The TomoSAR technique has been applied to forest aboveground biomass (forest AGB) estimation studies, but existing studies make insufficient use of the forest structure information detected by TomoSAR. In this paper, we proposed a forest AGB estimation method based on TomoSAR backscattered power distribution law. The method uses the TomoSAR vertical profiles calculated by the Beamforming spectral analysis algorithm to extract the backscattered power for fitting in order to obtain the power curve. Then the distribution law was summarized by analyzing the variation of backscattered power distribution at different forest AGB levels. Based on the distribution law of backscattered power, two new forest AGB estimation features, BPC-4 and GVPR-19, are proposed. After modeling and validation, the results show that the forest AGB estimation model built with BPC-4 and GVPR-19 as variables can have better accuracy compared to the models built with the features proposed in previous studies.

In this letter, an improved three-component decomposition method for compact PolInSAR under π/4 mode is proposed. In the proposed algorithm, the volume scattering model is refined by the polarimetric interferometric similarity parameter and the volume scattering can be reasonably reduced. Airborne L-band ESAR PolInSAR data are used to simulate the compact PolInSAR data and evaluate the performance of the method. The experimental results demonstrate that the proposed method can be used to characterize the scattering mechanisms of various terrain types and is a complementary approach to the decomposition method for compact PolInSAR.

Currently, Multi-Temporal InSAR (MT-InSAR) is extensively employed to predict the trend of ground deformation. The deformation data acquired through MT-InSAR has been utilized as a single parameter in the model for predicting land deformation. Nevertheless, these models still necessitate enhancement in terms of their ability to generalize and accurately predict outcomes. In this paper, a combined Long Short Term Memory (C-LSTM) is proposed to combine groundwater level, rainfall, and surface deformation information from MT-InSAR. We assess the predictive accuracy of single-factor and multi-factor models. The results show that after feature combination optimization, the R2of the C-LSTM subsidence prediction model with multi-feature training improves the prediction results by 9.7%, 0.48%, and 21.82%, respectively, over the prediction results of the single-feature-factor model. By improving the C-LSTM’s feature factors, this method improves the accuracy of the forecast of ground subsidence change areas.

Tropospheric delay significantly hinders the accurate acquisition of high-precision surface deformation by Time series Interferometric Synthetic Aperture Radar (InSAR). The main challenge for current InSAR tropospheric delay estimation lies in effectively utilizing the time-dependent characteristics of the tropospheric delay for accurate atmospheric delay estimation. This paper develops a model to estimate the time-dependent and stochastic components of the delay based on periodic and random characteristics. The experiment demonstrates the effectiveness of the proposed method regardless of whether terrain-dependent delays dominate or random delays dominate at Danba-Xiaojin. The Std of the corrected decreases in 89/90 of the interferograms. The average and maximum improvement of Std is more than 40% and 80% respectively. From the time series, the proposed method can effectively suppress the periodic signals in both non-deformation and deformation regions and can obtain smoother time series. Overall, the proposed method outperforms the other four models for InSAR tropospheric delay correction.

In this letter, a multiple-component polarimetric decomposition method with refined volume scattering models is proposed. In the proposed algorithm, the volume scattering models under the assumption of reflection symmetry are refined by employing the orientation angles. In addition, wire scattering component is introduced to overcome the overestimation of volume scattering. ESAR data collected by the German Aerospace Center (DLR) are used to evaluate the performance of the method. The experimental results demonstrate that the proposed method can be used to characterize the scattering mechanisms of various terrain types and the overestimation of volume scattering can be effectively overcome.

Tropical natural forest plays an important role in environmental change and biodiversity researches. However, the complexity of structures and its cloudy and rainy environment make tropical natural forest classification difficult. Taking Hainan, China as study area, we conduct a tropical natural forest classification study by combining multi-temporal synthetic aperture radar (SAR) images collected from Sentinel-1 satellite and optical images collected from Landsat-8 satellite in this paper. The backscatter coefficient, spectrum information, digital elevation mod (DEM), temporal information offered by multiple remote sensing data have been analyzed to identify the evergreen and deciduous broad-leaved forest, evergreen coniferous forest, tropical monsoon forest, typical tropical rain forest and other forest types. In addition, a two-stage tropical forest classification strategy is proposed based on support vector machine (SVM) classifiers, namely the primary land cover type classification and tropical natural forest type classification in which the time-series backscattering information is used. Finally, the Hainan tropical forest mapping image is obtained based on the proposed classification strategy and the overall accuracy reaches to 90% based on field survey data. The results show the effectiveness of the classification strategy on tropical natural forest classification.

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.

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.