The Ice, Cloud, and Elevation Satellite-2 (ICESat-2) has been witnessed to improve the performance of ground information retrieval. Yet, extracting high-precision ground photons over expansive areas remains a significant challenge, particularly in forested regions with steep slopes. Ground photons can not be extracted correctly in a large slope area because of the unclear spatial distribution difference between ground and canopy photons. To address the abovementioned issue, we propose a novel slope correction method to extract ground photons for sub-canopy terrain retrieval accurately. Our approach begins with a way to find the terrain trend of the photon cloud. Turning points are then identified from the generated terrain trend to segment the signal photon cloud into distinct terrain regions. We detrend the photon cloud in each segment by a horizontal rotation, making the spatial distribution of ground and canopy photons clear. Finally, an iterative method based on the percentile range of photons’ elevation to extract fine ground photons. We validated the proposed method using nine datasets from three rugged forest areas. The filtering process effectively denoised photon cloud data, while turning point detection achieved high accuracy, with F-scores ranging from 0.89 to 0.98. Terrain retrieval results demonstrated remarkable accuracy, yielding RMSE values of 3.82 m, 3.61 m, and 2.45 m across the study areas. Ablation experiments underscored the effectiveness of the slope correction and ground photon extraction techniques, showing RMSE reductions of 10.6%, 6.5%, and 3.6% after slope correction and a decrease in unusable terrain ratios (Inv+Res>3) by 8.1%, 3.6%, and 20%. Compared to conventional methods, our approach reduced RMSE values (from 0.55 m to 2.61 m) and Inv+Res>3 ratios (from 0.04 to 0.37). Furthermore, the method outperformed ATL08 terrain estimates, providing superior accuracy in sub-canopy terrain reconstruction.

Acquiring high-resolution and accurate forest height is essential for estimating terrestrial carbon storage and detecting changes. Since 2010, TanDEM-X has obtained an unprecedented global interferometric SAR dataset and has been widely used to estimate forest height. However, its accuracy is limited due to insufficient observational information. Since September 2018, NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has acquired global discrete surface elevation and forest canopy height, providing an excellent opportunity to improve the performance of TanDEM-X forest height estimation. Based on our previous research, this paper proposes some methods to estimate highresolution and large-scale forest height using these two missions combined with optical remote sensing data, and estimates forest height in Spain. As validated against LiDAR data, the RMSE of the estimated optimal average forest height ranges from 1.83 m to 3.70 m, at the resolution of onehectare forest stands.

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) plays an important role in the scientific task of global terrain mapping. ICESat-2 records high-precision object information in the form of photon clouds. Ground photon extraction is a key step in retrieving high-precision terrain. However, it is a difficult task to extract ground photons in mountainous forest areas due to the influence of terrain slopes. We proposed a slope correction method to mitigate the effect of terrain slope on ground photon extraction. First, the background noise photon is removed by a photon cloud filtering method. Second, we used the Douglas–Peucker algorithm to find the special terrain points (STPs), the photon cloud then was divided into a series of segments by STPs. We calculated the slope in each segment to form a rotation matrix, and corrected the slope of the photon cloud in each segment. The results show that the obtained STPs have high accuracy, and these STPs divided the photon cloud into a series of segments accurately. Compared with the reference slopes in each segment, the obtained slopes have a small RMSE of 2.3° and a high R2of 0.96. Additionally, the results indicate that the discrimination between ground photons and canopy photons in mountainous forest areas is significantly obvious after a slope correction.

Phase linking is the crucial step in distributed scatterer InSAR processing, which determines the quality of time-series interferometric phases. The weight matrix is the key measure of phase linking method, which controls the participation of each interferometric pair for the single-master phase linking. Existing methods don’t consider the impacts of temporal-changed polarimetric scattering characteristics, leading to large phase closure errors. Based on the polarimetric stationarity, we propose a novel scattering consistency weight measure. Combined with the coherence weight, a joint weight is generated to improve three general phase linking methods. The methods are validated with time-series Radarsat-2 PolSAR data over Kilauea Volcano, Hawaii. The results show that the proposed methods obtain higher temporal posterior coherence and better equivalent single-master (ESM) interferometric phase than traditional methods.

The upcoming launch of long-wavelength synthetic aperture radar (SAR) systems, including BIOMASS, TanDEM-L, and NISAR, will bring new perspectives to interferometric SAR (InSAR) topography mapping. However, these advanced SAR systems will inevitably encounter atmospheric effects in the repeat-pass interferometric mode. To demonstrate the viability of large-scale topography mapping using the new SAR satellites, we propose a digital elevation model (DEM) block adjustment considering atmospheric effects to correct systematic errors and atmospheric delay errors between different strips. This paper conducted simulated experiments in the coastal and inland areas using L-band Advanced Land Observation Satellite (ALOS)-1 PALSAR data, respectively. We utilized Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data to assess the vertical accuracy of DEM with and without considering atmospheric effects. The results showed the effectiveness of our method, with an improved RMSE of 84.7% in the coastal area (21.29m to 3.25m) and 75.6% in the inland area (13.01m to 3.17m).

TanDEM-X InSAR data has exhibited commendable performance in forest height inversion, with the semi-empirical SINC model (SeEm-SINC) proving its robustness in flat regions. However, the relationship between InSAR coherence and forest height become unreliable in forest scenes of mountainous terrain. Significant overestimation or underestimation of forest height occurs when using coherence only in areas with positive or negative slope, and the bias is strongly correlated with the range slope. Building upon the SeEm-SINC model, we investigate the relationship between slope and the bias involved in forest height inversion. Additionally, we present a forest height inversion approach utilizing TanDEM-X InSAR ascending and descending data. Experimental results demonstrate that this method effectively mitigates estimation deviations caused by slope in forested mountainous areas.

Understanding the vertical structure of vegetation is crucial for applications such as tree height inversion, biomass estimation, and terrain detection in forests. A novel approach for the classification of vegetation vertical structure is presented, utilizing multi-source data integration. The analysis begins with lidar waveform characteristics, defining vegetation layers based on peak count. Employing machine learning, a correlation is established between polarimetric features, polarimetric interferometric features, and vertical vegetation structure. The study explores the potential of spatial information extraction through combined polarimetric and polarimetric interferometric SAR techniques. This innovative method offers a fresh perspective on vertical vegetation classification.

