Synthetic aperture radar Interferometry (InSAR) has proven to be an effective landslide monitoring technique, especially for very slow landslides without observable morphological features. Integration of multi-source InSAR observations from different satellites/tracks could help reduce omissions and misjudgements of potential landslides, showing a promising trend toward automatic landslide detection and monitoring at regional or national scale. However, existing methods present poor error suppression performance, and are not capable of describing and solving potential information conflicts, due to the neglect of observation uncertainties during the integration. Here we propose a new integrated method based on Dempster-Shafer evidence theory to address these deficiencies. The two key steps are multi-source InSAR integration based on DST and a two-step decision rule. We apply the proposed method to the entire Jinsha River Basin using both ascending and descending Sentinel-1 SAR data from 2014 to 2023. The preliminary result on the Luoshui-Baini section based on two sources (ascending and descending orbits) identified 68 landslides, which is nearly the same between our method and the existing mosaic method.

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (<10 cm) period in 2017. The overall accuracy of the estimated water depth changes achieved an RMSE of 1.49 cm. Compared with three existing methods, our method showed significantly lower RMSE values overall. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.

Tropospheric delays have been a major limitation on the precision and accuracy of Interferometric Synthetic Aperture Radar (InSAR). InSAR data-based tropospheric delay correction methods, especially the local window methods, could estimate heterogeneous tropospheric delays in the same resolution as InSAR data, thus, are increasingly desired for modern high-resolution InSAR products. However, the phase-elevation relationship estimation at local windows can be severely contaminated by topography-correlated deformation. In this paper, we present a new InSAR phase-based tropospheric delay correction method based on texture correlation, which is shown to be relatively insensitive to topography-correlated deformation. The texture information represents the spatially high-frequency component of a two-dimensional image, which can be obtained using high-pass filtering. The method first produces a low-resolution tropospheric delay estimation using the window-wised texture correlation in the space domain, then refines it to high resolution by fitting the residual with the previously estimated tropospheric phase-elevation slope in the time domain. We apply the proposed method to ALOS-2 data over the Kirishima volcanic complex in Japan, encompassing typical topography-correlated deformation. The estimated phase-elevation slope shows seasonal oscillation in agreement with independent ERA5 prediction. The proposed method reduces the median spatial standard deviation of the residual phase from 1.3 cm to 0.7 cm, showing superior performance compared with other existing methods without compromising the deformation signal.

Benefiting from the wide coverage and high resolution of synthetic aperture radar (SAR) data, interferometric SAR (InSAR) has significant advantages in wide-area deformation detection and monitoring. In order to improve computational efficiency and save computational resources, it is expected to perform the deformation area identification first as accurately as possible, and then perform local time series inversion for the specific deformation area. However, with the interference of tropospheric delay, especially its systematic component, the accurate identification of the deformation area becomes challenging. To address this issue, we propose a two-step tropospheric delay removal method including time domain correction and spatial domain correction. The time domain correction is used to avoid the effect of the systematic component of tropospheric delay so as to derive an accurate deformation rate map. The purpose of the spatial domain correction is to finely remove the effect of tropospheric delay in local area to recover the correct deformation time series. Applying our method, external data based method and existing classical SAR data based method to the reservoir area of the Lianghekou hydropower station with Sentinel-1A ascending data for comparison, the results demonstrate the advantages of our method in deformation area identification and deformation monitoring.

California has lost most of its historical wetlands and it is in urgent need to conserve and protect the remaining wetlands. One of the key elements for wetland management is monitoring changes in surface water depths, which is challenging due to inaccessibility and dynamic hydrology of wetlands. Particularly, many California wetlands are privately owned with small areas (e.g., 40 ha) and bounded by levees, resulting in differences in hydrological regimes. Managed wetlands are characterized by high water depths in winter season and low depths in summer season, with a rapid transition in between. Considering the complicated spatiotemporal hydrological pattern, it is difficult to monitor regional water level variations based on in-situ measurement alone. This study explored the possibility of using multi-sensor observations for hydrological applications by investigating the relationship between satellite observations (Interferometric Synthetic Aperture Radar (InSAR) coherence, and Normalized Difference Vegetation Index (NDVI)) and in-situ water depth measurements.

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals within situfield observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

In the above article[1], Table I(b) cited an incorrect reference number. Reference [12] should have been given as [13], provided here as[2].

Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.

Many filtering algorithms have been developed to extract the digital terrain model (DTM) from dense urban light detection and ranging data or the high-resolution digital surface model (DSM), assuming a smooth variation of topographic relief. However, this assumption breaks for a middle-resolution DSM because of the diminished distinction between steep terrains and nonground points. This letter introduces a two-step semiglobal filtering (TSGF) workflow to separate those two components. The first SGF step uses the digital elevation model of the Shuttle Radar Topography Mission to obtain a flat-terrain mask for the input DSM; then, a segmentation-constrained SGF is used to remove the nonground points within the flat-terrain mask while maintaining the shape of the terrain. Experiments are conducted using DSMs generated from Chinese ZY3 satellite imageries, verified the effectiveness of the proposed method. Compared with the conventional progressive morphological filter method, the usage of flat-terrain mask reduced the average root-mean-square error of DTM from 9.76 to 4.03 m, which is further reduced to 2.42 m by the proposed TSGF method.