Polarimetric SAR Interferometry Research Articles

A general three-component polarimetric SAR interferometry target decomposition

In this study, a general three-component polarimetric SAR interferometry (PolInSAR) target decomposition framework is proposed by modifying the existing generalized surface, double-bounce, and volume models. The resulting general models are then compared to the original Freeman-Durden modeling strategy. Three types of generalized volume scattering models (generalized volume scattering model (GVSM), simplified Neumann volume scattering model (SNVSM) and simplified adaptive volume scattering model (SAVSM)) were employed. Simulated L-band PolInSAR data over deciduous and pine forest stands generated by PolSARpro and DLR P-band airborne PolInSAR data over a tropical forest area from the AfriSAR 2016 campaign were used for performance analysis. A qualitative comparison of the decomposition results shows that the three generalized volume scattering models generally deviate from the Freeman-Durden model, showing that the GVSM and SNVSM models have very similar results. In the case of airborne data over tropical forests, a tomographic synthetic aperture radar (TomoSAR) profile was also computed and used as a benchmark for comparison with the phase-center profiles of all four volume-scattering components. Not only do the GVSM and SNVSM models exhibit similar results between them (as with simulated data), but also a better match with the HV TomoSAR profile.

Uncertainty analysis for forest height inversion using L / P band PolInSAR datasets and RVoG model over kryclan forest site

Forest height, as a measure of the quantity and quality of forest resources, plays a significant role in the study of the ecological functions performed by forests. Although the polarimetric synthetic aperture radar interferometry (PolInSAR) technique has evolved as a potent method for forest height inversion, uncertainties still exist in the process of estimating forest height, and the uncertainties in predicted forest height directly lead into the uncertainty of terrestrial carbon stock calculation results. In this study, we took the Random Volume over Ground (RVoG) model as likelihood function and constructed a hierarchical Bayesian framework to calculate and reduce the uncertainty of forest height inversion using L / P band PolInSAR airborne data via RVoG model. Uncertainties resulted from five canopy types and three forest densities were analyzed, respectively. The results showed that among the five different canopy types, L band has the highest prediction accuracy in pure coniferous canopy with Acc. = 0.90. The uncertainty is extremely low for pure forest, with the ratio of uncertainty values of 0.09 for L band and 0.15 for the P band in pure coniferous canopy, and uncertainty values of 0.16 for L band and 0.11 for P band in pure broadleaf canopy, respectively. Furthermore, when the forest density is between 300 and 600 stems/ha, the ratio of uncertainties for the L band is 0.27, whereas the P band is 0.24. As forest density increases, the uncertainty in forest height estimates decreases for both bands. The changes in canopy types and forest density affect forest height estimation uncertainties obviously, the effects are different at each frequency. The forest height inversion accuracy of the L band in pure coniferous canopy surpasses that in other canopy types, with the lowest uncertainty. P band performed well in broadleaf canopy forest height inversion. The inversion uncertainties at both frequencies decrease with increase of forest densities.

A new snow depth retrieval method by improved hybrid DEM differencing and coherence amplitude algorithm for PolInSAR

Snow depth is a critical parameter for measuring the variations of the snowpack, and its measurement also influences the accuracy of hydrological and climate models. Polarimetric SAR interferometry (PolInSAR) technology combines the capabilities of Polarimetric SAR (PolSAR) and Interferometric SAR (InSAR), and has significant potential for retrieving snow depth. The hybrid DEM differencing and coherence amplitude algorithm (HDCA) is a robust algorithm for PolInSAR data, but encounters such problems as uncertainty in finding the ground scattering phase center, impure volume decorrelation, and the lack of consideration of the dense medium characteristic of snow. In this study, the HDCA is improved by optimizing the phase and decorrelation parameters and by considering the dense medium characteristic of snow. First, the Freeman-Duren decomposition method is put forward to extract the ground and volume scattering phase centers from the snow. Second, the coherence region boundary estimation method is proposed to optimize the volume decorrelation. Following this, the parameter assumed by the original method in free space is adjusted by considering the dense medium characteristic of snow. Finally, the Ku band UAV SAR data were applied to validate the new method, and in-situ data were used to assess its accuracy. The correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method are 0.93, 3.59 cm, and 2.98 cm, respectively, and were significantly superior to those of the original HDCA method of 0.74, 7.44 cm, and 6.04 cm, respectively.

