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.

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.

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.

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.

The freeze/thaw (F/T) state of the soil is an essential indicator for permafrost monitoring. However, current soil F/T products with a coarse spatial resolution (>1 km) have limited their use on a fine scale. In this letter, a new approach integrating two microwave sensors [i.e., Sentinel-1 and advanced microwave scanning radiometer 2 (AMSR-2)] is developed to identify the soil F/T state at a spatial resolution of 10 m in the Qinghai-Tibet engineering corridor (QTEC). Using a linear regression model to integrate the coarse AMSR-2 data with the finer Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), the frozen frequency product at a 1-km resolution can be obtained. Then, the high-spatial-resolution F/T map based on Sentinel-1 synthetic aperture radar (SAR) time-series images can be produced using the threshold extracted from each pixel of frozen-frequency products. We tested soil F/T results via both visual and quantitative evaluations. The overall accuracy of the 10-m soil F/T map achieves 84.63% and 77.09% for ascending and descending orbits based on four meteorological stations, respectively.

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 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.

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).

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.

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.

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.