The understory terrain is an essential component of forest vertical structure and ecosystem health, providing crucial insights for resource assessment and forestry surveys. This paper proposes a novel method for extracting understory terrain through forest backscattering power profiles and the Modified Cloth Simulation Filtering (MCSF) algorithm. It innovatively reconstructs SAR signals into a three-dimensional point cloud, eliminating side-lobe signals to reduce noise while only retaining the main lobe signals. The MCSF algorithm is subsequently utilized to extract ground and non-ground points based on the vertical distribution of the main lobe signals. The extracted ground points offer a more precise representation of actual terrain conditions. The feasibility of the method was validated utilizing airborne P-band multi-baseline SAR data obtained from the Saihanba test site in Hebei Province. The outcomes clearly indicate that our approach exhibits superior correlation (0.999) and a smaller root mean square error (3.07 m) in comparison to conventional methods when compared with the reference DEM.

Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise three-dimensional (3D) forest vertical structure information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3D reconstruction, which is time-consuming and low-efficiency for forest vertical structure mapping. To address these limitations, this paper proposes a novel micro-stack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P- and L-band. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improvingR2from 0.443 to 0.628, mean absolute error (MAE) from 0.779 m to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 m to 0.771 m. Additionally, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the L-band results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.

Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce three-dimensional images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to the elements such as orbit errors, which can seriously affect the quality of tomographic imaging and thus affect the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such problem, a novel method was developed and implemented in this paper, which includes two steps and remove parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach was applied to each track to remove the slowly but significantly spatially-varying part of the phase errors. Secondly, the modified autofocusing algorithm is proposed to correct the remaining phase errors, which adopted the two-dimensional image entropy as the optimization indicator, providing stronger robustness compared to traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as search engine. After the phase error correction, the forest height was extracted by identifying the upper and lower boundary of the forest from the corrected TomosAR profiles. Two P-band datasets obtained in north China are adopted to examine the proposed phase error correction method. Experimental results show that compared with traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.

In this paper, we studied the multi-feature combination estimation approach of forest above ground biomass (AGB) using X-band InSAR and P-band PolInSAR data. We focus on a crucial step of the estimation process, which is selection of the optimal feature combination. Firstly, the feature pool was acquired using multi-frequency SAR data, which includes optimized features (forest height and polarimetric interferometric feature) and original features (polarimetric features, intensity features, and texture features). Then, using machine learning method to select the optimal feature combination. Finally, the forest AGB was estimated based on multiple types of the features combination. The experimental results showed that the combination of optimized features with original features has the highest accuracy in forest AGB estimation, followed by the combination using only optimized features. The accuracy of forest AGB estimation is lower for the feature combination that does not include optimized features.

Net Primary Productivity (NPP) is an important part of the carbon cycle of terrestrial ecosystems. The technical advantages and huge potential of remote sensing technology in NPP estimation make it a hot spot in the research field. The vigorous development of many remote sensing fusion algorithms provides fine-resolution remote sensing data support for high-precision NPP dynamic monitoring. In recent years, the expansion of urban areas and climate change have had a great impact on the NPP of vegetation. In accordance with the requirements of large-scale and high-temporal-spatial resolution productivity assessment of urban area, we chose the northern Jiangsu area as the research area and uses three remote sensing data spatiotemporal fusion methods, STARFM, ESTARFM, and STDFA to blend Landsat and MODIS data. Three methods are compared in aspects of NDVI reconstruction ability in large-scale, highly heterogeneous regional application scenarios, the accuracy of NPP estimation through CASA model, and the ability of fine spatial description. The results of the study show that STDFA gets the highest correlation coefficient between its NDVI reconstruction results and MODIS products, which is 0.82. Correlation coefficient of STARFM and ESTARFM are 0.77 and 0.75, respectively. The STARFM is significantly lower in the NPP estimation results among the three methods, meanwhile STDFA performs best in our test site.

The errors of the theoretical model and the simplified SINC model in forest height inversion are analyzed. Simulation results show that the higher the tree height, the lower the kz value, the error between the theoretical and simplified SINC model is greater in inverting the forest height. When kz is 0.03 and forest height is 40 meters, the difference of the inversion height between theoretical and simplified SINC model can reach 1.5 meters. To address this problem, bisection iterative algorithm is utilized to eliminate or minimize the error between theoretical and simplified SINC forest height inversion model based on X-band airborne interferometric synthetic aperture radar (InSAR) coherence data. Lidar$H_{100}$CHM data are used to verify the effectiveness of bisection iterative algorithm. Compared with simplified SINC model, the performance of bisection iterative algorithm is better in retrieving the real value in the theoretical SINC model. The results show that the average height error of the bisection iterative algorithm is 0.5 m lower than that of the simplified SINC model in the inversion of forest parameters in the height range of 15–20 m.