Precision agriculture management relies on the delineation of crop field edges. Multi-polarization SAR technology has the ability to penetrate clouds and capture morphological structures or moistures, suited for extracting crop field edges. Due to the time-dependent characteristics and phenological evolutions of crops, the methods with single-date data are difficult to detect complete edges. Moreover, the existing methods fail to extract the dynamic time-varying patterns, limiting the improvement of edge detection accuracy. Based on this, this paper proposes a novel crop field edge detection method based on the time-varying polarimetric characteristics. First, a spatial-temporal homogeneity measure is proposed to pre-identify the edge and homogenous area, for guiding the adaptive calculation of edge strength. Based on the time-series polarimetric stationarity and the trace moment estimation theory, the proposed measure enlarges the separating degree of various crop parcels. Second, a joint edge strength is proposed to enlarge strength contrast between edge and homogenous area. With the spatial-temporal homogeneity measure, it combines the similarity with the root mean square and the similarity with time-series average covariance matrix. Based on the advantages of two kinds of similarities, it highlights the field edges and reduces the impact of speckle noises. Evaluated by 8 quad-polarization and 14 dual-polarization SAR images, the proposed edge detection method achieves better visual presentations and detection accuracies than traditional methods. With the statistics of the signal-noise ratio (SNR), the joint edge strength also has higher strength contrast than conventional strengths. The relevant codes can be found in https://github.com/DawnHanGeo/TSPolEdge.git.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been widely used to obtain high-precision sub-canopy topography. However, due to the vegetation cover over densely forested areas, the ground photons are sparse, which makes it difficult to accurately estimate the sub-canopy topography over densely forested areas. In this paper, we proposed a novel method for retrieving sub-canopy topography over densely forested areas from ICESat-2/ATL03 data. First, the proposed method used an improved elevation frequency histogram statistics (imEFHS) method to obtain candidate ground seed photons (GSPs). In densely forested areas, the obtained candidate GSPs are easily misclassified as canopy photons. Therefore, we performed a gradient-constrained morphological operation to identify erroneous GSPs. Finally, an erroneous GSPs refinement approach was derived to correct erroneous GSPs over densely forested areas. In addition, the sub-canopy topography can be presented by the refined GSPs with cubic spline interpolation. ICESat-2/ATL03 data acquired over densely forested areas were selected for testing the proposed method. The results in the given test sites show that the proposed method can extract sub-canopy topography accurately, with a root-mean-square error (RMSE) of 1.71 m over densely forested areas. We also compared the retrieved sub-canopy topography results with NASA ATL08 terrain samples. We found that the ratio of useful sub-canopy topography results (Residual2) between the retrieved sub-canopy topography results and the reference high-precision DTMs reached 0.93, which is much higher than that of the ATL08 terrain samples (R2= 0.63).

Forest height is an indispensable parameter for natural resource investigations. Spaceborne Interferometric SAR (InSAR) has the sensitivity to measure forest height, especially TanDEM-X, which provides high-quality interferometric coherence without the effects of atmospheric delay and temporal decorrelation. In this paper, we seriously considered the limited penetrability of TanDEM-X InSAR, and proposed a two-step machine learning (ML) method to estimate large-scale forest height by combining TanDEM-X InSAR data, ICESat-2 data, and Landsat 8 data. The forest scattering phase center (SPC) height of InSAR is estimated by the first ML, and on this basis, the relationship between the SPC height and forest height is established by the second ML. We validated the effectiveness of proposed method in a Spanish Mediterranean climate forest region with airborne LiDAR data. As validated against LiDAR data, the accuracy of the estimated SPC height ranges from 2.07 m to 2.49 m, and the root mean square error (RMSE) of the average forest height ranges from 1.79 m to 2.93 m, at the resolution of four-hectare forest stands.

The TanDEM-X InSAR have shown great potential for estimation of forest height with high-resolution over large-scale. The feasibility of retrieving forest height is verified in subset area of different forest scenarios, indicating that InSAR coherence can be a reliable variable for inverting forest height without using interferometric phase. Therefore, for the Spanish forest with relatively short canopy height over Mediterranean climate, TanDEM-X InSAR data was used in this paper to map a large-scale forest height. The experimental results show that the proposed mapping method of forest height in large-scale has the advantages of promising accuracy and simplicity.

Purer contributions obtained from three diagonal elements of the coherency matrix are expected. This makes polarimetric synthetic aperture radar (PolSAR) easier to understand and to use in some applications, since the main information is aggregated into three mutually independent polarization channels. Therefore, we propose a method to decouple different polarization channels (DDPCs) in this letter. In such a case, all the nondiagonal elements of the coherency matrix are turned to zero, and three uncorrelated polarization channels are obtained. For this purpose, two rotation angles with physical meaning are proposed. They are responsible for rotating$T_{12}$to zero. Radarsat-2 data are used to validate the proposed method. The results show that the proposed rotation angles are effective for land-cover classification. Furthermore, three polarization channels obtained from the DDPC method can mitigate the overestimation of volume scattering power for oriented buildings.

Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.

Estimating vegetation height from polarimetric interferometric synthetic aperture radar (PolInSAR) data using the random volume over ground (RVoG) model has long been used. Most of these methods propose models and apply them to real airborne data to demonstrate their potential. The single-baseline PolInSAR forest height estimation based on the RVoG model lacks sufficient observation information. For this reason, multibaseline data are introduced to address this. This paper fits the relationship of model parameters in multibaseline observation scenarios, and focuses the forest height inversion on the calculation of pure volume decorrelation. Subsequently, a multibaseline forest height joint inversion method based on the least squares principle is adopted. Finally, we use airborne PolInSAR data from the Lope and Mondah sites collected by UAVSAR and F-SAR systems during AfriSAR 2016 to verify the proposed method. The experimental results show that the accuracy of the proposed method (Lope: root mean square error (RMSE) = 5.8 m, Mondah: RMSE = 5.12 m) is 38.1% and 34.53% higher than the coherence separation product (Lope: RMSE = 9.37 m, Mondah: RMSE = 7.82 m).

