The past decade has witnessed the rapid development of the SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) in engineering applications and scientific research. The near-global SRTM DEM was generated based on radar interference theory. The latest version of the SRTM DEM with a resolution of 1 arc-second has been widely used in various applications. However, many studies have shown the poor elevation accuracy of the SRTM DEM in forested areas. Recent developments in the field of spaceborne lidar have provided an additional chance to correct the elevation error of the SRTM DEM in forested areas. We developed an easy-to-use method to correct the elevation error of the SRTM DEM based on the spatial interpolation method using the recent Ice, Cloud and land Elevation Satellite-2 data. First, an ICESat-2 terrain control point selection criterion was proposed to reject some erroneous ICESat-2 terrains caused by many factors. Second, we derived the elevation correction surface based on the interpolation method using the refined ICESat-2 terrain. Finally, a corrected SRTM DEM of forested areas was generated through the obtained elevation correction surface. The proposed method was tested in the typical forested area located in Massachusetts, USA. The results show that the RMSE of the selected terrain control points in vegetation areas and non-vegetation areas are 1.03 and 0.68 m, respectively. The corrected SRTM DEM have an RMSE of 4.2 m which is significantly less than that of the original SRTM DEM with an RMSE of 9.8 m, which demonstrates the proposed method is feasible to correct the elevation error in forested areas. It can be concluded that the proposed method obviously decreases the elevation error of the original SRTM DEM.

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications.

The geolocation accuracy of spaceborne synthetic aperture radar (SAR) imagery is crucial for a wide range of applications, yet it is often degraded by errors in onboard parameters, making accurate geocoding refinement essential. Conventional refinement methods typically involve coregistering SAR images with digital elevation model (DEM)-simulated SAR images (DSSIs) to extract planar control points (PCPs), which are then utilized to construct polynomial models for correcting geolocation errors. However, a key drawback of this approach lies in its strong reliance on slope variations in the reference DEM to generate textural features in the DSSIs. Consequently, in low-relief terrains such as plains and deserts, the coregistration process often produces an inadequate number of unevenly distributed PCPs, leading to reduced refinement accuracy. When a standard SAR scene provides limited coverage, planar block adjustment (PBA) is commonly applied to integrate PCPs and propagate their constraints through tie points, alleviating the limitations of single-scene refinement. Nevertheless, most PBA techniques assume prerefined single scenes as a prerequisite and aim to compensate for residual errors from the initial refinement, rendering them ineffective in low-relief terrains. To overcome this limitation, we propose a robust PBA method that directly achieves holistic geocoding refinement, bypassing the need for error compensation. Experiments using LuTan-1 (LT-1) bistatic InSAR data from three representative test sites—including plains and deserts—demonstrate the effectiveness of the proposed approach. Our method consistently attained subpixel geolocation accuracy, overcoming the limitations of conventional PBA methods. In terms of elevation accuracy, the resulting LT-1 DEM achieved a root mean square error better than 2 m across all test sites, outperforming the Copernicus DEM in the same regions.