Optimization of the Vertical Wavenumber for PolInSAR Inversion Performance Based on Numerical CRLB Analysis

A number of advanced SAR missions have been planned to launch, which can operate in fully polarimetric SAR interferometry mode to acquire structural parameters of global forests. Before the PolInSAR mission, the system configuration of vertical wavenumber kz must be carefully designed because it has a significant impact on the inversion performance. To minimize the estimation error of forest height caused by the system error from the future PolInSAR campaigns, it is valuable for us to optimize the vertical wavenumber. To quantitatively investigate the impact of kz on PolInSAR inversion performance, this paper proposes the optimization of kz based on the Cramér–Rao Lower Bound (CRLB) analysis. Extensive numerical CRLB simulations have been conducted to analyze the impact of several parameters, including extinction level, incident angle, and system decorrelation, etc., on the optimum kz. Finally, by minimizing the simulated CRLB, the numerical optimum kz maps are provided for the system engineers to easily design the system parameters.

A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives

Forests cover approximately one-third of the Earth’s land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments.

Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes

Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) based forest height estimation for ecosystem monitoring and management has been developing rapidly in recent years. Spaceborne Pol-InSAR systems with long temporal baselines of several days always lead to severe temporal decorrelation, which can cause a forest height overestimation error. However, most forest height estimation studies have not considered the change in dielectric property as a factor that may cause temporal decorrelation with a long temporal baseline. Therefore, it is necessary to propose a new method that considers dielectric fluctuations and random motions of scattering elements to compensate for the temporal decorrelation effect. The lack of ground truth for forest canopy also needs a solution. Unsupervised methods could be a solution because they do not require the use of true values of tree heights as the ground truth to calculate their estimation accuracies. This paper aims to present an unsupervised forest height estimation method called Dielectric Fluctuation and Random Motion over Ground (DF-RMoG) to improve accuracy by considering the dielectric fluctuations and random motions. Its performance is investigated using Advanced Land Observing Satellite (ALOS)-1 Pol-InSAR data acquired over a German forest site with temporal intervals of 46 and 92 days. The authors analyze the relationship between forest height and different parameters with DF-RMoG and conventional models. Compared with conventional models, the proposed DF-RMoG model significantly reduces the overestimation error due to temporal decorrelation in forest height estimation according to its lowest average forest height.

Three-stage inversion improvement for forest height estimation using dual-PolInSAR data

This paper addresses an algorithm for forest height estimation using single frequency and single baseline dual polarization radar interferometry data. The proposed method is based on a physical two layer volume over ground model and is represented by using polarimetric synthetic aperture radar interferometry (PolInSAR) technique. The presented algorithm provides the opportunity to take advantages of the dual polarimetric data, i.e, better spatial resolution and wider swath width, in comparison with the full polarimetric data, in forest height estimation application. In this research, a polarimetric optimization method is utilized to choose the optimum volume polarization basis in order to improve the results of the three-stage inversion algorithm. For the performance analysis of the proposed approach, the L-band ESAR data of the European Space Agency from BioSAR 2007 campaign (ESA) which is acquired over the Remningstorp test site in southern Sweden, is employed. The experimental result shows the dual PolInSAR HH/HV data capability in the forest height estimation without decreasing the accuracy of the result compared with the full polarimetric data. The suggested method leads to the average root mean square error (RMSE) of 4.39 m and the determination of coefficient of 0.66 in the forest height estimation in 15 predetermined stands in comparison with the LiDAR reference heights.

An Improved Forest Height Model Using L-Band Single-Baseline Polarimetric InSAR Data for Various Forest Densities

