A new dual-baseline polarimetric SAR interferometry for vegetation height inversion using complex least squares adjustment

Vegetation height is an important parameter in quantifying the terrestrial carbon cycle effectively. P band synthetic aperture radar (SAR) is good at monitoring forest areas, which is sensitive to the contributions from vegetation layer and ground. Random Volume over Ground (RVoG) model has been widely applied to forest height inversion. Corresponding single baseline or muti-baseline methods have been established, such as three-stage and nonlinear iteration methods, whose good performance have been test with differential wavelength and areas. However, all the methods do not pay much attention to the complex coherence errors, and take measure to reduce the errors. In this paper, a new dual-baseline polarimetric SAR interferometry vegetation height inversion method, dual-baseline complex least squares adjustment (DBCLSA), is proposed. The DBCLSA method behind the concept of complex least squares adjustment. The main idea of DBCLSA method is as follow. Firstly, the adjustment model is summarized as combined adjustment of complex real and imagine parts. And then, stochastic model is established based on Cramer-Rao bounds. After that, we show the linearization method and parameter solving method. At last, the obtained volume-only coherence values are used to vegetation height retrieval. The DBCLSA is validated on E-SAR P band data of BioSAR2008 in Sweden. The results of Cloude dual-baseline method are computed at the same time. Compared with ground true measurement data, the result of new approach is more accurate.