Polarimetric SAR Interferometry Research Articles

Canopy height estimation with TanDEM-X in temperate and boreal forests

Various semi-empirical models for linking PolInSAR data (polarimetric synthetic aperture radar interferometry) to canopy height of vegetation exist. However, only single-polarized data were used during the TanDEM-X mission in order to create a global digital elevation model (DEM). Therefore, simplifications of the semi-empirical models have to be applied to use the PolInSAR models for canopy height estimation with single-polarized TanDEM-X data. We extracted the volume coherence from TanDEM-X acquisitions and used a linear as well as a sinc model for the estimation of canopy height, which are based on the semi-empirical Random Volume over Ground model (RVoG). Both, the linear as well as the sinc model, were applied in temperate forests of Germany and boreal forests of Canada. The estimated canopy height was validated with LiDAR based canopy height models. In general, the sinc model resulted in higher coefficients of determination R2 from 0.08 to 0.64 and lower root mean squared errors (RMSE) between 4.8 m and 12.5 m compared to the linear model with R2 values between 0.08 and 0.62 (RMSE = 5.4 m to 13.5 m). Higher accuracies were generally achieved in winter and with higher height of ambiguity.

Assessing the performance of indicators resulting from three-component Freeman–Durden polarimetric SAR interferometry decomposition at P-and L-band in estimating tropical forest aboveground biomass

ABSTRACT Aboveground Biomass (AGB) estimation performance in a dense tropical forest using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data, at P-and L-band, is evaluated. At high levels of biomass, the backscatter saturation effect leads to a low sensitivity of the backscattered intensity for biomass. This study tries to overcome this problem based on decomposition of PolInSAR data into ground, ground-trunk, and volume components to retrieve the vertical structure information of the forest and polarimetric characteristics of layers. Some sensitive parameters, which extracted from Polarimetric Synthetic Aperture Radar (PolSAR) and PolInSAR data are chosen. Then, many sets of these features are used for assessing biomass estimation using Linear Regression (LR) and Support Vector Machine (SVM) regression models. The data analyzed in this paper are from the P-and L-band airborne dataset acquired by Office National d’Études et de Recherches Aérospatiales (ONERA) over French Guiana in 2009, in the frame of the European Space Agency (ESA) campaign, TropiSAR. The average forest biomass is 374.54 t ha−1 and goes up to 503 t ha−1 for the in-situ plots. The results display that the indicator of double-bounce (C DB) contribution matrix, extracted from PolInSAR data decomposition, is the highest correlated feature to AGB, at both P-and L-band. The combination of PolInSAR indicators improved the Root-Mean-Squared Error (RMSE) values up to 24.76 t ha−1 at P-band and 18.02 t ha−1 at L-band. At the best state, AGB estimation RMSE reduces to 40.95 t ha−1 (12.73%) in simultaneous use of the features derived from PolSAR and PolInSAR data, at P-band. SVM produces a more realistic biomass value than LR. However, cross-validated AGB RMSE of two regression models is close to each other.

Impact of Backscatter in Pol-InSAR Forest Height Retrieval Based on the Multimodel Random Forest Algorithm

For forest with complex structure, the vertical structure backscatter is influenced by a combination of factors, including the frequencies of the radar waves and the forest biophysical parameters (i.e., density, species). The backscatter-induced error is thus a critical element in limiting the accuracy of polarimetric synthetic aperture radar interferometry (Pol-InSAR) forest height inversion based on a single model. It is, therefore, necessary to select the optimal backscatter profile from the multiple possible solutions in each pixel of the test site. In this letter, the impacts of backscatter in forest height estimation based on the models of random volume over ground (RVoG) ( $\sigma >0$ ), RVoG ( $\sigma ), and Gaussian vertical backscatter (GVB) were investigated in the complex plane, and then with the combined use of Pol-InSAR and light detection and ranging (LiDAR), a random forest (RF) classifier is trained to obtain the optimal backscatter function and Pol-InSAR forest height from the results based on the different models in each pixel. The proposed method was tested with single- and multi baseline Pol-InSAR data in the P-band, and the root-mean-square errors (RMSEs) of the proposed approach were 2.85 and 2.69 m, respectively, which represented average improvements of 20.6% and 17.7% over the optimal single-model inversion.

Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image

Polarimetric SAR interferometry (PolInSAR) is an efficient remote sensing technique that allows to extract forest heights by means of model-based inversion. Recently, there have been plenty of researches on the retrieval of vegetation parameters by single frequency single baseline PolInSAR, such as the ESPRIT method and three-stage inversion method. However, these methods have several shortcomings which tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. In order to overcome these shortcomings, an improved three-component decomposition technique using PolInSAR image is proposed in this paper. By means of coherence set and a Newton-Raphson method, the proposed method improves the accuracy of forest height estimation. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and L-band PolInSAR image pair of Tien-Shan test site which is acquired by the SIR-C/X-SAR system.

An improved adaptive decomposition method for forest parameters estimation using polarimetric SAR interferometry image

ABSTRACT Forest parameters estimation using polarimetric synthetic aperture radar interferometry (PolInSAR) images is one of the greatest interests in remote sensing applications. Applying the model-based decomposition concept to PolInSAR data opened a new way for forest parameters estimation. However, the method tends to underestimate the forest height due to reflection symmetry assumption and required the numerical solution of nonlinear equation system. In order to overcome these limitations, an improved adaptive decomposition technique with PolInSAR data is proposed. In this approach, an accurate topographical phase and asymmetry volume scattering model are applied to the model-based decomposition technique for polarimetric SAR interferometry image. The accurate topographical phase is first estimated and then used as the initial input parameter to our numerical method. This approach is not only avoiding large error generated by the constant topographical phase in fluctuating forest areas but also improve the accuracy of forest height estimation and the magnitude associated with each mechanism. The performance of proposed method is demonstrated with simulated data from PolSARproSim software and SIR-C/X-SAR spaceborne PolInSAR images over the Tien-Shan areas, China. Experimental results indicate that forest parameters could be effectively extracted by proposed method.

Forest Height Estimation Using Multibaseline PolInSAR and Sparse Lidar Data Fusion

We demonstrate a method using lidar data fusion to improve the forest height estimation accuracy of multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR). Compared to single-baseline PolInSAR, multibaseline PolInSAR allows forest canopy height to be estimated more accurately across a wider range of height values. However, to arrive at a single forest height estimate, the estimates from the multiple baselines must be selected or weighted. A number of approaches to selecting between baselines have been proposed in the literature, but they are generally based on simple metrics of the PolInSAR data and do not necessarily capture the full range of characteristics that make one baseline produce more accurate forest height estimates than another. We solve this problem by treating baseline selection as a supervised classification problem that can be trained using a small amount of sparse lidar data located within the PolInSAR coverage area. We train a support vector machine classifier using a variety of coarse lidar sample spacings of 250 m and greater, to demonstrate that data from future spaceborne lidar missions will be sufficient for this purpose. We demonstrate results for multiple study areas in the country of Gabon using data collected by NASA's uninhabited aerial vehicle synthetic aperture radar and land, vegetation, and ice sensor lidar. The use of lidar fusion for PolInSAR baseline selection yields improved results compared to standard baseline selection methods, and further demonstrates the strong potential of PolInSAR and lidar fusion for remote sensing of forests.

N-SAR: A New Multichannel Multimode Polarimetric Airborne SAR

Recent years have seen a surging interest in several novel tools pertaining to SAR, such as multichannel high-resolution and wide-swath (HRWS) SAR, multibaseline interferometric SAR (InSAR), multisubband SAR, polarimetric SAR (PolSAR), and polarimetric SAR interferometry (PolInSAR). We believe that these new approaches to SAR have valuable scientific applications. Here, we present a new experimental airborne SAR system named “N-SAR” (SAR of the Nanjing Research Institute of Electronic Technology) that can fulfill new requirements and is scalable to allow rapid development of modern SARs. As a dual-antenna airborne SAR system, the N-SAR system will be used to test new technologies and signal processing algorithms such as PolSAR and PolInSAR, multibaseline SAR interferometry, multichannel multichannel HRWS SAR/InSAR, and SAR ground moving target indicator. It will play a key role in evaluating the performance of current engineering-oriented SAR systems by using several new operational modes for scientific purposes. In this paper, we provide a conceptual description of the general system design features, instrument design, and capabilities of the N-SAR system. To meet the requirements of different experiments, a novel operational mode, named the alternating bistatic multipolarized mode based on the N-SAR system, is presented. A series of flight tests that started in April 2017 and will be carried out over the next few years are shown. Several preliminary experimental results pertaining to the calibration of PolSAR, multichannel SAR imaging, and interferometry are presented in this paper as an early validation of the capabilities of the N-SAR system.