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

In this article, a multilevel model (MLM) for forest height inversion is introduced and investigated using X-band single-pass interferometric synthetic aperture radar (InSAR) coherence data. Compared with the two-level model (TLM), as a generalized model, the MLM is more in line with the characteristics of forest structure and the scattering mechanism for X-band data. Based on the MLM model, three simplified MLM models were derived: TLMm, SINC, and MTND. An improved method of calculating coherence considering the assumptions of the MLM model is proposed. At two test sites, airborne (CASMSAR) and spaceborne (TanDEM-X) X-band single-pass InSAR data and LiDAR H100 data were used to verify the proposed new approach. The results showed that the MTND model can obtain more reliable and accurate inversion results compared to the SINC model and the TLMm. With airborne InSAR data, the MTND model’s highest accuracy was 82.56%, and the root mean square error (RMSE) was 2.4 m. With spaceborne InSAR data, the highest inversion accuracy of the MTND model was 73.55%, with an RMSE of 4.92 m. The inversion accuracy of forest height can be effectively improved by ensuring that the theoretical models and calculations of coherence obey the same assumptions.

Forest biomass plays an important role in restraining global warming and protecting ecosystem. With the development of ALOS SAR satellite and the improvement of its sensor performance, it plays a more and more important role in quantitative retrieval of forest biomass. In this paper, we used KNN-FIFS, KNN, SVR and Multiple Regression Models to invert the typical forest biomass of Genhe in Inner Mongolia and Yiliang in Yunnan based on ALOS1 PALSAR1 and ALOS2 PALSAR2. The results show that: 1) ALOS-2 PALSAR-2 has better inversion effect than ALOS-1 PALSAR-1;2) KNN-FIFS has better inversion accuracy than KNN, SVR and multiple regression function.

In this paper, we studied the nonparametric estimation approach of wheat biomass using C-band PolSAR data. We focus on a crucial step of the estimation process, which is feature combination selection. Firstly, the original feature pool was acquired using PolSAR data. Then, using genetic algorithm (GA) as searching engine we selected the feature combination which has the best performance in the estimation model. Finally, the wheat biomass was estimated based on the features combination selected by GA. The experimental results showed that the selected feature combination by GA can outperform the original feature pool and the feature combination selected based on Pearson correlation coefficient (PCC) between feature and biomass.

WCM (water cloud model) developed in 1978 was proved useful for vegetation parameters estimation. However, its no sense of polarization dependence limited its application with quad-polarimetric data. In this study, we developed polarimetric water cloud model (PWCM) and applied to quad-polarimetric GF-3 data for winter wheat biomass estimation. (HH-VV)/HV, (HH+W)/HV, HH/VV and VOL (volume scattering from Freeman Durden decomposition)/(ODD (odd scattering or surface scattering)+DBL (double bounce scattering)) were used as surface volume ratio parameters and provided in the developed PWCM. The results showed the effectiveness of developed PWCM for crop biomass inversion. Among all the parameters, (HH-VV)/HV showed best performance for winter wheat biomass inversion. The RMSE is 157.58 g/m2, the relative accuracy is 63.88.

In this study, a simple semi-empirical model based on random volume over ground model framework was used to estimate forest canopy height through single-pass TanDEM-X interferometric coherence data. Many studies have showed that SINC model could have good performance for forest canopy height inversion in relatively flat areas, this paper is aimed at assessing whether this method works well over complex terrain areas. The result showed that the inversion accuracy is influenced by local incidence angle and should be considered when mapping forest canopy height using SINC model.

Synthetic Aperture Radar (SAR) is able to image the Earth's surface in all weather conditions, regardless of whether it is day or night. It can provide high quality data for large-scale mapping. In recent years, the deep learning method, which has robust performance, can automatically extract hierarchic features of images with reduced manual participation, and is beneficial to the rapid implementation of large-scale mapping. Based on the theory of deep learning, we propose an end-to-end framework for the dense, pixel-wise classification of GF-3 dual-pol SAR imagery with convolutional highway unit network (U-net). U-net consists of a stack of learned convolutional highway unit that extract hierarchical contextual image features, and is a popular form of deep learning networks. A series of experiments show that the networks consider a large amount of context to provide fine-grained classification maps. The overall classification accuracy is 85.0%, Kappa is 0.803. The results are good enough to meet the needs of the mapping application.

In this study, we tested the performance of averaged Stokes parameters in rape biomass inversion over Radarsat-2 data. Five time series Radarsat-2 data were acquired and they covered the whole rape growth season. At each growth stage, 16 Stokes and its child parameters were extracted to analyze their sensitivity to rape biomass. When it comes to biomass inversion, random forest (RF) algorithm were chosen for the rape biomass inversion. The results showed that the total scattered intensity g0 has highest coherence with rape biomass during the whole growth cycle. R2 between them was 0.704. Other parameters including degree of polarization m, the degree of depolarization (1- m), the degree of linear polarization (Pl) and the linear polarization ratio (Uc) showed secondary higher coherence with R2 between 0.3 to 0.4. These parameters also showed higher importance for the parameter importance analysis with RF method. RF method showed potential performance for rape biomass inversion with R2 of 0.585 and RMSE of 71.86 g/m2.