Lots of noise photons limit the application of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in forest canopy height retrieval. Abundant noise photons lead to unstable filtering results, thus affecting the canopy top photons classification. Therefore, this study proposes a method that takes account of the background noise level in the photon cloud. First, we propose an improved Differential, Regressive, and Gaussian Adaptive Nearest Neighbor (DRAGANN) filtering approach based on the DRAGANN filtering method. To obtain a more stable filtering result, large-scale and small-scale search radiuses are combined to improve the DRAGANN filtering performance. Second, we retrieve sub-canopy terrain topography by the multi-scale window detection method from the filtered photon cloud. Finally, a robust photon acquisition criterion based on the elevation difference of the filtered photons and the uneven density of signal photons is proposed to extract the canopy-top surface, which aims to mitigate the influence of inconsistencies of residual noise photons along the track. In addition, considering the fluctuation of the canopy surface, we use a non-spline interpolation method to obtain a continuous canopy surface, which avoids the Runge phenomenon caused by the spline interpolation method. The ICESat-2 data acquired in the Harvard Forest Region (HARV) is selected to assess the proposed method. The filtering performance of the improved DRAGANN approach shows stable than that of the DRAGANN approach. The proposed method’s root means square error (RMSE) and coefficient of determination (R2) reach 3.85 m and 0.55, respectively. The results indicate that the proposed method can retrieve reliable forest canopy height from the ICESat-2 data and performs significantly better than the ATL08 canopy height product in the test site.

In this letter, we propose a new scattering model to describe the polarimetric scattering information of the real part of$T_{23}$in the coherency matrix. To achieve this goal, by combining the dihedral corner reflector scattering model and the polarimetric orientation angle (POA), a rotated dihedral scattering model is proposed. The proposed model is embedded into Singh’s six-component decomposition model, and we further develop a seven-component decomposition model. The proposed method was validated by polarimetric synthetic aperture radar (SAR) data sets acquired by the ALOS-2/PALSAR-2 and AIRSAR systems. The results show that, compared with the existing decomposition methods, the proposed method has a superior ability to distinguish oriented buildings from vegetation.

Vegetation height estimation of desert ecosystems is important for understanding the groundwater cycle. ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) provides an opportunity to measure vegetation heights on a global scale. This letter proposed a method for retrieving low and sparse vegetation heights in desert ecosystems. Considering the significant difference in density between the vegetation photons and the ground photons, the ground photons were removed based on the terrain-adaptive method first. The localized density parameter was then introduced to distinguish the vegetation photons and the noise photons. Finally, the vegetation heights were obtained by the elevation percentile approach. The proposed method was tested using the ICESat-2 data acquired over a desert located in Arizona. The vegetation height results derived by the proposed method have an RMSE of 0.78 m which is significantly less than that of ATL08 with an RMSE of 4.26 m, which demonstrates it is feasible to extract low and sparse vegetation height in desert areas. The results showed that ICESat-2 photon cloud lidar data are suitable for low and sparse vegetation height investigations in desert ecosystems.

The objective of this letter is to extend a method proposed by Kweon et al. to retrieve soil moisture (mv) over bare soil surface by combining neighborhood pixels of single-polarization synthetic aperture radar (SAR) data. This letter uses single-polarization (HH, VV) SAR data to simultaneously retrieve the root-mean-square (rms) height (hrms) and the real part of the relative dielectric constant (εs) which can be converted to soil moisture content. For the copolarization SAR data, the letter first uses the Integral Equation Model (IEM) and the semiempirical calibration of the correlation length (L) to obtain the probability distribution curve of rms height and the real part of the relative dielectric constant for each neighborhood pixel. Then, these probability distribution curves are placed on the εs-hrmsplane, and the juxtaposition model is applied to obtain the average value of estimations of neighborhood pixels. The average soil moisture estimations of neighborhood pixels in farmlands are compared with the in-situ measurements with the RMSE equal to 0.036 cm3/cm3and the correlation coefficient equal to 0.84 at VV polarization in the L band, which demonstrates that the proposed method is suitable to invert soil moisture with acceptable accuracy and high resolution. However, volume scattering contribution from crops can decrease the performance of the proposed method.

A novel method based on covariance vector sparse Bayesian learning (CV-SBL) is proposed in this letter to reconstruct the vertical structure of forests using a small number of synthetic aperture radar (SAR) images. This method regards the inversion of forests reflectivity profiles in the wavelet domain as a sparse signal reconstruction (SSR). Based on the covariance matrix matching criterion, the backscatter power of forests signal and noise will be jointly solved adaptively. Through a few iterations, the exact positions of the closely spaced phase centers can be obtained to simplify the characterization of the vertical structure of the forests. Unlike the traditional compressive sensing (CS) method based on$\ell _{1} $norm convex optimization, the novel method can obtain a real sparse solution without setting hyper-parameters and has a more reliable and accurate reconstruction performance. Besides, the computational efficiency of the new method is much higher than that of the$\ell _{1} $minimization CS method, and it is more suitable for large-scale forests mapping applications. The proposed method is validated using P-band TropiSAR 2009 data set over a test site in Paracou, French Guiana. Furthermore, the reconstruction performance of the proposed method is compared with the spectral analysis methods (Beamforming and Capon) and the$\ell _{1} $minimization super-resolution CS.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), launched in May 2019, increased the availability of different types of spaceborne laser altimetry data. But the obtained photon point cloud, especially those for the forest area with steep terrains, contains a lot of background noise that may greatly decrease the accuracy of the extracted digital elevation model (DEM) and forest height. Therefore, removing the background noise photons mixed up with the signal photons is necessary. We proposed a method for photon point cloud filtering using the backward elliptical distance (BED). First, we used the BED to express the spatial distance of the photon point cloud. On this basis, the backward local density was derived to identify signal photons and noise photons. Then we divided the data into several segments and set a local threshold for each segment to identify signal photons and noise photons. We validated the proposed method in the forested area with steep terrains in Washington State and Spain, and compared the results with that of other filtering methods. The comparison shows that the proposed method separates signal photons and noise photons better than other methods. The comprehensive evaluation indexes$F$in Spain and that of the left, center, and right channels in Washington reach 0.9892, 0.9899, 0.9905, and 0.9915, respectively. In addition, compared with the global threshold selection, the local threshold selection is more stable.

