide a valuable means of detecting small surface changes from amplitude images and topographic changes from bistatic TSX/TDX data. For short temporal and perpendicular baselines, TDX/TSX/PAZ can also provide useful deformation data, even in presence of vegetation. No global background mission currently acquires TSX/TDX/PAZ data at volcanoes: 70 % high spatial resolution data, limiting their suitability for studying pre-eruptive unrest. Coordinated targeting by SAR constellations of priority volcanoes would provide data and insights valuable for forecasting eruptions and associated hazards.

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review.

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

A 3D imaging method for flexible array SAR based on a distributed POS

ABSTRACT The longer the baseline length of an airborne array synthetic aperture radar (SAR) is, the richer the ground object information can be resolved. The airborne array flexible SAR can overcome the limitation that the baseline length is limited by the belly length by attaching the antenna to the wing. However, fusing the airborne array flexible SAR distributed position and orientation system (POS) data under the wing and the belly is a major difficulty. To address this issue, the paper analyzes the inconsistency of airborne array flexible SAR data with a distributed POS and proposes a three-dimensional imaging method of airborne array flexible SAR based on distributed POS by fusing the Doppler centre, yaw angle and time of the observation data. By using distributed POS data, flexible data can obtain higher resolution than single-POS processing. The experiment of airborne array flexible SAR data proves the superiority of the proposed method.