Search Results (75)

Tropospheric delays can be the leading source of error in spaceborne interferometric synthetic aperture radar (InSAR) measurements. Here, we find that the non-uniform troposphere delay features are dependent on the squint angles used for repeat-pass InSAR data acquisitions. Large squint angles cause large along-track shifts in these non-uniform troposphere delay features. By processing the airborne L-band uninhabited aerial vehicle SAR (UAVSAR) data with three different squint angles, we were able to see various non-uniform delay structures of different sizes with varying delays of up to a few centimeters across the observed interferograms. We were also able to estimate the altitude of the effective troposphere delay layers. The understanding of the squint-dependent troposphere delay can help us separate the surface deformation component from the atmosphere delay component in the InSAR phase measurements. A number of methods are proposed for this separation. We used the UAVSAR data and simulated surface deformations to verify these methods. This technique can also be used for spaceborne cases. Full article

Accurate and timely 3D vegetation structure information is essential for ecological modeling and land management. However, these needs often cannot be met with existing airborne LiDAR surveys, whose broad-area coverage comes with trade-offs in point density and update frequency. To address these limitations, this study introduces a deep learning framework built on attention mechanisms, the fundamental building block of modern large language models. The framework upsamples sparse (<22 pt/m²) airborne LiDAR point clouds by fusing them with stacks of multi-temporal optical (NAIP) and L-band quad-polarized Synthetic Aperture Radar (UAVSAR) imagery. Utilizing a novel Local–Global Point Attention Block (LG-PAB), our model directly enhances 3D point-cloud density and accuracy in vegetated landscapes by learning structure directly from the point cloud itself. Results in fire-prone Southern California foothill and montane ecosystems demonstrate that fusing both optical and radar imagery reduces reconstruction error (measured by Chamfer distance) compared to using LiDAR alone or with a single image modality. Notably, the fused model substantially mitigates errors arising from vegetation changes over time, particularly in areas of canopy loss, thereby increasing the utility of historical LiDAR archives. This research presents a novel approach for direct 3D point-cloud enhancement, moving beyond traditional raster-based methods and offering a pathway to more accurate and up-to-date vegetation structure assessments. Full article

Search and rescue (SAR) operations with unmanned aerial vehicles (UAVs) have been the subject of numerous scientific studies. Their effectiveness relies on intelligent and efficient path planning. Not only can they save expensive resources, they can minimize potential risks for the rescue team. This paper deals with optimal path planning for automated UAV-SAR operations, tailored specifically to the challenging inter-tidal environment of the Wadden Sea. The aim is to minimize the search time and maximize the discovery probability of lost persons (LPs) with intelligent UAV path-planning strategies. To achieve this, first a dynamic probability map (PM) of the lost person’s possible location is generated. Two distinct methods are evaluated for this purpose: Monte Carlo simulations (MCSs), and a more efficient Markov chain (MAC) model. The PM then directly informs the UAV’s decision-making process. Then, several automated search strategies are systematically evaluated and compared in a comprehensive simulation study, from simple coverage patterns to advanced PM-driven algorithms. MAC-generated PMs prove to provide a fast and reliable foundation for time-critical applications such as SAR operations. Additionally, PM-based search strategies outperform standard search patterns, especially in larger search regions. Furthermore, the evaluation is extended to multi-UAV scenarios, showing in this case that an area-segmentation approach is most effective. The results validate and provide a considerable contribution for an efficient, time-critical framework for UAV deployment in complex, real-world SAR operations. Full article

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design. Full article

Limited labels and detailed changed land-cover interpretation requirements pose challenges for time-series PolSAR change monitoring research. Accurate labels and supervised models are difficult to reuse between massive unlabeled time-series PolSAR data due to the complex distribution shifts caused by different imaging parameters, scene changes, and random noises. Moreover, many related methods can only detect binary changes in PolSAR images and struggle to track the detailed land cover changes. In this study, an unsupervised cross-domain method based on limited-label transfer learning and a vision transformer (LLTL-ViT) is proposed for PolSAR land-cover change monitoring, which effectively alleviates the problem of difficult label reuse caused by domain shift in time-series SAR data, significantly improves the efficiency of label reuse, and provides a new paradigm for the transfer learning of time-series polarimetric SAR. Firstly, based on the polarimetric scattering characteristics and manifold-embedded distribution alignment transfer learning, LLTL-ViT transfers the limited labeled samples of source-domain PolSAR data to unlabeled target-domain PolSAR time-series for initial classification. Secondly, the accurate samples of target domains are further selected based on the initial transfer classification results, and the deep learning network ViT is applied to classify the time-series PolSAR images accurately. Thirdly, with the reliable secondary classification results of time-series PolSAR images, the detailed changes in land cover can be accurately tracked. Four groups of cross-domain change monitoring experiments were conducted on the Radarsat-2, Sentinel-1, and UAVSAR datasets, with about 10% labeled samples from the source-domain PolSAR. LLTL-ViT can reuse the samples between unlabeled target-domain time-series and leads to a change detection accuracy and specific land-cover change tracking accuracy of 85.22–96.36% and 72.18–88.06%, respectively. Full article

Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R² and RMSE values across models: KNN (R² of 0.496, RMSE of 5.13 m), RF (R² of 0.510, RMSE of 5.06 m), SVM (R² of 0.544, RMSE of 4.88 m), and XGB (R² of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies. Full article

