Search Results (101)

Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band synthetic aperture radar (SAR) satellite data for BES monitoring. We found that the data have mainly been analyzed using image classification and regression methods, with classification methods attempting to understand how the extent, spatial distribution, and/or changes in different types of land use/land cover affect BES, and regression methods attempting to generate spatially explicit maps of important BES-related indicators like species richness or vegetation above-ground biomass. Random forest classification and regression algorithms, in particular, were used frequently and found to be promising in many recent studies. Deep learning algorithms, while also promising, have seen relatively little usage thus far. PALSAR-1/-2 annual mosaic data was by far the most frequently used dataset. Although free, this data is limited by its low temporal resolution. To help overcome this and other limitations of the existing L-band SAR datasets, 64% of studies combined them with other types of remote sensing data (most commonly, optical multispectral data). Study sites were mainly subnational in scale and located in countries with high species richness. Future research opportunities include investigating the benefits of new free, high temporal resolution L-band SAR datasets (e.g., PALSAR-2 ScanSAR data) and the potential of combining L-band SAR with new sources of SAR data (e.g., P-band SAR data from the “Biomass” satellite) and further exploring the potential of deep learning techniques. Full article

Forest fires, intensified by climate change, threaten tropical ecosystems by accelerating biodiversity loss, releasing carbon emissions, and altering hydrological cycles. Continuous detection of fire-induced forest loss is therefore critical. However, commonly used optical-based methods often face limitations, particularly due to cloud cover and coarse spatial resolution. This study explores the use of C-band Sentinel-1 Synthetic Aperture Radar (SAR) time series, combined with Bayesian Online Changepoint Detection (BOCD), for detecting and continuously monitoring fire-induced vegetation loss in forested areas. Three BOCD variants are evaluated: two single-polarization approaches individually using VV and VH reflectivities, and a dual-polarization approach ($pol$-BOCD) integrating both channels. The analysis focuses on a fire-affected area in Baixo Uraim (Paragominas, Brazil), supported by field-validated reference data. BOCD performance is compared against widely used optical products, including MODIS and VIIRS active fire and burned area data, as well as Sentinel-2-based difference Normalized Burn Ratio (dNBR) assessments. Results indicate that $pol$-BOCD achieves spatial accuracy comparable to dNBR (88.2% agreement), while enabling detections within a delay of three Sentinel-1 acquisitions. These findings highlight the potential of SAR-based BOCD for rapid, cloud-independent monitoring. While SAR enables continuous detection regardless of atmospheric conditions, optical imagery remains essential for characterizing the type and severity of change. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

Forested wetlands in temperate mountain ecosystems play a critical role in carbon sequestration and biodiversity maintenance, yet their accurate delineation remains challenging due to spectral similarity with forests and anthropogenic interference. Here, we present an optimized two-stage Random Forest framework integrating 2019–2022 growing season datasets from Sentinel-1 C-SAR, ALOS-2 L-PALSAR, Sentinel-2 MSI, and Landsat-8 TIRS with environmental covariates. The methodology first applied NDBI thresholding (NDBI > 0.12) to exclude 94% of urban/agricultural areas through spectral masking, then implemented an optimized Random Forest classifier (ntree = 1200, mtry = 28) with 10-fold cross-validation, leveraging 42 features including L-band HV backscatter (feature importance = 47), Sentinel-2 SWIR (Band12; importance = 57), and land surface temperature gradients. This study pioneers a 10 m resolution forest swamp map in the Changbai Mountain wetlands, achieving 87.18% overall accuracy (Kappa = 0.84) with strong predictive performance (AUC = 0.89). Forest swamps showed robust classification metrics (PA = 80.37%, UA = 86.87%), driven by L-band SAR’s superior discriminative power (p < 0.05). Quantitative assessment demonstrated that L-band SAR increased classification accuracy in canopy penetration scenarios by 4.2% compared to optical-only approaches, while thermal-IR features reduced confusion with forests. Forested swamps occupied 229.95 km² (9% of protected areas), predominantly in transitional ecotones (720–850 m elevation) between herbaceous wetlands and forest. This study establishes that multi-sensor fusion enables operational wetland monitoring in topographically complex regions, providing a transferable framework for temperate mountain ecosystems. The dataset advances precision conservation strategies for these climate-sensitive habitats, supporting sustainable development goals targets for wetland protection through enhanced machine learning interpretability and anthropogenic interference mitigation. Full article

