Abstract
The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.
Highlights
It is well known that vegetation height plays an important role in quantifying the terrestrial carbon cycle [1,2]
(a)ground groundsurface surfacephase phaseestimated estimated three-stage method; (b) ground surface estimated by the difference between the ground surface phases obtained by the three‐stage estimated by truncated singular value decomposition (TSVD); (c) the difference between the ground surface phases obtained by the three-stage method and TSVD
Differing from the traditional three-stage method, the new method estimates the pure volume coherence intuitively from thethe complex interferometric coherence, and has estimates the pure volume coherence intuitively from complex interferometric coherence, and has the capacity to adjust the contributions of the polarizations in the estimation of the model parameters
Summary
It is well known that vegetation height plays an important role in quantifying the terrestrial carbon cycle [1,2]. Accurately extracting vegetation height at a large scale is an important task. Given the fact that polarimetric interferometric synthetic aperture radar (PolInSAR) can separate the scattering power of a single resolution cell into the contributions of surface, double-bounce, and volume scattering, it can be considered to be a viable remote sensing technique for estimating vegetation height in large-scale areas [4,5,6,7,8,9]. In a number of PolInSAR campaigns, the random volume over ground (RVoG) model [6,10] has been used to extract vegetation height from the complex interferometric coherence [15,16,17]. Based on the RVoG model, Papathanassiou [6] proposed six-dimensional nonlinear optimization method, which has been
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