Abstract
Spaceborne polarimetric synthetic aperture radar interferometry (PolInSAR) has the potential to deal with large-scale forest height inversion. However, the inversion is influenced by strong temporal decorrelation interference resulting from a large temporal baseline. Additionally, the forest canopy induces phase errors, while the smaller vertical wavenumber (kz) enhances the sensitivity of the inversion to temporal decorrelation, which limits the efficiency in forest height inversion. This research is based on the random volume over ground (RVoG) model and follows the assumptions of the three-stage inversion method, to quantify the impact of repeat-pass spaceborne PolInSAR temporal decorrelation on the relative error of retrieval height, and develop a semi-empirical improved inversion model, using ground data to eliminate the interference of coherence and phase error caused by temporal decorrelation. Forest height inversion for temperate forest in northern China was conducted using repeat-pass spaceborne L-band ALOS2 PALSAR data, and was further verified using ground measurement data. The correction of temporal decorrelation using the improved model provided robust inversion for mixed conifer-broad forest height retrieval as it addressed the over-sensitivity to temporal decorrelation resulting from the inappropriate kz value. The method performed height inversion using interferometric data with temporal baselines ranging from 14 to 70 days and vertical wavenumbers ranging from 0.015 to 0.021 rad/m. The R2 and RMSE reached 0.8126 and 2.3125 m, respectively.
Highlights
Forest ecosystems are the main components of terrestrial ecosystems [1]
In order to address the apparent temporal decorrelation of coherence amplitude, phase interference, and canopy phase center shift suffered during the repeat-pass spaceborne PolInSAR inversion, this study proposes a new inversion method to achieve the inversion of forest height by empirical iteration
By improving the model inversion of forest height, the significant errors caused by repeat-pass spaceborne PolInSAR temporal decorrelation are reduced, increasing the scope of application of this data in forest height inversion
Summary
Forest ecosystems are the main components of terrestrial ecosystems [1]. Estimating the distribution and change of biomass and carbon storage in forest ecosystems can help to understand the relationship between carbon sources and carbon sinks, and the changing trends in terrestrial ecosystems [2,3,4]. Forest height is an essential parameter for representing the vertical structure of the forest. It provides a significant reference value in estimating forest carbon storage and plays a key role in evaluating forest stand quality and climate impact [5,6,7]. Remote sensing is an essential forest monitoring method that has allowed the development of various forest height retrieval technologies. Spaceborne synthetic aperture radar (SAR) is widely used to observe forest heights of various forest
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