Frequently updating DEMs based on multi-track repeat-pass InSAR observations using robust variance component estimation

Abstract

Space-borne interferometric synthetic aperture radar (InSAR) is a useful technique to generate or update digital elevation models (DEMs) over large regions. Specifical InSAR missions for DEM generation/update currently work in bistatic mode. The bistatic InSAR satellites have a low temporal coverage, causing the difficulty to keep DEM products up to date. InSAR satellites working in a repeat-pass mode can offer numerous data sources with a short temporal coverage, offering a great potential to frequently update DEMs to keep DEM valid with time. However, the accuracy of repeat-pass InSAR DEMs using the existing algorithms is too low for practical uses currently. To circumvent this, we proposed a new method to frequently update DEMs from repeat-pass InSAR datasets, in order to improve update accuracy. Firstly, multi-track repeat-pass InSAR datasets were utilized to offer more redundant observations to mitigate InSAR noises. A new quantitative model was then developed to scientifically guide the exclusion of multi-track interferograms with very short spatial baselines, in order to further reduce the propagation of InSAR errors into DEM products. Thirdly, a robust variance component estimation (RVCE) algorithm, which can adaptively weight multi-track InSAR observations and automatically exclude outliers, was used to dynamically update the DEMs. The proposed method was tested over the Hambach open-pit mine in Germany. The results show that the mean accuracy of the updated DEMs is about 8.7 m, demonstrating a 60 % improvement over classical single-track repeat-pass InSAR techniques. The proposed method offers a new option to frequently update DEMs, especially over areas with changes of surface terrain.