Abstract
Abstract. Glacier displacements play a vital role in the monitoring and understanding of glacier dynamics. Glacier displacement fields are typically retrieved from pre- and post-event SAR images using DInSAR. The glacier displacement map produced by DInSAR contains missing values due to decorrelation of the SAR images. This study demonstrates the utility of direct sampling—a well-established multiple-point geostatistics method—for deriving those missing values. Univariate and bivariate implementations of direct sampling are employed. In the univariate implementation, missing values are derived in single displacement map, whereas in bivariate implementation gaps in two displacement maps are filled simultaneously. Evaluation is carried out by artificially generated missing values on the displacement map of different shapes and sizes at different locations with known values. Imposed missing values are then reconstructed and compared with the original values. Reconstruction results of both implementations were compared with ordinary kriging using qualitative and quantitative measures. The study shows that with an increase in the size of such discontinuities, ordinary kriging predictions deteriorate significantly, whereas only slight decrease in reconstruction accuracy is observed for direct sampling. The results of both implementations are similar with the univariate implementation performing slightly better over bivariate implementation because the information from ancillary data is only partly complementary for bivariate reconstructions. Direct sampling performed better than ordinary kriging with accuracy below the DInSAR detection limit. This study concludes that multiple-point geostatistics is an effective method for deriving missing values in DInSAR derived displacement maps. Direct sampling based reconstruction is fast and straightforward to implement.
Highlights
Glaciers are masses of ice formed by the accumulation and compaction of snow over a long duration of time
The interferograms of Ngozumpa glacier, before unwrapping, computed from Synthetic Aperture Radar (SAR) image pair I and II are shown in Figure 3(a) and 3(b) respectively
This study concludes that a novel S1 SAR dataset can be successfully used to retrieve the surface displacements of mountain glaciers employing a well-established differential interferometric SAR (DInSAR) technique
Summary
Glaciers are masses of ice formed by the accumulation and compaction of snow over a long duration of time. They constantly move because of stresses induced by their weight and gravity. Satellite remote sensing techniques—feature tracking in both optical and Synthetic Aperture Radar (SAR) images, and interferometric SAR (InSAR)—have been applied for deriving surface velocity of mountain glaciers over in situ measurements because in situ measurements are costly, time-consuming, limited over a small geographical area, and impractical to perform in inaccessible, remote and vast mountain glaciers (Joughin et al, 2010). Feature tracking in SAR images and InSAR techniques eliminate this limitation as SAR images are independent of sun-illumination, penetrate clouds and can function day and night in all-weather condition. InSAR technique is greatly valued for glacier velocity studies
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