Deep learning has shown significant potential in multi-band Polarimetric Synthetic Aperture Radar (PolSAR) land cover classification. However, the existing methods face two main challenges: accurately modeling the complex nonlinear relationships between multiple bands and balancing classifier parameter efficiency with classification accuracy. To address these challenges, this paper proposes a novel decision-level multi-band fusion framework that leverages the synergistic optimization of the Vision Transformer (ViT) and Kolmogorov–Arnold Network (KAN). This innovative architecture effectively captures global spatial–spectral correlations through ViT’s cross-band self-attention mechanism and achieves parameter-efficient decision-level probability space mapping using KAN’s spline basis functions. The proposed method significantly enhances the model’s generalization capability across different band combinations. The experimental results on the quad-band (P/L/C/X) Hainan PolSAR dataset, acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences, show that the proposed framework achieves an overall accuracy of 96.24%, outperforming conventional methods in both accuracy and parameter efficiency. These results demonstrate the practical potential of the proposed method for high-performance and efficient multi-band PolSAR land cover classification. Full article

The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed $L_1$ regularization solution, which has a mediocre reconstruction effect on complex scenes. In this paper, a novel SAR imaging deep unfolding network based on approximate observation is proposed for multi-band SAR systems. Firstly, the approximate observation module is separated from the optimal solution network model and selected according to the multi-band radar echo. Secondly, to realize the SAR imaging of non-sparse scenes, $L_p$ regularization is used to constrain the uncertain transform domain of the target scene. The adaptive optimization of $L_p$ parameters is realized by using a data-driven approach. Furthermore, considering that phase errors may be introduced in the real SAR system during echo acquisition, an error estimation module is added to the network to estimate and compensate for the phase errors. Finally, the results from both simulated and real data experiments demonstrate that the proposed method exhibits outstanding performance under 0.22 THz and 9.6 GHz echo data: high-resolution SAR focused images are achieved under four different sparsity conditions of 20%, 40%, 60%, and 80%. These results fully validate the strong adaptability and robustness of the proposed method to diverse SAR system configurations and complex target scenarios. Full article

This paper introduces a novel miniaturized, four-mode, semi-flexible leaky wave Multiple-Input Multiple-Output (MIMO) antenna specifically designed to advance vehicular communication systems. The proposed antenna addresses key challenges in 5G low- and high-frequency bands, including millimeter-wave communication, by integrating innovative features such as a periodic Spoof Surface Plasmon Polariton Transmission Line (SSPP-TL) and logarithmic-spiral-like semi-circular strip patches parasitically fed via orthogonal ports. These design elements facilitate stable impedance matching and wide impedance bandwidths across operating bands, which is essential for vehicular networks. The hybrid combination of leaky wave and SSPP structures, along with a defected wide-slot ground structure and backside meander lines, enhances radiation characteristics by reducing back and bidirectional radiation. Additionally, a naturalization network incorporating chamfered-edge meander lines minimizes mutual coupling and introduces a fourth radiation mode at 80 GHz. Compact in size (14 × 12 × 0.25 mm³), the antenna achieves high-performance metrics, including S11 < −18.34 dB, dual-polarization, peak directive gains of 11.6 dBi (free space) and 14.6 dBi (on vehicles), isolation > 27 dB, Channel Capacity Loss (CCL) < 3, Envelope Correlation Coefficient (ECC) < 0.001, axial ratio < 2.25, and diversity gain (DG) > 9.85 dB. Extensive testing across various vehicular scenarios confirms the antenna’s robustness for Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Infrastructure (V2I) communication. Its exceptional performance ensures seamless connectivity with mobile networks and enhances safety through Specific Absorption Rate (SAR) compliance. This compact, high-performance antenna is a transformative solution for connected and autonomous vehicles, addressing critical challenges in modern automotive communication networks and paving the way for reliable and efficient vehicular communication systems. Full article

In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset. Unfortunately, due to various constraints, the availability of labeled image data for training SAR vehicle detection networks is quite limited. This scarcity of data has become one of the main obstacles hindering the further development of SAR vehicle detection. In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. In the experiment, the samples we generated and screened achieved an average improvement of 2.32%, with a maximum of 6.6% in $mA{P}_{75}$ on five different strong baseline detectors. Full article