A general volume scattering model was proposed by Neumannet al. based on anisotropy and orientation randomness, which has been successfully applied to forest parameter retrieval from polarimetric interferometric SAR. A general implementation of the Neumann volume scattering model is designed for fully polarimetric SAR data, incorporating the contributions from the ground level consisting of surface scattering and double-bounce scattering. This combination helps to apply the Neumann volume scattering model to different vegetation scenarios. Based on this, a new parameter inversion framework is developed to estimate the anisotropy and the orientation randomness of the volumetric media, which are expected to be consistent with the ground truth. Meanwhile, the polarimetric information from the ground responses is extracted so that the interaction process between the SAR signals and the ground objects is fully explored. The L-band AIRSAR image covering Flevoland, Netherlands was selected for the experiments. Compared with Neumann decomposition, the parameters estimated by the proposed method can more realistically reflect the canopy scattering characteristics. Moreover, the classification accuracy of crops is effectively improved by the feature parameters obtained by the proposed method.

For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over forest areas. In this letter, based on the TD + RVoG model, a new forest height estimation method is proposed for use with multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. First, the correlation between the ground-to-volume ratios (GVRs) associated with the different polarizations is parameterized according to the geometric interpretation of the RVoG model. An interferometric pair that is assumed to have no TD is then selected based on the eccentricity of the polarimetric coherence region, and the other interferometric pairs are fitted by the TD + RVoG model. E-synthetic aperture radar (E-SAR)$P$-band PolInSAR data sets affected by TD are used to prove the effectiveness of the proposed method. The experimental results show that the forest height results are improved by 25.90% when compared to the RVoG-based method.

Homogeneous pixel selection (HPS) plays an important role in the application of multitemporal SAR interferometry. The statistical goodness-of-fit testing of the temporal samples has been widely used for HPS. However, the detection rates of the existing methods are unsatisfactory under small datasets. In this paper, a stacked auto-encoder (SAE) network based method is proposed for the selection of homogeneous pixels under the idea of deep learning image classification, as termed by SAEHPS. The SAE network is used to learn the spatial distribution behavior of the average intensity image. The deep network is trained and tested on different high-resolution SAR datasets of the Hong Kong Airport and the Fuzhou City, and three pixel-wise labels (i.e., high, medium, and low reflections) are regarded as outputs of model learning. The unsupervised training and supervised fine-tuning realize the class prediction. The results show that the SAE can achieve robust accuracies above 90% based on empirically labeled samples, especially in non-architectural areas where the distributed scatterers exist. The SAE results are devoted to the multitemporal PS/DS InSAR approach to identify homogeneous pixels. Both qualitative and quantitative experiments in HPS, phase optimization, and deformation monitoring have demonstrated the superiority of the novel method.

With the increasing amount of synthetic aperture radar (SAR) data with various imaging geometries (at least ascending/descending tracks), it is possible to obtain accurate multi-dimensional (MD) deformation time series with long time span. However, in most cases SAR data of different geometries are un-synchronously acquired over the same region, making it impossible to directly solve the underdetermined observation model (OSM) between the interferometric SAR (InSAR) measurements and the MD deformations. Kalman filter (KF), as one of the most famous dynamic estimators, can obtaina prioriinformation of the unknowns based on the preexisting time series, therefore it can be used to deal with this InSAR underdetermined problem. This article employs the KF to realize the dynamic estimation of MD deformations with short-baseline interferograms. The innovation lies in the establishment of the KF state transition model (STM) and OSM, which aims to make the InSAR monitoring problem better adapt to the KF. Particularly, by assuming a smooth deforming process, existing deformation time series are used to establish the STM and to predict the deformations at current moment. Besides, a strain model (SM) is employed to assist the establishment of the OSM. Simulation and real experiments in the Geysers geothermal field (GGF), U.S. demonstrate that, compared with the state-of-the-art methods, the proposed KF method allows more robust deformation estimation and achieves higher computational efficiency for dynamic estimation.

Interferometric phase optimization is important key processing for ensuring the application performance of interferometric synthetic aperture radar (InSAR) technology. The noise’s standard deviation depends on the number of looks and the coherence magnitude. Usually, the coherence estimation uses statistical averaging with spatial samples to reduce the speckle noise in interferometric phase images. It has been demonstrated that polarization plays a significant role in the variation of interferometric complex coherence. Currently, InSAR technology utilizes polarimetric information to develop the coherence optimization theory for improving the phase quality. However, the observed coherence region in the complex unitary circle is usually biased from the free-noise one due to the finite multilooking effect and the practical scene heterogeneity, which makes the coherence optimization unstable. In contrast, based on the coherence estimation theory, this article proposes taking polarimetric information as the statistical samples for constructing polarimetric InSAR (PolInSAR) total power (TP) coherency matrix and performs a joint polarization-space nonlocal estimation. Simulated and real experimental results demonstrate that the proposed method improves the performance of the interferometric phase optimization in these three aspects compared with traditional coherence optimization, including phase quality improvement, the number of high coherent points, and computational efficiency.

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm’s effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.

Accurately predicting ground surface deformation is a crucial task in mining-related geohazards control. Interferometric synthetic aperture radar (InSAR) technique can widely detect historical line-of-sight (LOS) displacements with a high spatial resolution. By incorporating with spatio-temporal deformation models, InSAR can predict kinematic 3-D displacements due to underground mining. However, this method depends on the geometric parameters (at least seven generally) of underground mined-out areas, hindering its practical applications especially over a large area. To circumvent this, we proposed a new method for predicting kinematic 3-D mining displacements by incorporating InSAR with a temporal model, rather than spatio-temporal models used before, in this article. In doing so, much less prior parameters (only three and can be empirically given) are required, with respect to the previous InSAR-based methods. To achieve this, we first revealed that InSAR LOS displacements caused by underground longwall mining at a single point temporally follow an S-shaped growth pattern. Meanwhile, we also showed that a Weibull model can describe the temporal evolution well. Based on these findings, the proposed method first predicts, in a point-wise manner, the kinematic LOS mining displacements from historical InSAR measurements using the Weibull model and a Kalman filter. The kinematic 3-D displacements are then resolved from the predicted LOS displacements with the help of a common prior information relating to mining deformation. The proposed method was tested in the Datong coal mining area of north China. The results show an averaged accuracy of about 0.007 m of the resolved kinematic 3-D displacements.