Compact Polarimetric SAR Interferometry Target Decomposition With the Freeman–Durden Method

In this paper, we apply the Freeman–Durden decomposition method to the compact polarimetric synthetic aperture radar (SAR) interferometry (C-PolInSAR) data. The complex cross-correlation matrix of the single-baseline C-PolInSAR is decomposed into three $2\times 2$ covariance matrices, which correspond to the surface, the dihedral and the volume scattering, respectively. Under reasonable assumptions, each model of the three scattering mechanisms are derived and formulated in term of the $\pi /4$ C-PolInSAR mode and the circular polarization transmitting and linear polarization reception (CTLR) C-PolInSAR mode. Prior to the target decomposition, detailed steps to retrieve the volume scattering amplitude are given, which are remarkably different for the $\pi /4$ mode and the CTLR mode. In addition, the topographic phase is also retrieved based on the random volume over ground (RVoG) model with the C-PolInSAR data. Finally, a numerical method is applied to solve the system of nonlinear equations in the target decomposition procedure, and the amplitude contributions, as well as the vertical phase center heights are retrieved for each of the three scattering mechanisms with the C-PolInSAR data. The proposed target decomposition method has been verified both with a simulated forest data, and with the L -band BioSAR2008 airborne data acquired by the DLR E-SAR system of a forest site in the north of Vindeln municipality, Sweden, wherein detailed performance results for the proposed method are presented as compared to the more usual quad polarimetric SAR interferometry target decomposition.

A Volume Optimization Method to Improve the Three-Stage Inversion Algorithm for Forest Height Estimation Using PolInSAR Data

This letter proposes a novel method to improve the results of the three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry. Since the accuracy of the estimated forest height is affected by the volume only coherence selection, finding the optimum coherence value is an important challenge for the conventional three-stage method. In the three-stage algorithm, a specific polarization state, HV, is usually used as the volume only channel. However, in this letter, an optimization algorithm is developed to find a more accurate volume only coherence on the coherence line. We used the experimental airborne SAR L-band single-baseline single-frequency polarimetric interferometry data to evaluate the proposed algorithm. The experimental results show the proposed optimized volume only coherence leads to 2.9-m improvement in the results of the three-stage inversion algorithm.

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field.

Spaceborne PolInSAR and ground-based TLS data modeling for characterization of forest structural and biophysical parameters

Mapping of forest biophysical parameters is an effective tool for determination of forest inventories, vegetation modeling, and the global carbon cycle. The present study aims at quantification of the forest biophysical parameters of a sub-tropical Forest of India with Terrestrial Laser Scanner (TLS) and Polarimetric Interferometry SAR (PolInSAR) modeling approaches. TLS data was acquired in single and multiple scans and it was found that the multiple scanning approaches have the capability to provide forest parameters with very high accuracy. The RANSAC algorithm was implemented on TLS point cloud data to derive forest structural parameters. The TLS modeled DBH and stem volume exhibited a coefficient of determination of 0.88 and 0.80 and RMSE of 2.85 cm and 0.4 cu.m respectively. PolInSAR Coherence Amplitude Inversion (CAI) and RVoG techniques were implemented to retrieve forest stand height. Both the PolInSAR inversion based models were employed with the integration of complex coherences occurring in different polarimetric channels. CAI estimated the forest height precisely nearly equivalent to the field measured forest height with a coefficient of determination of 0.52, percent accuracy 85.85% and RMSE of 2.94 m. Application of the RVoG model further improved the statistics of the forest height with a coefficient of determination of 0.57, percent accuracy 89.94% and RMSE of 2.13 m. Measurement of tree height estimation using RVoG showed false vegetation height for dry riverbed and the same feature was characterized by the CAI model with zero vegetation height. The research showed that the forest vegetation height characterization of CAI model is better than the RVoG. This study has successfully investigated the potential of spaceborne PolInSAR and ground-based TLS data for forest structural and biophysical parameters retrieval.

