Canopy height is an essential feature in forest inventory, and for the assessment of biomass and carbon budgets. Spatially explicit maps of forest height over large areas can be derived from satellite synthetic aperture radar data. We aimed to evaluate the capacity of TanDEM-X (TDX) data to assess canopy height in Mediterranean forests of Spain, which are of relatively short height (typically < 20 m), diverse in species and structure, and adapted to summer drought. Interferogram coherence was retrieved from single-pol image pairs. Forest height estimation was carried out by previously fitting a sinc-type function, with two empirical parameters, to the data measured. Six types of forest were defined to assess the convenience of stratification for model implementation. The influence of terrain slope, forest type, and interferometric baseline on model performance was evaluated, and a strategy for large area mapping was proposed and tested. TDX-derived heights were compared to a contemporaneous LiDAR-derived canopy height model for assessment of quality. Results limited to slopes below 10° provided the best results, reaching R ² = 0.91 and root-mean-square error = 1.24 m in one of the study sites. However, in some areas the results were much worse, especially in regions characterized by rugged terrain with broadleaved species. This work demonstrates the feasibility of deriving a forest height map over the entire area of Spain from TDX data. Stratification per slope interval and selection of long interferometric baselines are recommended.

Polarimetric synthetic aperture radar interferometry (PolInSAR) and light detection and ranging (LiDAR) have their own respective advantages and disadvantages in extracting large-scale forest height. In this letter, we present an advanced approach to obtain forest canopy height by combining these two strategies. More specifically, the novelty of the proposed method focuses on a dual-baseline selection from multibaseline PolInSAR data, which ensures the robust performance of the forest height inversion by effectively improving the estimation of volume-only coherence. The dual-baseline selection can be regarded as a supervised classification problem. We consider support vector machine (SVM) as an appropriate classifier, and a small amount of sparse LiDAR samples within the coverage of the PolInSAR data (less than 1%) are chosen to assist with the training of the dual-baseline combination classification, which can be met by the current spaceborne LiDAR missions. Finally, we validate the proposed approach by airborne P-band synthetic aperture radar (SAR) data acquired by the F-SAR system and LiDAR data acquired by the National Aeronautics and Space Administration (NASA) Land, Vegetation, and Ice Sensor (LVIS) during the 2016 AfriSAR campaign. The estimation accuracy of the proposed method [$R^{2} = 0.73$ , root-mean-square error (RMSE) = 3.17 m] is 25.24% higher than that of the existing SVM fusion approach devoted to single-baseline selection ($R^{2} = 0.59$ , RMSE = 4.24 m).

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

Existing volume scattering models play an important role in interpreting the scattering characteristics of vegetation canopies. A common assumption of these models is that a vegetation canopy consists of one type of particle. However, since a vegetation canopy comprises boughs, twigs, and leaves (or needles), they tend to form scatterers with various shapes, resulting in differing scattering processes. In this article, we propose a new volume scattering model. The proposed scattering model uses an isotropic plate, isotropic dihedral, and dipole particles to model vegetation scatterers. Different preferred orientation angles characterize the latter two types of particles. The proposed method is validated with AIRSAR and ALOS2/PALSAR2 datasets. The results show that the proposed model provides us an opportunity to extract refined scattering processes of vegetation canopies. In addition, it can be found that the proposed model shows potential in vegetation classification and biomass estimation, resulting from modeling a vegetation canopy as various types of particles.