Acquiring high-resolution and accurate forest height is essential for estimating terrestrial carbon storage and detecting changes. Since 2010, TanDEM-X has obtained an unprecedented global interferometric SAR dataset and has been widely used to estimate forest height. However, its accuracy is limited due to insufficient observational information. Since September 2018, NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has acquired global discrete surface elevation and forest canopy height, providing an excellent opportunity to improve the performance of TanDEM-X forest height estimation. Based on our previous research, this paper proposes some methods to estimate highresolution and large-scale forest height using these two missions combined with optical remote sensing data, and estimates forest height in Spain. As validated against LiDAR data, the RMSE of the estimated optimal average forest height ranges from 1.83 m to 3.70 m, at the resolution of onehectare forest stands.

The upcoming launch of long-wavelength synthetic aperture radar (SAR) systems, including BIOMASS, TanDEM-L, and NISAR, will bring new perspectives to interferometric SAR (InSAR) topography mapping. However, these advanced SAR systems will inevitably encounter atmospheric effects in the repeat-pass interferometric mode. To demonstrate the viability of large-scale topography mapping using the new SAR satellites, we propose a digital elevation model (DEM) block adjustment considering atmospheric effects to correct systematic errors and atmospheric delay errors between different strips. This paper conducted simulated experiments in the coastal and inland areas using L-band Advanced Land Observation Satellite (ALOS)-1 PALSAR data, respectively. We utilized Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data to assess the vertical accuracy of DEM with and without considering atmospheric effects. The results showed the effectiveness of our method, with an improved RMSE of 84.7% in the coastal area (21.29m to 3.25m) and 75.6% in the inland area (13.01m to 3.17m).

TanDEM-X InSAR data has exhibited commendable performance in forest height inversion, with the semi-empirical SINC model (SeEm-SINC) proving its robustness in flat regions. However, the relationship between InSAR coherence and forest height become unreliable in forest scenes of mountainous terrain. Significant overestimation or underestimation of forest height occurs when using coherence only in areas with positive or negative slope, and the bias is strongly correlated with the range slope. Building upon the SeEm-SINC model, we investigate the relationship between slope and the bias involved in forest height inversion. Additionally, we present a forest height inversion approach utilizing TanDEM-X InSAR ascending and descending data. Experimental results demonstrate that this method effectively mitigates estimation deviations caused by slope in forested mountainous areas.

Iceberg information in polar regions is crucial for various applications. Synthetic aperture radar (SAR) satellites provide high-resolution remote sensing images without being affected by weather conditions, which are widely used for iceberg monitoring. Most current studies track icebergs by shape similarity and use the distance between the centroids of the icebergs as the offset between the two temporal images. However, it is difficult to characterize icebergs comprehensively with a single shape similarity, the matching performance of the traditional shape similarity-based iceberg tracking method depends on the profile extraction accuracy in the iceberg detection. Furthermore, the limited coverage of satellite remote sensing images prevents the full capture of all icebergs. The drift of these icebergs in the time-series image exhibits shape variations, leading to a mismatch in the centroids, affecting the accuracy of drift velocity calculations. To address these issues, this letter proposes an object-oriented rotated intensity matching (OORIM)-based iceberg drift method that considers both the boundary shape and the intensity similarity of iceberg objects and obtains both offset information and rotation angle simultaneously. We successfully tracked 21 icebergs from Sentinel-1 SAR images near the Weddell Sea, and the results demonstrate the superiority of this method compared with other methods in terms of the accuracy of iceberg tracking. Specifically, the correct tracking accuracies of the centroid distance histogram (CDH), angle distance vector (ADV), and proposed OORIM methods are 90.5%, 66.7%, and 100%, respectively.

Forest height is an indispensable parameter for natural resource investigations. Spaceborne Interferometric SAR (InSAR) has the sensitivity to measure forest height, especially TanDEM-X, which provides high-quality interferometric coherence without the effects of atmospheric delay and temporal decorrelation. In this paper, we seriously considered the limited penetrability of TanDEM-X InSAR, and proposed a two-step machine learning (ML) method to estimate large-scale forest height by combining TanDEM-X InSAR data, ICESat-2 data, and Landsat 8 data. The forest scattering phase center (SPC) height of InSAR is estimated by the first ML, and on this basis, the relationship between the SPC height and forest height is established by the second ML. We validated the effectiveness of proposed method in a Spanish Mediterranean climate forest region with airborne LiDAR data. As validated against LiDAR data, the accuracy of the estimated SPC height ranges from 2.07 m to 2.49 m, and the root mean square error (RMSE) of the average forest height ranges from 1.79 m to 2.93 m, at the resolution of four-hectare forest stands.