Four-Stage Inversion Algorithm for Forest Height Estimation Using Repeat Pass Polarimetric SAR Interferometry Data

This paper proposes a new method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The new algorithm estimates the forest height based on the random volume over the ground with a volume temporal decorrelation (RVoG+VTD) model. We approach the problem using a four-stage geometrical method without the need for any prior information. In order to decrease the number of unknown parameters in the RVoG+VTD model, the mean extinction coefficient is estimated in an independent procedure. In this respect, the suggested algorithm estimates the mean extinction coefficient as a function of a geometrical index based on the signal penetration in the volume layer. As a result, the proposed four-stage algorithm can be used for forest height estimation using the repeat pass PolInSAR data, affected by temporal decorrelation, without the need for any auxiliary data. The suggested algorithm was applied to the PolInSAR data of the European Space Agency (ESA), BioSAR 2007 campaign. For the performance analysis of the proposed approach, repeat pass experimental SAR (ESAR) L-band data, acquired over the Remningstorp test site in Southern Sweden, is employed. The experimental result shows that the four-stage method estimates the volume height with an average root mean square error (RMSE) of 2.47 m against LiDAR heights. It presents a significant improvement of forest height accuracy, i.e., 5.42 m, compared to the three-stage method result, which ignores the temporal decorrelation effect.

Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L -, C -, and X -band frequencies. The X -band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L -band ALOS-2/PALSAR-2 result in a good height inversion with ${\boldsymbol{r}^2}$ and RMSE of 0.77 and 1.86 m ( X -band) and 0.75 and 1.94 m ( L -band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

An Improved Three-Stage Inversion Algorithm in Forest Height Estimation Using Single-Baseline Polarimetric SAR Interferometry Data

This letter provides an advanced method to improve the result for three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on the random volume over ground model. In the conventional three-stage method, the ground phase, extinction coefficient, and volume layer height are estimated in a geometrical way without the need for a prior information or separate reference digital elevation model. The extinction and volume height estimation is done in the third stage by searching in the 2-D area. In the proposed algorithm, defining a new geometrical index, according to signal penetration in the forest, imposes a limited range for the extinction coefficient. The new index, as an axillary data, helps make searching limited to a reasonable 2-D area. The proposed algorithm was applied on L-band E-SAR single-baseline single-frequency PolInSAR data. As a result of applying this restriction in the extinction range, a 2.5-m improvement was observed in the RMSE of the proposed algorithm compared with that of the conventional three-stage method.

Slope three-layer scattering model for forest height estimation over mountain forest areas from L-band single-baseline PolInSAR data

A slope three-layer scattering model (STSM) for retrieving forest height in mountain forest region using L-band polarimetric synthetic aperture radar interferometry (PolInSAR) data is proposed in this paper. The proposed model separates the vertical structure of forest into three layers: canopy, tree trunk, and ground layer, which account for the effect of topography for forest height calculation in a sloping forest area. Compared to the conventional two-layer random volume over ground model, the STSM improves substantial for modeling of actual mountain forest, allowing better understanding of microwave scattering process in sloping forest area. The STSM not only enables the accuracy improvement of the forest height estimation in sloping forest area but also provides the potential to isolate more accurately the direct scattering, double-bounce ground trunk interaction, and volume contribution, which usually cannot be achieved in the previous forest height estimation methods. The STSM performance is evaluated with simulated data from PolSARProSim software and ALOS/PALSAR L-band spaceborne PolInSAR data over the Kalimantan areas, Indonesia. The experimental results indicate that forest height could be effectively extracted by the proposed STSM.

The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data

Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.

Bistatic PolInSAR Inversion Modelling for Plant Height Retrieval in a Tropical Forest

Polarimetric SAR interferometry (PolInSAR) based modeling approaches have been used by several researchers for forest height estimation using airborne and space-borne sensors. Temporal gaps between interferometric pairs due to repeat pass acquisition of SAR data induces volume decorrelation in the coherence values and this may cause error during inversion modelling for forest height estimation. Tandem-X CoSSC products were acquired by operating TerraSAR-X and Tandem-X both the sensors with maintained spatial baseline and zero temporal gap. The prime focus of the present study was to evaluate the performance of bistatic X-band PolInSAR pair for forest height retrieval. Two algorithms; three stage inversion (TSI) and coherence amplitude inversion (CAI) were implemented on the complex coherences obtained from PolInSAR processing. Both the inversion algorithms showed their potential for forest height retrieval through bistatic PolInSAR data. Relationship between field-measured and CAI based modelling showed correlation of 0.27 with RMSE 4.81 m. TSI based modelling showed better results in comparison to CAI based inversion with correlation 0.74 and RMSE of 3.79 m. This study investigated the potential of bistatic PolInSAR data over tropical forest area and it was found that space-borne bistatic SAR has great potential to preserve coherence values for forest height estimation.