Forest density affects the inversion of forest height by influencing the penetration and attenuation of synthetic aperture radar (SAR) signals. Traditional forest height inversion methods often fail in low-density forest areas. Based on L-band single-baseline polarimetric SAR interferometry (PolInSAR) simulation data and the BioSAR 2008 data, we proposed a forest height optimization model at the stand scale suitable for various forest densities. This optimization model took into account shortcomings of the three-stage inversion method by employing height errors to represent the mean penetration depth and SINC inversion method. The relationships between forest density and extinction coefficient, penetration depth, phase, and magnitude were also discussed. In the simulated data, the inversion height established by the optimization method was 17.35 m, while the RMSE value was 3.01 m when the forest density was 100 stems/ha. This addressed the drawbacks of the conventional techniques including failing at low forest density. In the real data, the maximum RMSE of the optimization method was 2.17 m as the stand density increased from 628.66 stems/ha to 1330.54 stems/ha, showing the effectiveness and robustness of the optimization model in overcoming the influence of stand density on the inversion process in realistic scenarios. This study overcame the stand density restriction on L-band single baseline PolInSAR data for forest height estimation and offered a reference for algorithm selection and optimization. The technique is expected to be extended from the stand scale to a larger area for forest ecosystem monitoring and management.

Improved Model-Based Forest Height Inversion Using Airborne L-Band Repeat-Pass Dual-Baseline Pol-InSAR Data

This paper proposes an improved model-based forest height inversion method for airborne L-band dual-baseline repeat-pass polarimetric synthetic aperture radar interferometry (PolInSAR) collections. A two-layer physical model with various volumetric scattering attenuation and dynamic motion properties is first designed based on the traditional Random Motion over Ground (RMoG) model. Related PolInSAR coherence functions with both volumetric and temporal decorrelations incorporated are derived, where the impacts of homogenous and heterogeneous attenuation and dynamic motion properties on the performance of forest height inversion were investigated by the Linear Volume Attenuation (LVA), Quadratic Volume Attenuation (QVA), Linear Volume Motion (LVM), and Quadratic Volume Motion (QVM) depictions in the volume layer. Dual-baseline PolInSAR data were acquired to increase the degree of freedom (DOF) of the coherence observations and thereby provide extra constraints on the forest parameters to address the underdetermined problem. The experiments were carried out on a boreal forest in Canada and a tropical one in Gabon, where physical models with LVA + QVM (RMSE: 3.56 m) and QVA + LVM (RMSE: 6.83 m) exhibited better performances on the forest height inversion over the boreal and tropical forest sites, respectively. To leverage the advantages of LVA, QVA, LVM, and QVM, a pixel-wise optimization strategy was used to obtain the best forest height inversion performance for the range of attenuation and motion profiles considered. This pixel-wise optimization surpasses the best-performing single model and achieves forest height inversion results with an RMSE of 3.21 m in the boreal forest site and an RMSE of 6.48 m in the tropical forest site.

Use of TanDEM-X PolInSAR for canopy height retrieval over tropical forests in the Western Ghats, India

Canopy height is a critical parameter in quantifying the vertical structure of forests. Polarimetric SAR Interferometry (PolInSAR) is a radar remote sensing technique that makes use of polarimetric separation of scattering phase centers obtained from interferometry to estimate height. This article discusses the potential of the X-band PolInSAR pair for forest height retrieval over tropical forests in the Western ghats. A total of 19 fully polarimetric datasets with various spatial baselines acquired from November 2015 to February 2016 in bistatic mode are utilized in this study. After compensating for all possible non-volumetric decorrelations in the data-sets, the remaining volume decorrelation is modeled using a Random Volume Over Ground (RVoG) model to invert height from PolInSAR data. A modified three-stage algorithm developed by Cloude and Papathanassiou (2003) is adopted for height inversion. PolInSAR derived heights were cross-validated against reference height data measured during a field survey conducted in March 2019. RMSE values of all TerraSAR-X/TanDEM-X PolInSAR heights with respect to field measured heights range from 3.3 to 13.8 m and the correlation coefficient r2 varies between 0.16 and 0.79. The results suggest that the use of a dataset with optimal wavenumber can improve the tree height estimation process. The best performance was achieved for the dataset acquired on 11 December 2015 with RMSE = 3.4 m and r2 = 0.79. Furthermore, the effects of parameters such as angle of incidence, precipitation, and forest biomass on height inversion accuracy are assessed. A large-scale Shimoga Forest height map was generated using multiple TanDEM-X acquisitions with the best correlation results. To improve the accuracy of the height estimation, a merged height approach is explored. The best height estimates among all PolInSAR estimates for a given field plot are chosen in this regard. The merged height approach gave rise to an improved inversion accuracy with RMSE = 1.9 m and r2 = 0.92. The primary objective of this study was to demonstrate the ability of spaceborne X-band data to estimate height with maximum accuracy over natural forests in India, in which height retrieval research has seldom been done.

Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped-random volume over ground, the terrain-compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L-Band Synthetic aperture Radar (PALSAR) dataset.

Combining L-band Synthetic Aperture Radar backscatter and TanDEM-X canopy height for forest aboveground biomass estimation

Synthetic aperture radar (SAR) backscatter based above-ground biomass (AGB) estimates are limited by the saturation of the backscatter-AGB curve. This work explores the potential of combining backscatter with polarimetric SAR interferometry (PolInSAR) estimated forest stand height for improved AGB estimation. The models combining L-band backscatter and TanDEM-X height are compared with established backscatter based models. The models are also temporally cross-validated, i.e., trained on one acquisition date and validated for other dates. It is observed that with the input of height, the combined models perform significantly better than backscatter based models, with an improvement in root mean square error (RMSE) between 19% and 46%. The model utilizing HV-polarized backscatter and TanDEM-X PolInSAR height provide the best case AGB inversion with an R2 = 0.78 and an RMSE of 27.1 Mg/ha or 22% of mean AGB. The results demonstrate the potential of the synergistic combination of L-band PolSAR (backscatter) and X-band PolInSAR (height) products for AGB mapping over a tropical forest range in India.

Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

Abstract Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR). We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals. We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.

A Modified Two-Steps Three-Stage Inversion Algorithm for Forest Height Inversion Using Single-Baseline L-Band PolInSAR Data

Forest height inversion with Polarimetric SAR Interferometry (PolInSAR) has become a research hotspot in the field of radar remote sensing. In this paper, we systematically studied a modified two-step, three-stage inversion simulating the L-band (L = 23 cm) full-polarization interferometric SAR data with an average forest height of 18 m using ESA PolSARpro-SIM software. We applied this method to E-SAR L-band single-baseline full PolInSAR data in 2003. In the first step, we modified the three-stage inversion algorithm based on phase diversity (PD)/maximum coherence difference (MCD) coherence optimization methods, corresponding to PD, MCD, respectively. In the second step, we introduced the coherence amplitude inversion term and modified the fixed weight to the variable of ε times the ground scattering ratio, which improved the accuracy of forest height inversion. The mean of forest height inversion by the HV method was the lowest (15.83 m) and the RMSE was the largest (4.80 m). The PD method was superior to the HV method with RMSE (4.60 m). The MCD method was slightly better than using the PD method with the smallest RMSE (4.43 m). After adding the coherence amplitude term, the RMSE was improved by 0.15 m, 0.14 m, and 0.08 m, respectively. The smallest RMSE was obtained by MCD, followed by the PD and HV methods. Although the robustness of the forest height inversion algorithm was reduced, the underestimation was improved and the RMSE was reduced. Due to the complexity of the real SAR E-SAR L-band single-baseline full PolInSAR data and the small sample sizes, the three-stage inversion methods based on coherent optimization were lower than the three-stage in-version method. After introducing the coherent magnitude term, the overestimation of the forest height was significantly weakened in HVWeight, PDweight, and MCDWeight, and PDWeight was optimal. The modified two-step, three-stage inversion algorithm had significant effects in alleviating forest height underestimation and overestimation, improving the accuracy of forest height inversion, and laying a foundation for the upcoming L-band SAR satellite generation, new SAR and LIDAR systems combined with RPAs (remotely piloted aircrafts)/UAVs (unmanned aerial vehicles) for small areas mapping initiatives, and promoting the depth and breadth of the SAR applications of the new SAR system.

PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data

This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m).

A Novel Iterative Reweighted Method for Forest Height Inversion Using Multibaseline PolInSAR Data

Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline PolInSAR joint inversion of forest height. First, we establish a better stochastic model and weight function to obtain more accurate parameter estimation considering the relationship between vertical wavenumber and forest height. Second, according to the proposed inversion criterion, the baseline observations with unsuitable interferometric geometry are regarded as gross errors and eliminated through reweighted iteration. Finally, we select airborne P-band synthetic aperture radar (SAR) data collected by the F-SAR system during the AfriSAR 2016 campaign for experimental validation. The experimental results show that using initial iteration values obtained by three optimal baseline selection methods, the accuracy of the proposed method achieves 4.47, 4.46, and 4.24 m, which is about 34.74%, 32.32%, and 33.23% higher than those of the existing multibaseline joint inversion method [root mean square error (RMSE) = 6.85, 6.59, and 6.35 m], respectively.

