Historically, the Kyrgyz Republic and neighboring countries have experienced numerous strong earthquakes. Our research focused on investigation of surface deformations caused by 5 earthquake events which had occurred during 2014-2017. A Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was applied for Sentinel-1 data acquired before after events. The pairs images were pro...

Abstract. The NASA MEaSUREs Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project seeks to accelerate understanding critical glaciers ice sheet processes by providing researchers with global, low-latency, comprehensive state the art records surface velocities elevations as observed from space. Here we describe image-pair velocity product processing methodology for ESA ...

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful in continuously deforming regions, much information is lost by simply fitting a line through time series. Thanks regular acquisitions across most world ESA Sentinel-1 satellite constellation, we...

The Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) considers agricultural fields as one of the essential variables that can be derived from satellite data. We evaluated accuracy at which delineated Sentinel-1 (S1) and Sentinel-2 (S2) images in different landscapes throughout growing season. used supervised segmentation based multiresolution (MRS) algorithm to fi...

Abandoned agricultural land (AAL) is a European problem and phenomenon when gradually overgrown with shrubs forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove validate the concept satellite-based estimation woody above-ground (AGB) on AAL in Western Carpathian region. analysis based Sentinel-1 -2 satellite data, supported by ...

Large-scale crop type mapping often requires prediction beyond the environmental settings of training sites. Shifts in phenology, field characteristics, or ecological site conditions previously unseen area, may reduce classification performance machine learning classifiers that overfit to This study aims assess spatial transferability Random Forest models for across Germany. The effects differe...