Comparative Analysis of Temporal Decorrelation at P-Band and Low L-Band Frequencies Using a Tower-Based Scatterometer Over a Tropical Forest

Temporal decorrelation is a critical parameter for repeat-pass coherent radar processing, including many advanced techniques such as polarimetric SAR interferometry (PolInSAR) and SAR tomography (TomoSAR). Given the multifactorial and unpredictable causes of temporal decorrelation, statistical analysis of long time series of measurements from tower-based scatterometers is the most appropriate method for characterizing how rapidly a specific scene decorrelates. Based on the TropiScat experiment that occurred in a tropical dense forest in French Guiana, this letter proposes a comparative analysis between temporal decorrelation at P-band and at higher frequencies in the range of 800–1000 MHz (the low end of the L-band). This letter aims to support the design of future repeat-pass spaceborne missions and to offer a better understanding of the physics behind temporal decorrelation. Beyond the expected lower values that are found and quantified at the low L-band compared with the P-band, similar decorrelation patterns related to rainy and dry periods are emphasized in addition to the critical impacts of acquisition time during the day.

On the potential of Polarimetric SAR Interferometry to characterize the biomass, moisture and structure of agricultural crops at L-, C- and X-Bands

Polarimetric SAR Interferometry (Pol-InSAR) has shown great promise for estimating the height of agricultural crops through the inversion of a scattering model of the plant canopy and the soil. The inversion also provides estimates of model parameters describing the microwave attenuation within the canopy and the relative scattering contributions from canopy and soil surface.Here, we investigate how vegetation characteristics including biomass, water content (VWC) and canopy structure are related to these parameters and provide a first assessment of the potential of estimating such characteristics using Pol-InSAR time series in L-, C- and X-Bands.The overall attenuation for maize is positively related to total VWC in L- and C-Bands. Furthermore, larger attenuation in VV than HH points toward the existence of anisotropic propagation effects due to vertical orientation of the stalks.Conversely, for wheat in C- and X-Bands there is no consistent relation between attenuation loss and VWC. Rather, structural changes occurring within the plant growth cycle appear to have an appreciable polarization-dependent effect on the observed attenuation changes.In addition, the estimated normalized volume backscattering power NVP (a measure of the relative scattering contribution from the canopy compared to the underlying soil) is associated with wet biomass. However, the contrasting sign of this relation (negative for maize in L- and C-Bands; positive for wheat in C- and X-Bands) indicates again the role of crop structural properties in the Pol-InSAR measurements. For instance, the NVP for maize in L- and C-Bands appears to decrease with increasing biomass due to the increasingly important double bounce ground-stalk scattering contribution as plants become taller and thicker.Overall, these results indicate the sensitivity of the Pol-InSAR parameters to canopy structure and biomass; this sensitivity is however dependent, amongst others, on crop type and radar frequency. When choosing an appropriate baseline/frequency configuration, the Pol-InSAR attenuation loss and NVP may complement the information of the estimated crop height, especially if the latter shows very little variation over the plant growth cycle (e.g. as for wheat).

Combination of PolInSAR and LiDAR Techniques for Forest Height Estimation

Forests are simplified as homogeneous volumes constituted of randomly uniform particles, characterized by a constant extinction coefficient in the random volume over ground (RVoG) model, which has been extensively applied to polarimetric synthetic aperture radar (SAR) interferometry for forest height estimation. This letter takes into account the heterogeneous vertical structure reflected by the vertically varying extinction coefficient curve in the forest volume layer, and modifies the RVoG model to make it be more suitable for height inversion of forests with complex structures. For this purpose, the normalized extinction coefficient curve is fit by large-footprint light detection and ranging full waveform data using the Gaussian function. Finally, the varying extinction RVoG model is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system. The results are compared with in situ measurements, which indicate that the varying extinction RVoG model can obtain more accurate results for forest height inversion.