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications. Full article

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks. Full article

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R² = 0.62, p < 0.01), reducing RMSE to 579 kg·ha⁻¹. Using only optical or SAR data yielded R² values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. Full article

The Leaf Area Index (LAI) plays a crucial role in assessing the health of forest ecosystems. This study utilized ICESat-2/ATLAS as the primary information source, integrating 51 measured sample datasets, and employed the Sequential Gaussian Conditional Simulation (SGCS) method to derive surface grid information for the study area. The backscattering coefficient and texture feature factor from Sentinel-1, as well as the spectral band and vegetation index factors from Sentinel-2, were integrated. The random forest (RF), gradient-boosted regression tree (GBRT) model, and K-nearest neighbor (KNN) method were employed to construct the LAI estimation model. The optimal model, RF, was selected to conduct accuracy analysis of various remote sensing data combinations. The spatial distribution map of Dendrocalamus giganteus in Xinping County was then generated using the optimal combination model. The findings reveal the following: (1) Four key parameters—optimal fitted segmented terrain height, interpolated terrain surface height, absolute mean canopy height, and solar elevation angle—are significantly correlated. (2) The RF model constructed using a combination of ICESat-2/ATLAS, Sentinel-1, and Sentinel-2 data achieved optimal accuracy, with a coefficient of determination (R²) of 0.904, root mean square error (RMSE) of 0.384, mean absolute error (MAE) of 0.319, overall estimation accuracy (P₁) of 88.96%, and relative root mean square error (RRMSE) of 11.04%. (3) The accuracy of LAI estimation using a combination of ICESat-2/ATLAS, Sentinel-1, and Sentinel-2 remote sensing data showed slight improvement compared to using either ICESat-2/ATLAS data combined with Sentinel-1 or Sentinel-2 data alone, with a significant enhancement in LAI estimation accuracy compared to using ICESat-2/ATLAS data alone. (4) LAI values in the study area ranged mainly from 2.29 to 2.51, averaging 2.4. Research indicates that employing ICESat-2/ATLAS spaceborne LiDAR data for regional-scale LAI estimation presents clear advantages. Incorporating SAR data and optical imagery and utilizing diverse data types for complementary information significantly enhances the accuracy of LAI estimation, demonstrating the feasibility of LAI inversion with multi-source remote sensing data. This approach offers an innovative framework for utilizing multi-source remote sensing data for regional-scale LAI inversion, demonstrates a methodology for integrating various remote sensing data, and serves as a reference for low-cost high-precision regional-scale LAI estimation. Full article

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m. Full article

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R² = 0.56, RMSE = 21.01 cm) and SVM (R² = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R² = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R² = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops. Full article

The effects of simulated acid rain (SAR) on the photosynthetic performance of subtropical coniferous species have not been thoroughly investigated. In this study, we treated two coniferous species, Pinus massoniana (PM) and Cunninghamia lanceolata (CL), with four gradients of SAR and then analyzed their photosynthetic activities through measurements of gas exchange, prompt fluorescence (PF), delayed fluorescence (DF), and modulated reflection at 820 nm (MR₈₂₀). Gas exchange analysis indicated that the decrease in the net photosynthetic rate (Pn) in PM and CL was unrelated to stomatal factors. For the PF transients, SAR induced positive K-band and L-band, a significant reduction in photosynthetic performance index (PI_ABS), the quantum yield of electron transfer per unit cross-section (ET_O/CS_m), and maximal photochemical efficiency of photosystem II (F_v/F_m). Analysis of the MR₈₂₀ kinetics showed that the re-reduction kinetics of PSI reaction center (P700⁺) and plastocyanin (PC⁺) became slower and occurred at later times under SAR treatment. For the DF signals, a decrease in the amplitude of the DF induction curve reduced the maximum value of DF (I₁). These results suggested that SAR obstructed photosystem II (PSII) donor-side and acceptor-side electron transfer capacity, impaired the connectivity between PSII and PSI, and destroyed the oxygen-evolving complex (OEC). However, PM was better able to withstand SAR stress than CL, likely because of the activation of a protective mechanism. Full article