Semiempirical compensated optimal coherence amplitude method to invert forest height based on InSAR

Abstract

Interferometric synthetic aperture radar (InSAR) is an important resource for rapidly obtaining forest height information. The coherence amplitude method relies solely on interferometric coherence amplitude information for forest height inversion; however, interferometric data are commonly affected by decorrelation factors including time, baseline, and noise decorrelation, as well as observation geometry errors, leading to errors in estimated forest height. Thus, in this paper, we propose a method to improve the accuracy of forest height estimation by correcting the decorrelation and vertical wave number using a priori forest height knowledge. UAVSAR-L cross-polarization channel HV data from the AfriSAR project are used as interferometric data to invert the forest height using the coherence amplitude method, and the relative height variable RH100 from land, vegetation, and ice sensor light detection and ranging (LiDAR) is used for validation. We optimize the coherence amplitude method by iteratively setting different steps for the nonvolume decorrelation (γd) and the correction parameter (τ) for the vertical wavenumber (kz). The optimal compensation parameter is identified when the root mean square error (RMSE) between the inversion height and LiDAR height is minimized, and the stability of the returned parameter is evaluated through an independent validation sample. Our results indicate that enhancing the coherence amplitude method using a semiempirical iterative approach can effectively improve inversion accuracy. In the validation results, all compensation schemes exhibit a significant improvement in the inversion results compared with those without parameter compensation. The R2 increases by 0.13 and the RMSE decreases by 9.88 m when compensating only γd, whereas the R2 value does not change when only compensating kz, but the RMSE decreases by 19.24 m. When compensating for both γd and kz, the R2 increases by 0.08, and the RMSE decreases by 19.73 m. This changing pattern is consistent with that recorded in the training sample, indicating that our proposed parameter compensation scheme for the coherence amplitude method is effective. With the widespread usage of satellite data, such as ALOS-2 and SAOCOM, as well as the future TanDEM-L and BIOMASS satellites and NISAR program, the combination of ICESat-2 and GEDI forest height data to compensate and optimize inversion results and model parameters is expected to greatly improve the efficiency of forest resource monitoring in the future.