Estimation of Canopy Height From a Multi-SINC Model in Mediterranean Forest With Single-Baseline TanDEM-X InSAR Data

Abstract

TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this article, a method for retrieving canopy height in Mediterranean forests, characterized by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semiempirical parameters and then expand the single curve into a collection of three curves, forming the multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of light detection and ranging (LiDAR) data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared with the semiempirical SINC model (RMSE = 3.28 m and 2.36 m).

Keywords

• Coherence
• forest height
• interferometric synthetic aperture radar (InSAR)
• machine learning (ML)
• multi-SINC
• TanDEM-X