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Interferometric Synthetic Aperture Radar (InSAR) is a radar technique to generate map of surface deformation using phase differences acquired at different times. The InSAR technique measures these phase differences in its line of sight (LOS). Regarding LOS projection to a ground coordinate system, the combination of SAR data from ascending and descending can estimate at least two dimensional fields which are vertical and east-west components and three dimensional fields with either an additional motion model or vector displacements from along-track direction. The purpose of this work is to generate 2D and 3D vector displacements from two different InSAR tracks (e.g. ascending and descending) with a calculation programme both for the mean velocity and time series data named as PS DISP. 2D mode is calculated in which the north-south component is neglected due to insensitive along the flight direction, thus, assuming to be zero (0). On the other hand, 3D mode is calculated with an assumption that the northsouth component moves along the elevation downward. The experimental results were conducted for the Puncak Pass and Puncak Highway landslides and the Lombok earthquake in Indonesia. For the landslide study, the computation generated the movement towards the slope surface in the 3D Cartesian coordinate (dU,dE, and dN (psuedo)) and found both the crown body and cumulative zone of the landslide. For the earthquake study, PS DISP decomposed 2D vectors (dU and dE). The area of earthquake rupture was lifted by 42 cm and other northern Lombok areas by 10 - 25 cm. Regarding the west-eastern motion, it shows that the west of northern Lombok was displaced ±5 cm toward the west direction, while the other side to the east direction. [ABSTRACT FROM AUTHOR]

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level. [ABSTRACT FROM AUTHOR]

Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters. [ABSTRACT FROM AUTHOR]