It is a common method to resolve three-dimensional (3-D) deformation components associated with underground mining by incorporating Single-track interferometric synthetic aperture radar (InSAR) with a prior deformation model termed linear proportion model (LPM) (hereinafter referred to as Sin-LPM). Nevertheless, the Sin-LPM method relies on three model parameters that are needed to bein situcollected, and it neglects their dynamic changes during the period of underground extraction, narrowing the practical applications of the Sin-LPM method, and degrading the accuracy of the estimated 3-D displacements. In this article we propose a new method to resolve 3-D mining displacements from multi-track InSAR observations by incorporating with the LPM. In which, the model parameters are first considered as dynamic and further adaptively estimated from the multi-track InSAR observations using a robust solver. Following that, 3-D mining displacements are resolved from the multi-track InSAR using the conjugate gradient method (CGM). The proposed method was tested in Datong coalfield, China. The results suggest that the proposed method can well estimate 3-D mining displacements with a mean error of about 1.8 cm. Compared with the previous Sin-LPM, the proposed method can effectively improve the accuracy of the estimated 3-D displacements (e.g., 69% in this study), and can work well even over a large area where the model parameters are unknown. The proposed method offers a new insight to improve the InSAR-based retrieval of 3-D displacements induced by other anthropologic or geophysical activities.

The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.

The potential of interferometric synthetic aperture radar (InSAR) for subsurface height estimation has long been recognized; however, this method is greatly limited by the data sources and the various errors encountered in a highly dynamic environment such as a desert. In this letter, a coherence scattering model based on the volume coherence and imaging geometry of the InSAR acquisitions is proposed to retrieve the penetration depth of the synthetic aperture radar (SAR) signal in a hyperarid desert area. The proposed method includes two main parts: 1) the dielectric constant of the study area is first derived by employing an empirical model with the L-band SAR data, and then, the results are used to calibrate the vertical effective wavenumber after the refraction process and 2) together with the extracted volume coherence from the SAR data, the scattering model is employed to retrieve the penetration depth. The application scope of the vertical effective wavenumber in the volume and temporal decorrelation effect of the model is also discussed in this letter. The method was tested with the Advanced Land Observing Satellite-1 (ALOS-1) Phased Array-type L-Band Synthetic Aperture Radar (PALSAR) data from a desert area in southeast Libya. The results show that the average penetration depth of the L-band SAR in the study area is 2.98 m, and the standard deviation is 1.06 m.

In this letter, we propose a forest height inversion model based on three-order Fourier-Legendre (FL) polynomials from the multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. The proposed model expresses the vertical structure of the volume layer as three-order FL polynomials. Meanwhile, the forest height is treated as an unknown parameter, rather than a priori information, as adopted in polarization coherence tomography technology. On the other hand, to be more realistic, the proposed model uses multipolarization PolInSAR data and considers that the synthetic aperture radar (SAR) signals in different polarizations describe the forest vertical structure in different ways so that we can obtain a more comprehensive forest vertical structure. Airborne P-band PolInSAR data acquired over the boreal and tropical forest areas were selected for testing the forest height inversion method. The results show that, compared to random volume over ground (RVoG) model-based inversion, the accuracy of the proposed model is improved by 28.20% and 17.30%, respectively, for the boreal and tropical forest scenes.

The traditional nonlocal estimations have been demonstrated to be effective and widely used in polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, there still exist some problems about two key steps: 1) in the homogeneous pixels selection step, the regular square (RS) patches matching strategy shows the limited performance in textured area and 2) in the central pixel value estimation from the selected pixels, the well-known Lee estimator, which only uses the intensity statistic, tends to be unstable. To overcome these restrictions, we put forward two robust strategies and then propose an improved PolInSAR complex coherence nonlocal estimation: 1) the shape-adaptive (SA) patch is utilized for flexibly matching the similar pixels in a large search window, which is constructed by combining the likelihood ratio test (LRT) and the region growing (RG) algorithm and 2) the trace-moment-based nonlocal reduced bias (TMB-NLRB) estimator is employed, which considers the interchannel correlations and evaluates more accurately the homogeneity level between the selected pixels. The denoising effect of both strategies is quantitatively analyzed on the simulated data set, and the proposed algorithm is compared with classical estimation algorithms on a TerraSAR-X/TanDEM-X PolInSAR data set. These experimental results show that the proposed method provides better performance in speckle reduction, detail preservation, and complex coherence estimation.

The first results of a project aimed at estimating forest height over the entire Spain by means of TanDEM-X data are shown and discussed in this work. Four test sites representative of the Spanish forests are introduced, as well as the data used for validation of results. Results obtained over one of the test sites (in Teruel province) are presented here. Among the challenges found in the project, the influence of slope in mountain areas and the relatively short height of the forest canopy make this work distinctive to previous projects employing TanDEM-X data for estimation of heights in tropical, boreal and temperate regions.

Multitemporal Sentinel-1 data sets are suitable for high-precision agricultural classification mapping due to its short revisit period and dual-polarization channels. At present, more and more attention has been paid to the multitemporal classification methods with feature curve matching, because the time-varying polarimetric characteristics show great potential to crop classification. However, current methods only use the variation of single intensity feature, and the indicators for evaluating similarity have not considered the effect of the variable growing seasons of different parcels. Based on this, a new method with feature curve matching is proposed, which uses the combination of multiple features and applies the discrete Fréchet distance and the Pearson distance to evaluate the similarity between two curves. The proposed method applies time-series Sentinel-1 images for crop classification in two study areas of Gansu province, China. The results show that the overall accuracies in two study areas of the proposed method are 94.98% and 90.20%, respectively. This method achieves higher classification accuracies, compared with the SVM classification method and some other methods with feature curve matching.

Polarimetric synthetic aperture radar interferometry (PolInSAR) and light detection and ranging (LiDAR) have their own respective advantages and disadvantages in extracting large-scale forest height. In this letter, we present an advanced approach to obtain forest canopy height by combining these two strategies. More specifically, the novelty of the proposed method focuses on a dual-baseline selection from multibaseline PolInSAR data, which ensures the robust performance of the forest height inversion by effectively improving the estimation of volume-only coherence. The dual-baseline selection can be regarded as a supervised classification problem. We consider support vector machine (SVM) as an appropriate classifier, and a small amount of sparse LiDAR samples within the coverage of the PolInSAR data (less than 1%) are chosen to assist with the training of the dual-baseline combination classification, which can be met by the current spaceborne LiDAR missions. Finally, we validate the proposed approach by airborne P-band synthetic aperture radar (SAR) data acquired by the F-SAR system and LiDAR data acquired by the National Aeronautics and Space Administration (NASA) Land, Vegetation, and Ice Sensor (LVIS) during the 2016 AfriSAR campaign. The estimation accuracy of the proposed method [$R^{2} = 0.73$ , root-mean-square error (RMSE) = 3.17 m] is 25.24% higher than that of the existing SVM fusion approach devoted to single-baseline selection ($R^{2} = 0.59$ , RMSE = 4.24 m).

