ESTIMATING 3D LAND SUBSIDENCE FROM MULTI-TEMPORAL SAR IMAGES AND GNSS DATA USING WEIGHTED LEAST SQUARES
Analysis of multi-temporal synthetic aperture radar (SAR) satellite images using persistent scatterer interferometry is an effective approach for monitoring land subsidence, which is a serious issue in some urban areas. However, a drawback to this approach is that it is limited to displacement along the radar line-of-sight direction. An accurate understanding of land subsidence requires estimation of 3D displacement. One solution is to combine observations from multiple sources and directions, such as multi-temporal SAR images acquired on ascending and descending orbits, with global navigation satellite system (GNSS) data. While this approach estimates 3D displacement, other methods do not account for differences in data accuracy. Therefore, in this paper, we propose a method for estimating 3D land subsidence from multi-temporal SAR images and GNSS data by using the weighted least squares method. The weights for data sources are calculated from the PSI results and GNSS data. We apply the method to Kansai International Airport, using 13 ALOS-2/PALSAR-2 ascending images from 2014 to 2018 and 17 ALOS-2/PALSAR-2 descending images from 2015 to 2018. Root mean squared errors in the east–west, north–south and vertical directions are 6, 13, and 10 mm/year, respectively. These results demonstrate that combining PSI and geodetic results is effective for monitoring land deformation accurately with high spatial resolution.