In this paper, we studied the synergetic estimation approach of forest above ground biomass (AGB) based on the multidimensional SAR data (dual antenna X-band InSAR and P-band PolSAR data) that acquired by air-borne CASMSAR system of China. Firstly, the high-resolution DSM data of the experimental area was acquired from the X-InSAR data. Then, based on the filtered DSM, the terrain correction of P-PolSAR data and X-InSAR coherence was completed. Finally, forest AGB was estimated based on the characteristics of multi-dimensional SAR that after the terrain correction. The experimental results showed that the combined multi-dimensional SAR features can obtain higher estimation accuracy than the single-dimensional SAR features. Compared to only using P-PolSAR features and X-InSAR coherence feature, the accuracy of combined estimation approach was improved by 6.4% and 5.1%, respectively.

In this paper, we focus on the topographic effects of InSAR interferometric land use (ILU) images and a corresponding terrain correction method is proposed. Firstly, Effective scattering area correction and angular variation effect correction are performed for intensity information. Secondly, for the coherence information, a novel difference equation method of terrain correction based on the SINC volume decorrelation model is proposed. The Tandem-X/Terrasar-X InSAR data and SRTM DEM data are used to illustrate the method. The results showed that terrain effects not only existed in the intensity image, but also existed obviously in the coherence image and can been effectively removed by the proposed method. Finally, the interpretability of the ILU imagery treated in this manner is improved in comparison to the uncorrected case.

In this study, 5 compact polarimetric (CP) data were simulated from 5 consecutive fully polarimetric images, which covered the whole rape growth period. Four groups including 25 CP parameters were extracted from each CP image at different rape growth stages, respectively. With the analysis of the relationships between CP parameters and LAI, g3in stokes parameter group, Ucin stokes child parameters group, σRRin backscattering parameters group and PVαin decomposition parameters group were chose to inverse rape LAI. The results showed that, among these four CP parameters, PVαperformed best for rape LAI inversion, the determination coefficient R2 between inversion results and the ground truth data is 0.917, root mean square error (RMSE) is 0.36.

Two extraction models applied in estimating forest height and above-ground biomass (AGB) were developed using the X-band Interferometric Synthetic Aperture Radar (InSAR) data, which was acquired from the China airborne SAR system in 2013 covering part of forested area in northeast China. The models using multi-passes InSAR data for the estimations of forest height and AGB were introduced respectively with detailed ground true data derived from both forest plot data and airborne Light Detection And Ranging (LiDAR) data. The estimation results indicated that it is feasible to estimate forest height using X-band InSAR data without the external DEM. Moreover, the model using multi-passes InSAR data could descript the forest structure characteristics and can be used to estimate forest AGB faithfully.

Forest above ground biomass (AGB) retrieval by TomoSAR technology has been preliminary studied over the last two years. Recent experiments have demonstrated that the backscattered power in HV channel at 30-m layer have strong correlation with the forest AGB, and be successfully used to build the estimation model. However, much progress remains necessary to make the best of the vertical distribution of the backscattered power. In this work, we proposed a multivariate regression equation to refine the AGB estimation model. The experiments were carried out on TropiSAR campaign data over the site of Paracou, French Guiana. As expected, the results demonstrate the relevance of the proposed method.

The radiometric quality of polarimetric SAR (PolSAR) image is affected by terrain undulations due to 1) the variation of effective scattering area, 2) the variation of scattering mechanisms, and 3) the variation of polarization states. This paper proposed a three-stage terrain correction method of PolSAR data for the impact of above three aspects. PALSAR-2 PolSAR data and LiDAR forest AGB data are used to illustrate the method. The results showed that terrain effects can been effectively removed and the correlation between forest biomass and backscatter coefficient are improved after three-stage terrain correction. The HV polarization has the best correlation with the forest AGB (R = 0.75) and the correlation coefficient was increased about 0.3 compared with uncorrected case.

The objective of this paper is to derive DEM and DHM in tropical forests based on the technology of multi-baseline InSAR tomography, focusing on the requirement of the minimum number of flight tracks. The experiments were carried out on P-band HH polarization airborne multi-baseline InSAR data over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR 2009. Tomographic processing was carried out by Capon spectral estimation technique with three-track observations, subsequently two elevation values with respect to the relative maximum peaks were retrieved representing DEM and DSM respectively. As expected, the retrieved DEM and DHM are found agreements with LiDAR measurements.