Polarimetric synthetic aperture radar tomography (Pol-TomoSAR) allows to achieve a 3-D characterization over urban areas using multiple polarimetric acquisitions. However, using spaceborne datasets, such as TerraSAR-X, it is difficult to localize the distributed or uncorrelated scattering patterns along elevation due to the temporal decorrelation. In order to overcome this limitation, this paper proposes polarimetric correlation tomographic techniques based on Tandem-mode images. The key of this technique is to build a covariance matrix from the observed Tandem coherence pairs, and then apply conventional covariance-based tomographic techniques. This processing allows to extract both coherent and distributed scatterers. The resulting 3-D reconstruction is more refined and detailed, compared to the one derived from TerraSAR-X data. Seven TSX/TDX pairs in fully polarimetric mode over a small county in Yunnan province, China, are used to demonstrate the effectiveness of this technique for the characterization of urban environments.

Due to the limited accuracy of the current navigation systems, uncompensated motion errors during airborne synthetic aperture radar (SAR) preprocessing, i.e., the residual motion error (RME), cause undesirable phase errors in the final interferogram. Especially in airborne repeat-pass interferometric SAR (InSAR), the removal of RME is critical for topographic mapping. In this letter, based on the geometry of a single-baseline interferogram, a model is first developed for describing the relationship between the time-varying baseline parameters and the interferometric phase errors. A robust estimation is then employed to estimate the RME-induced phase errors. The performance of the proposed method was validated by the use of P- and L-band single-baseline interferograms acquired by the airborne E-SAR system. The results showed that the phase artifacts in the initial differential interferograms can be greatly mitigated. In addition, the corrected interferograms acquired in the P- and L-bands were used to estimate the digital elevation model (DEM). After correction, the root-mean-square errors (RMSEs) of the two DEMs with respect to the light detection and ranging (LiDAR) DEM were 2.6 and 4.6 m, respectively, which are improvements of 48.0% and 63.8%. Furthermore, even in the case of low coherence, the proposed method can still work well.

A novel method based on the optimal normal matrix constraint and cross-iteration algorithm is proposed in this letter to estimate the forest height using the polarimetric interferometry synthetic aperture radar (PolInSAR) data. First, to avoid the null ground-to-volume ratio assumption of the three-stage method, we use the PolInSAR optimal normal matrix constraint method to find out the pure volume coherence. This method can also provide a more accurate initial value for the least-squares iteration. Second, the cross-iteration is used for the forest height inversion, which provides better selection of the best polarization channel and solves the ill-conditioned matrix in the traditional least-squares iteration algorithm. This new method is validated using the BioSAR 2008 P-band data. The results show that the proposed method achieves an average accuracy of 2.6 m, which is better than that of the three-stage inversion method [root-mean-square error (RMSE) = 5.89 m] and the 6-D nonlinear iteration method (RMSE = 4.42 m).

In this paper, a method for digital elevation model (DEM) extraction over forest areas from single-baseline interferometric synthetic aperture radar (InSAR) data is proposed. The main idea of this method is that some backscattering variations which are linked to the geometrical structures of forest occur during the radar acquisition. The time-frequency analysis is used to retrieve these variations by dividing the synthesized SAR image into multiple SAR images in the Fourier domain called sublook images. Then, by interferometry, the sublook images characterized by the same Doppler bandwidth and acquired from spatially separated locations at either end of a baseline are used to estimate the sublook coherences and the above backscattering variations are converted into the variations of sublook coherences. As a result, the number of InSAR observations can be increased. The sublook coherences are then interpreted by the two-layer vegetation scattering model and are assumed to follow a near-linear relationship in the complex plane. The ground phase can then be estimated by linear regression of the sublook coherences. The performance of the proposed method was validated by E-SAR L- and P-band SAR data acquired over coniferous and tropical forests. For the coniferous scenario, the underlying DEM estimated by the proposed method has a root-mean-square error (RMSE) of 4.39 m, which is slightly less accurate than the DEM (with an RMSE of 4.07 m) derived by the polarimetric line-fit (LF) method, but represents a significant improvement in DEM accuracy over the HH InSAR method. For the tropical scenario, the DEMs derived by the proposed method and the polarimetric LF method are closer to the ground surface than those derived by the HH InSAR method, and their mean ground height difference is 0.62 m. The two experiments confirm that it is feasible to extract a DEM by the proposed method, which has a comparable performance in DEM inversion to the polarimetric LF method and only requires single-polarization InSAR data.

In this paper, we demonstrate that the spaceborne dual-platform TerraSAR-X (TSX) and TanDEM-X (TDX) pursuit monostatic mode full polarimetric (full-pol) synthetic aperture radar (SAR) data with a time lag can be used to monitor maritime traffic. For single polarization (single-pol) SAR data, the performance of vessel velocity estimation is mainly determined by 2-D cross correlation of SAR intensity data. As the sea clutter is changing dynamically during the TSX/TDX data acquisition, the correlation between two dual-platform images decreases significantly. We may get unstable or incorrect estimations of vessel velocity, especially under a higher wind condition. For solving this problem, we propose an object-oriented polarimetric likelihood ratio test (PolLRT) method based on the complex Wishart distribution. The proposed method makes PolLRT statistics of the detected target pixels for eliminating the effect of varied sea clutter. Two pairs of full-pol SAR data sets covering the Strait of Gibraltar acquired by dual-platform TSX/TDX in pursuit monostatic mode with a time lag of approximately 10 s are selected for the experiments. The experimental results demonstrate that the proposed PolLRT method has a better performance than that of the classical normalized cross correlation (NCC) method with VV polarization SAR data and the mutual information (MI) method with full-pol SAR data. Specifically, under the lower wind condition, the correct estimation rate of the NCC, the MI, and the proposed PolLRT methods are 85.7%, 57.1%, and 100%, respectively; under the relatively higher wind condition, the correct estimation rate of the above three methods are 48.8%, 23.2%, and 90.1%, respectively.

