A Modified Three-Stage Inversion Algorithm Based on R-RVoG Model for Pol-InSAR Data

Abstract

Abstract:In this paper, a modified two-layer scattering model is applied to a three-stage algorithm for high-precision retrieval of forest parameters from Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) data. Traditional Random-Volume-over-Ground (RVoG) model considers forest target as a two-layer combination of flat ground and volumetric canopy. However, when it comes to sloped terrain, the inversion accuracy of three-stage process deteriorates with the ascending estimation error in volume correlation which is mainly caused by the existence of underlying terrain slope. Aiming at this problem, a Range-sloped RVoG (R-RVoG) model is presented in this paper. By modifying the ground layer as a range-sloped plane, the complex correlation of R-RVoG model can be amended as a function of ground phase, ground-to-volume scattering ratio, forest height, mean extinction and range slope. The introduction of range slope variable makes this modified model better resemble to real scene and thus improves the performance of three-stage algorithm. Both of the simulated data with different terrain slopes and the Space-borne Imaging Radar-C (SIR-C) real data in Tianshan test area are processed to verify the validity of this modification.

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

“…Since the underlying topography over the forest area commonly takes on certain fluctuations, the forest height inversion on the basis of the traditional RVoG model under the flat ground hypothesis reckons to be susceptible to the terrain slope [6,[27][28][29][30][31][32]. Accordingly, it is indispensable to incorporate the influence of the terrain slope into the forest height estimation.…”

“…So far, the RVoG model depicting the forest vertical backscatter profile with an exponential function has been widely taken advantage of in forest height estimation [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Moroever, when referring to temporal decorrelation [21][22][23][24][25], canopy filling [12,26] and terrain fluctuations [6,[27][28][29][30][31][32], some modified models were proposed to complement the RVoG model. Under the circumstance of the Quad-pol single-baseline observation, the forest height estimation based on the RVoG model is a six-dimensional parameter optimization problem [1,3,5,10,12].…”

“…Specifically, the NGVR hypothesis indicates that the ground-to-volume ratio (GVR) of at least one polarimetric channel is sufficiently small, usually less than −10 dB [12]. In view of the RVoG model, the three-stage [12] inversion process applying single-baseline data has been widely implemented for forest height acquisition [15,31,33,34]. That is a geometric inversion method that requires fitting the estimated coherences of various polarimetric channels to obtain the coherence line on the complex plane.…”

“…Topographic fluctuations have been manifested to be one of the significant factors affecting the performance of the model-based Pol-InSAR inversion [6,[27][28][29][30][31][32]. Specifically, the positive terrain slope causes an overestimation of the forest height, while the negative terrain slope causes an underestimation of the forest height [6,29,30,32].…”

Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

“…The experimental site is located in Krycklan catchment, Northern Sweden, mainly covered by boreal forest (spruce, pine, and birch), and the Pauli-basis polarization composite map is presented in Figure 7 [23]. On account of the significant topographic fluctuations in the test area, the external Digital Elevation Model (DEM) data is used to generate terrain slope to compensate the inversion model, without which there would be a certain degree of inversion error [23,26,29,30,[34][35][36][37]. In addition, the LiDAR-measured forest height provided in the data set is used as the true value to evaluate the inversion performance.…”