Application of the Trace Coherence to HH-VV PolInSAR TanDEM-X Data for Vegetation Height Estimation

This article investigates, for the first time, the inclusion of the operator Trace Coherence (TrCoh) in polarimetric and interferometric synthetic aperture radar (SAR) methodologies for the estimation of biophysical parameters of vegetation. A modified inversion algorithm based on the well-known Random Volume over Ground (RVoG) model, which employs the TrCoh, is described and evaluated. In this regard, a different set of coherence extrema is used as input for the retrieval stage. In addition, the proposed methodology improves the inversion algorithm by employing analytical solutions rather than approximations. Validation is carried out exploiting single-pass HH-VV bistatic TanDEM-X data, together with reference data acquired over a paddy rice area in Spain. The added value of the TrCoh and the convenience of the use of analytical solutions are assessed by comparing with the conventional polarimetric SAR interferometry (PolInSAR) algorithm. Results demonstrate that the modified proposed methodology is computationally more effective than current methods on this dataset. For the same scene, the steps required for inversion are computed in 6 min with the conventional method, while it only takes 6 s with the proposed approach. Moreover, vegetation height estimates exhibit a higher accuracy with the proposed method in all fields under evaluation. The root-mean-squared error reached with the modified method improves by 7 cm with respect to the conventional algorithm.

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Abstract. Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China

Spaceborne polarimetric synthetic aperture radar interferometry (PolInSAR) has the potential to deal with large-scale forest height inversion. However, the inversion is influenced by strong temporal decorrelation interference resulting from a large temporal baseline. Additionally, the forest canopy induces phase errors, while the smaller vertical wavenumber (kz) enhances the sensitivity of the inversion to temporal decorrelation, which limits the efficiency in forest height inversion. This research is based on the random volume over ground (RVoG) model and follows the assumptions of the three-stage inversion method, to quantify the impact of repeat-pass spaceborne PolInSAR temporal decorrelation on the relative error of retrieval height, and develop a semi-empirical improved inversion model, using ground data to eliminate the interference of coherence and phase error caused by temporal decorrelation. Forest height inversion for temperate forest in northern China was conducted using repeat-pass spaceborne L-band ALOS2 PALSAR data, and was further verified using ground measurement data. The correction of temporal decorrelation using the improved model provided robust inversion for mixed conifer-broad forest height retrieval as it addressed the over-sensitivity to temporal decorrelation resulting from the inappropriate kz value. The method performed height inversion using interferometric data with temporal baselines ranging from 14 to 70 days and vertical wavenumbers ranging from 0.015 to 0.021 rad/m. The R2 and RMSE reached 0.8126 and 2.3125 m, respectively.

Forest height estimation by means of compact PolInSAR data

Forest monitoring and management have become a necessity to restrain deforestation and human encroachment. In this respect, the polarimetric synthetic aperture radar interferometry (PolInSAR) techniques have demonstrated their capability for this purpose. Recently, a new mode of SAR systems called compact polarimetry (CP) has been proposed, which can overcome some limitations of fully polarimetric (FP) SAR system. This study aims to illustrate the capability of compact PolInSAR (C-PolInSAR) data for forest height estimation. For this purpose, a novel method based on random volume over ground (RVoG) model and PolInSAR decomposition technique is proposed. The main motivation of the proposed method is to directly and simultaneously estimate the ground only and volume only coherences from the C-PolInSAR data without the need for any prior information. The proposed method was verified with the L-band single-baseline PolInSAR data acquired over the Remningstorp test site, southern Sweden. In addition, light detection and ranging (LiDAR) data were used to vertify the results. The experimental results demonstrated that the C-PolInSAR data i.e., pi/4-PolInSAR and circular transmit-linear receive PolInSAR (CTLR-PolInSAR) data could provide a significant accuracy for forest height estimation compared to the fully PolInSAR (F-PolInSAR) data by 4.2 m and 4.49 m improvements, respectively.