This letter proposes a modified general polarimetric model-based decomposition method which includes a simplified Neumann volume scattering model (SNVSM). This is useful to avoid a known limitation in one of the state-of-the-art general model-based decomposition methods (i.e., Chen's method), which considers only four possible discrete volume scattering models. Two types of SNVSM, assuming horizontal or vertical dipoles, are derived from the Neumann volume scattering model. The resulting volume coherency matrix exhibits a continuous range of volume scattering models. In addition, this volume model covers both random and nonrandom volume cases, which are distinguished by a randomness parameter. Monte Carlo simulations are used to test this approach. The proposed method with SNVSM overall improves the final accuracy of estimated parameters in comparison with the original approach and shows consistency with another existing generalized volume scattering model (GVSM). In addition, results from two fully polarimetric C- and L-band AIRSAR images over San Francisco region show that the proposed method produces reasonably physical results and outperforms the traditional Y4R method. Finally, the differences obtained between SNVSM and GVSM in two building areas show the potential advantage of SNVSM in identifying more types of volume scenes than that of GVSM.

Compensating the residual motion error (RME) is very important in airborne interferometric synthetic aperture radar (InSAR). In this paper, the wavelet decomposition and polynomial fitting-based (WDPF) method is proposed for detecting and correcting the RME. Wavelet decomposition with root-mean-square error (RMSE) change ratio-based decomposition scale identification is used to detect the RME from the differential interferogram. Polynomial fitting in combination with robust estimation-based least squares is used to absorb the incidence-angle-dependent and topography-dependent components of the RME. A simulated experiment was conducted to test the proposed WDPF method. High-precision RME (with an RMSE of 0.0375 rad) was obtained, which can meet the requirements of InSAR. Real-data L- and P-band InSAR experiments were also performed to test the WDPF method. The results confirmed that the WDPF method can effectively correct the RME for the interferogram. The RMSE of the estimated digital elevation model (DEM) was reduced from 8.03 to 3.46 m and 8.18 to 3.10 m for the L- and P-band interferograms, respectively. Finally, the effects of the external DEM error and polarization on the RME calibration were investigated. The results indicated that the global InSAR DEM products can fulfill the requirement of differential interferogram generation for the WDPF method, and the multipolarization interferograms can help to reduce the effect of the topographic error phase on RME estimation.

This paper presents a wavelet decomposition-based correlation analysis (WDCA) method to correct atmospheric effects for interferometric synthetic aperture radar interferometry. The main idea is based on thea prioriknowledge that the atmospheric effects are independent of the polarizations. This provides the possibility to find the identical atmospheric phases (ATPs) from the two different polarimetric interferograms. To achieve this goal, differential interferometry is performed with different topographic data so that the obtained differential interferograms (D-Infs) have different topographic errors. A polynomial incorporating topographic information is then used to remove the orbit error phase. Thus, the ATPs are the only identical components in the obtained D-Infs. A forward wavelet transform is then utilized to perform multiresolution analysis for the two obtained D-Infs. After this, we apply correlation analysis to identify the wavelet coefficients attributed to the atmospheric effects. The corrected D-Infs are then obtained by down-weighting the wavelet coefficients during inverse wavelet transform. The performance of the WDCA method was tested with L-band ALOS-1 PALSAR dual-polarization SAR images acquired over Southern California and Qilian mountain test sites characterized by different topographic conditions. For the Southern California test site, two interferometric pairs with long and short baselines (750 and 50 m) were formulated. The results show that the WDCA method can work well for both of the interferometric pairs, and the root-mean-square errors (RMSEs) of the obtained DEMs with respect to the Shuttle Radar Topography Mission digital elevation model (DEM) are 7.86 and 13.78 m, and show a decrease of 34.7% and 80.4% for the long- and short-baseline cases, respectively. For the Qilian mountain test site, the corrected interferogram can provide a DEM with an RMSE of 19.73 m, which is an improvement of 22.3% with respect to the DEM containing the atmospheric signals. In addition, the above two experiments show that compared with the existing topographic information-based wavelet method, this approach can remove not only the topography-dependent ATP but also the turbulent ATP.

Interferometric synthetic aperture radar (InSAR) technique is a proven technique for measuring 3-D surface deformations by combining InSAR measurements from different techniques (i.e., differential InSAR, multiaperture InSAR, and pixel offset-tracking) and different tracks (i.e., ascending and descending) on a pixel-by-pixel basis. However, it is difficult to obtain the exact a priori variances or weights for such different kinds of InSAR measurements, resulting in inaccurate estimations of 3-D deformations. This paper proposes a method to retrieve 3-D deformations with InSAR by integrating the strain model and variance component estimation algorithm, which can exploit the spatial correlation of the adjacent points' deformations and produce accurate weights for multiple InSAR measurements. The proposed method is assessed with both simulated and real data sets. The results have shown that the proposed method can accurately measure 3-D surface deformations associated with geohazards, and even those occurring in a transient or short-term period (e.g., earthquake and volcanic eruption). In the case study of the 2007 eruption of Kilauea Volcano (Hawai'i), improvements of 51.2%, 22.4%, and 18.5% have been achieved for the derived east, north, and up displacements, respectively, with respect to those derived from the classical weighted least squares method.

This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries' SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications.

Measuring 3-D mining-induced displacements is essential to understand mining deformation mechanisms and assess mining-related geohazards. In our previous work, we proposed a method for estimating 3-D mining-induced large displacements with the surface deformation along the radar line-of-sight (LOS) direction derived from a single amplitude pair (SAP) of synthetic aperture radar (SAR) using the offset tracking (OT) procedure (hereafter referred to as OT-SAP). The OT-SAP method effectively reduces the strict requirements on SAR data of the previous OT-based methods for 3-D mining-induced displacement retrieval. However, OT-SAP is not robust to errors in the LOS deformation, due to the lack of redundant observations. In this paper, we present an alternative approach (hereafter called AOT-SAP) to OT-SAP. The AOT-SAP method involves estimating the 3-D mining-induced large displacements with OT-derived 2-D deformation observations along the LOS and azimuth directions from an SAP of SAR, instead of just the LOS deformation in the OT-SAP method. Consequently, more redundant observations are incorporated in the AOT-SAP method compared with the previous OT-SAP method. The theoretical analysis and experiments based on both simulated and real data sets suggest that AOT-SAP can effectively improve the accuracies of the estimated 3-D displacements compared with the OT-SAP-estimated ones.

