Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Abstract

Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning

“…The PolInSAR method combines the interferometric and polarimetric information to better separate the different scattering phase centers in the vegetation volume, which has been demonstrated to be useful for estimating vegetation structural parameters [26,27]. For vegetation height estimation, the PolInSAR technique has been validated in a variety of forest types with many airborne and few spaceborne datasets at different radar frequencies [22,23,[28][29][30][31][32][33][34][35][36][37][38][39]. The performance is constrained by two key aspects: temporal decorrelation and spatial baseline.…”

Estimation of aboveground biomass from PolSAR and PolInSAR using regression-based modelling techniques

“…Polarimetric SAR Interferometry (PolInSAR) is a further extended technique of SAR. It uses the benefits of polarimetry and interferometry coherently Kumar et al 2020). From the PolInSAR-based coherency matrices, both the polarimetric and interferometric properties of images can be retrieved separately (Papathanassiou and Cloude 2001).…”

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

“…Currently, there are more studies using PolSAR observations to invert forest canopy height based on data-driven ideas. However, most of these studies are based on the linear correlation between PolSAR observations and forest canopy height, a priori knowledge, or multiple adjustments of parameters to select the optimal combination of features [30][31][32][33][34]. In addition, empirical relational models or linear/polynomial regression models are typically chosen because of the limited number and type of Pol-SAR observations [16,35].…”