Search Results (66)

Accurate estimation of young forest height is essential for assessing the carbon sequestration potential of vast Siberian boreal forests recovering from wildfires. Satellite radar interferometry, particularly PolInSAR, is a promising tool for this task. However, its application in winter conditions and over sparse young forests remains underexplored. This study proposes a novel method for estimating the height of sparse young pine (Pinus sylvestris) stands using fully polarimetric bistatic TerraSAR-X/TanDEM-X data acquired in winter. The method is based on an analysis of the multimodal distribution of the unwrapped interferometric phase of the surface scattering component, which was isolated via PolInSAR decomposition. We hypothesize that the phase centers correspond to the snow-covered ground (located between tree groups) and the rough surface formed by the upper layer of branches and needles (of the tree groups). The results demonstrate that the difference between the dominant modes of the surface scattering phase distribution correlates with the height of young trees. However, the measurable height difference is limited by the interferometric height of ambiguity. Furthermore, a temporal analysis of the phase and meteorological data revealed a strong correlation between sudden phase shifts and daytime temperature rises around 0 °C. This is interpreted as the formation of a layered snowpack structure with a dense ice crust. This study confirms the potential of X-band PolInSAR for monitoring the structure of young Siberian forests in winter but also highlights a significant limitation: the critical impact of snowpack metamorphism, particularly melt-freeze cycles, on the interferometric phase. The proposed method is only applicable to certain forest regeneration stages where tree height does not exceed the ambiguity limit and snow conditions are stable. Full article

Forest canopy height is essential for ecosystem process modeling and carbon stock assessment. However, most prediction approaches rely on sparse or interpolated LiDAR labels, leading to uncertainties in heterogeneous forests where laser footprints are limited or unevenly distributed. To address these issues, this study proposes a multi-source ensemble learning framework that uses airborne PolInSAR-derived continuous canopy height as training labels for accurate forest height prediction. The framework features two key innovations: (1) a hybrid baseline selection strategy (PROD+ECC) within the PolInSAR inversion, significantly improving the quality and stability of initial labels; (2) a dual-layer ensemble learning model that integrates machine learning and deep learning to interpret multi-source features (Landsat-8, GEDI, DEM, and kNDVI), enabling robust upscaling from local inversion to regional prediction. Independent validation in Gabon’s Akanda National Park achieved R² = 0.748 and RMSE = 5.873 m, reducing RMSE by 43.6% compared with existing global products. This framework mitigates sparse supervision and extrapolation bias, providing a scalable paradigm for high-accuracy canopy height mapping in complex tropical forests and offering an effective alternative to LiDAR-based approaches for global carbon assessment. Full article

Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R² = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article

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

Existing forest height estimation methods based on polarimetric interferometric synthetic aperture radar (PolInSAR) typically process each pixel independently, potentially introducing inconsistent estimates and additional decorrelation in the covariance matrix estimation. To address these limitations and effectively exploit the spatial context information, this paper proposes the first patch-based inversion method named joint pixel optimization inversion (JPO). By leveraging the smoothness and regularity of homogeneous pixels, a joint-pixel optimization problem is constructed, incorporating a first-order regularization on the ground phase. To solve the non-parallelizable problem of the alternating direction method of multipliers (ADMM), we devise a new parallelizable ADMM algorithm and prove its sublinear convergence. With the contextual information of neighboring pixels, JPO can provide more reliable forest height estimation and reduce the overestimation caused by additional decorrelation. The effectiveness of the proposed method is verified using spaceborne L-band repeat-pass SAOCOM acquisitions and LiDAR heights obtained from ICESat-2. Quantitative evaluations in forest height estimation show that the proposed method achieves a lower mean error (1.23 m) and RMSE (3.67 m) than the existing method (mean error: 3.09 m; RMSE: 4.70 m), demonstrating its improved reliability. Full article

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

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

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

The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term. Full article

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE). Full article

Rice is a primary food source, and height is a crucial parameter affecting its growth status. Consequently, high-precision, real-time monitoring of quantitative changes in crop height are required for improved crop production. Polarimetric interferometric SAR (PolInSAR) has both polarization and interferometric observation capabilities. Due to the short height of crops and rapid growth changes, the large spatial and short temporal baselines of PolInSAR data are essential for effective crop height inversion; however, relevant satellite-borne SAR and airborne SAR data are currently limited. This study presents a PolInSAR rice height inversion algorithm that uses the oriented volume over ground (OVoG) mode with PolInSAR 0-time and controllable spatial baseline data from a LAMP microwave anechoic chamber. Exploiting the advantages of microwave anechoic chamber measurement data, which includes continuous frequency bands, multiple baselines, and varied incidence angles, the influences of incident angles, baseline length, number of baselines, and baseline combinations are assessed. The highest accuracy rice plant height inversion has a root mean square deviation (RMSE) of 0.1093 m and is achieved with an incidence angle of 35–55°, baseline length of 0.25°, and three to four baselines. Furthermore, an imaging geometric equivalence analysis provides reliable foundation data to guide research into new earth observation SAR systems. The results indicate that, under simulated observations from the GF3 satellite at an altitude of 755 km, the optimal spatial baseline ranges for various frequency bands are as follows: L-band: 10.93–42.59 km; S-band: 4.10–15.97 km; C-band: 2.48–9.64 km; X-band: 1.36–5.32 km; Ku-band: 0.87–3.40 km. Notably, the measurement modes corresponding to the C, X, and Ku bands are ultimately identified as the most suitable for PolInSAR rice height inversion. Full article

In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the forest volume. This is based on the consideration that the forest height remains constant within neighboring pixels. Meanwhile, we also assume that the coefficients of the FL polynomials remain unchanged within neighboring pixels, except for the last polynomial coefficient. The idea of using neighboring pixels to increase the observations provides us with the possibility to obtain high-order FL polynomials. With this approach, it is possible to obtain a high-resolution vertical profile that is suitable for forest height estimation without losing too much spatial resolution. P-band PolInSAR data acquired in Mabounie in Gabon and Krycklan in Sweden were selected for testing the proposed algorithm. The results show that the algorithm outperforms the random volume over ground (RVoG) model by 18% and 16.7% in forest height estimation for the Mabounie and Krycklan study sites, respectively. Full article

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 $k_z$ 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 $k_z$ on PolInSAR inversion performance, this paper proposes the optimization of $k_z$ 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 $k_z$. Finally, by minimizing the simulated CRLB, the numerical optimum $k_z$ maps are provided for the system engineers to easily design the system parameters. Full article

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations $({\sigma }_{VV}^0$, ${\sigma }_{VH}^0$), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, ${\sigma }_{VH}^0$ of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola ${\sigma }_{VH}^0$ of Sentinel-1 offered higher performance (r = 0.88). Generally, ${\sigma }_{VV}^0$, ${\sigma }_{VH}^0$ performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture. Full article

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. Full article