The exchange of water, sediment, and nutrients in wetlands occurs through a complex network of channels and overbank flow. Although optical sensors can map channels at high resolution, they fail to identify narrow intermittent channels colonized by vegetation. Here we demonstrate an innovative application of rapid-repeat interferometric synthetic aperture radar (InSAR) to study hydrologic connectivity and tidal influences in Louisiana’s coastal wetlands, which can provide valuable insights into water flow dynamics, particularly in vegetation-covered and narrow channels where traditional optical methods struggle. Data used were from the airborne UAVSAR L-band sensor acquired for the Delta-X mission. We applied interferometric techniques to rapid-repeat (~30 min) SAR imagery of the southern Atchafalaya basin acquired during two flights encompassing rising-to-high tides and ebbing-to-low tides. InSAR coherence is used to identify and differentiate permanent open water channels from intermittent channels in which flow occurs underneath the vegetation canopy. The channel networks at rising and ebbing tides show significant differences in the extent of flow, with vegetation-filled small channels more clearly identified at rising-to-high tide. The InSAR phase change is used to identify locations on channel banks where overbank flow occurs, which is a critical component for modeling wetland hydrodynamics. This is the first study to use rapid-repeat InSAR to monitor tidal impacts on water flow dynamics in wetlands. The results show that the InSAR method outperforms traditional optical remote sensing methods in monitoring water flow in vegetation-covered wetlands, providing high-resolution data to support hydrodynamic models and critical support for wetland protection and management. Full article

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty. Full article

Accurate and timely crop distribution data are crucial for governments, in order to make related policies to ensure food security. However, agricultural ecosystems are spatially and temporally dynamic systems, which poses a great challenge for accurate crop mapping using fine spatial resolution (FSR) imagery. This research proposed a novel Tri-Dimensional Multi-head Self-Attention Network (TDMSANet) for accurate crop mapping from multitemporal fine-resolution remotely sensed images. Specifically, three sub-modules were designed to extract spectral, temporal, and spatial feature representations, respectively. All three sub-modules adopted a multi-head self-attention mechanism to assign higher weights to important features. In addition, the positional encoding was adopted by both temporal and spatial submodules to learn the sequence relationships between the features in a feature sequence. The proposed TDMSANet was evaluated on two sites utilizing FSR SAR (UAVSAR) and optical (Rapid Eye) images, respectively. The experimental results showed that TDMSANet consistently achieved significantly higher crop mapping accuracy compared to the benchmark models across both sites, with an average overall accuracy improvement of 1.40%, 3.35%, and 6.42% over CNN, Transformer, and LSTM, respectively. The ablation experiments further showed that the three sub-modules were all useful to the TDMSANet, and the Spatial Feature Extraction Module exerted larger impact than the remaining two sub-modules. Full article

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production. Full article

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R² = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available. Full article

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. Full article

The Slumgullion landslide is a large translational debris slide whose currently active part has likely been sliding for approximately 300 years. Its permanent motion and evolutionary processes have attracted the attention of many researchers. In order to study its mass wasting processes and evolution trend, the spatial–temporal displacement of the Slumgullion landslide was retrieved using an adaptive pixel offset tracking (POT) method with multi-track Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images. Based on three-dimensional displacement and slope information, we then revealed the spatial–temporal distribution of surface mass depletion or accumulation in the landslide, which provides a new perspective to analyze the evolutionary processes of landslides. The results indicate that the Slumgullion landslide had a spatially variable displacement, with a maximum displacement of 35 m. The novel findings of this study mainly include two parts. First, we found that the surface mass accumulated in the toe of the landslide and depleted in the top and middle area during the interval, which could increase the resisting force and decrease the driving force of the Slumgullion landslide. This result is compelling evidence which indicates the Slumgullion landslide should eventually tend to be stable. Second, we found that the distribution of geological structures can well explain some of the unique mass wasting in the Slumgullion landslide. The larger local mass depletion in the landslide neck area verifies that the sharp velocity increase in this region is not only caused by the reduction in width but is also significantly affected by the local normal faults. In summary, this study provides an insight into the relation between the landslide motion, mass volume change, and geological structure. The results demonstrate the great potential of multi-track airborne SAR for displacement monitoring and evolutionary analysis of landslides. Full article

The model-based polarimetric decomposition is extensively studied due to its simplicity and clear physical interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) data. Though there are many fine basic scattering models and well-designed decomposition methods, the overestimation of volume scattering (OVS) may still occur in highly oriented buildings, resulting in severe scattering mechanism ambiguity. It is well known that not only vegetation areas but also oriented buildings may cause intense cross-pol power. To improve the scattering mechanism ambiguity, an appropriate scattering model for oriented buildings and a feasible strategy to assign the cross-pol power between vegetation and oriented buildings are of equal importance. From this point of view, we propose a five-component decomposition method with a general rotated dihedral scattering model and an assignment strategy of cross-pol power. The general rotated dihedral scattering model is established to characterize the integral and internal cross-pol scattering from oriented buildings, while the assignment of cross-pol power between volume and rotated dihedral scattering is achieved by using an eigenvalue-based descriptor ${\mathrm{D}}_{\mathrm{OOB}}$. In addition, a simple branch condition with explicit physical meaning is proposed for model parameters inversion. Experiments on spaceborne Radarsat−2 C band and airborne UAVSAR L band PolSAR datasets demonstrate the effectiveness and advantages of the proposed method in the quantitative characterization of scattering mechanisms, especially for highly oriented buildings. Full article