The new generation of synthetic aperture radar (SAR) sensors provides us with an opportunity to match multiple high-resolution SAR images. Moreover, the multiple SAR image matching methods have recently gained a lot of attention due to the fact that they can obtain more accurate, better distributed, and more reliable matches than the stereo matching methods. In this letter, we present an improved multi-image matching method to simultaneously identify matches from multiple SAR amplitude images. The proposed method makes better use of the relationships between the pixels in the deformed correlation window and integrates geometric and radiometric information from multiple SAR images. Experiments on Chinese Academy of Surveying and Mapping Synthetic Aperture Radar (CASMSAR) data sets demonstrate that the improved multi-image matching method is capable of providing more accurate and better distributed matches, as well as offering a better multi-image matching solution in stereo-radargrammetry under the conditions of geometric and radiometric distortions, especially in low-texture areas.

A new approach is presented for mapping 3-D surface displacement caused by subsurface fluid volumetric change based on 1-D interferometric synthetic aperture radar (InSAR) line-of-sight measurements and surface deformation modeling. The relationship between surface deformation and source fluid volumetric change is modeled according to elastic half-space theory. A distinctive advantage of the proposed approach is that it effectively extends the capability of the sun-synchronous orbit side-looking synthetic aperture radar that has been essentially only able to measure 1-D displacements accurately or at most 2-D displacements when InSAR measurements from more than one orbit or platform are combined. Experimental studies are carried out with both simulated and real data sets to test the performance of the method. The results have demonstrated that the approach works very well.

Underground extraction can be roughly classified into three types, i.e., subcritical, critical, and supercritical extraction, in accordance with the geological conditions in the overburden and the geometry of mined-out areas. In 2016, we proposed an approach based on the interferometric synthetic aperture radar (InSAR) technique and the probability integral method (PIM) for the cost-effective prediction of 3-D mining-induced displacements (abbreviated as InSAR-PIM). Due to the inherent assumption of critical extraction in the PIM, the InSAR-PIM method performs well in predicting the 3-D displacements caused by critical and/or supercritical extraction, but poorly for subcritical extraction. In this paper, we first propose a generalized PIM (GPIM) by modifying the traditional PIM with a simplified Boltzmann function. We then replace the PIM of the InSAR-PIM with the proposed GPIM to develop an extension of InSAR-PIM (referred as to InSAR-GPIM). The InSAR-GPIM was tested in the Qianyingzi coal mining area, China. The results show that the InSAR-GPIM-predicted horizontal and vertical displacements caused by subcritical, critical, and supercritical extraction agree well with the in situ observations, with average root-mean-square errors of about 0.032 and 0.050 m, respectively. These accuracies represent improvements of 60.9% and 59% when compared with the accuracies predicted by the InSAR-PIM in the horizontal and vertical directions. The results indicate that the InSAR-GPIM is capable of accurately predicting 3-D mining-induced displacements under different extraction conditions (i.e., subcritical, critical, and supercritical extraction), and it performs much better than the InSAR-PIM in the case of subcritical extraction. It is therefore believed that InSAR-GPIM will have a wider scope of applications than the previous InSAR-PIM.

This paper presents a novel method for estimating the model parameters of the probability integral method (PIM) based on the line-of-sight deformation derived from the interferometric synthetic aperture radar. Then, it applies the settled PIM to forward predict the horizontal and vertical displacements induced by the extraction of a new working panel. The proposed method first constructed the functional relationship between the InSAR-derived LOS deformation and the model parameters of PIM. Subsequently, an improved genetic algorithm (GA), in which gross error elimination was imposed, was proposed, and used to estimate the model parameters of PIM with a large number of LOS deformation measurements. The estimated model parameters and PIM were then employed to forward predict the horizontal and vertical displacements induced by the extraction of a working panel. Simulated experiments show that the rmses of the predicted displacements along the up-down, west-east, and north-south directions are 1.5, 0.9, and 2.5 mm, respectively. Real data experiments over the Qianyingzi coal mining area of China indicate that the predicted displacements are highly consistent with those by field surveys, with rmses of 4.1 and 3 cm for the vertical and horizontal directions, respectively. These imply that the proposed approach can be a very promising tool for predicting the mining-induced displacements and will potentially contribute to the assessing and forecasting of possible geological hazards in the mining area.

In standard differential synthetic aperture radar interferometry, there could still be a residual tilt (orbital error) in the interferometric phase due to inaccurate baseline estimation. We demonstrated theoretically that the orbital errors were partially elevation dependent. On the basis of this, we introduced an elevation-dependent item to the conventional polynomial model to simulate, and therefore, compensate the orbital errors, as well as the small scale topographic and/or topography-related phase errors. Robust regression approach was suggested to determine the parameters of the proposed model. The model was validated with both synthetic and real ALOS PALSAR data of the Zhouqu, China mudslide. The synthetic test indicated that upon applying the refined model, the accuracies of phase measurements were improved by nearly two times, compared to those using conventional linear and quadratic models. The real data experiment indicated that after utilizing the refined model, the correlation between the interferogram and the digital elevation model of Zhouqu reduced to about 1/5 of those using linear and quadratic models. This demonstrates that the elevation-dependent phase components have been largely removed by the new model. More importantly, the interferogram corrected by the new model visibly disclosed the deformation area affected by the Zhouqu mudslide.

A Kalman-filter-based approach is presented for resolving 3-D surface displacements using multisensor, multitrack, and multitemporal interferometric synthetic aperture radar (SAR) measurements. Measurements from each interferogram are projected into the three reference directions and combined in the Kalman filter model with displacements determined from previous interferograms to produce updated displacement measurements. Both simulated and real data sets are used to test the proposed approach. It is found that the method works well when the measurement noise is low. The displacements in the north direction, however, are much lower in accuracy than those in the other two directions and even become unstable when the measurement noise is high due to the polar-orbiting imaging geometries of the current satellite SAR sensors.

Previous approaches that integrate interferometric synthetic aperture radar (InSAR) and GPS measurements for 3-D surface displacement mapping require statistically estimating the variances of the measurements to yield optimal results. We present a variance component estimation approach to weigh the InSAR and GPS measurements in deriving 3-D surface displacements. The approach exploits the observations themselves for determining the weighting scheme, and therefore the a priori information on the stochastic model of the observations is not required. This is of great importance as accurate knowledge on the stochastic model is often unavailable. The performance of the proposed method is validated with both simulated and real datasets.