A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram

Accuracy assessment of ASTER and SRTM DEMs: A case study in Andean Patagonia

The ASTER global digital elevation model (GDEM) and the SRTM-C digital elevation model (DEM) provide nearly global coverage, with spatial resolutions of 30 and 90 m, respectively. We assessed the geolocation, elevation, and morphological accuracy of the SRTM-C, the ASTER GDEM, and two single-scene DEMs derived from ASTER data for a site in Patagonia (ASTER DEMs). We found systematic and widely dispersed geolocation errors for the SRTM-C (Linear RMSE = 85.0 m) and the ASTER GDEM (Linear RMSE = 101.1 m). The SRTM-C had a narrow elevation error distribution (RMSE 8.3 ± 2.9 m), whereas the ASTER GDEM had a smaller RMSE (9.4 ± 2.3 m) than the analyzed ASTER DEMs. The ASTER DEMs provided more detailed morphological information than the SRTM-C, but also had more noise.Fil: Gómez, Mariano. Provincia del Chubut. Centro de Investigación y Extensión Forestal Andino Patagónico; ArgentinaFil: Lencinas, José. Provincia del Chubut. Centro de Investigación y Extensión Forestal Andino Patagónico; ArgentinaFil: Siebert, Antje. Provincia del Chubut. Centro de Investigación y Extensión Forestal Andino Patagónico; ArgentinaFil: Díaz, Gastón Mauro. Provincia del Chubut. Centro de Investigación y Extensión Forestal Andino Patagónico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Land Cover Classification using Sentinel-1 Radar Mission Interferometry

Synthetic Aperture Radar (SAR) has been widely used for many years in the field of remote sensing. SAR has valuable contribution due to its ability to provide complementary information to optical systems, penetration of radar waves through volumetric targets and high-resolution. SAR has the ability to operate during day and night. It provides operational services under all weather conditions. SAR imagery has many applications including land cover changes, environmental monitoring, climate change and military surveillance. This work focuses on land cover classification with SAR interferometry (InSAR) technique using Sentinel-1 space radar image pair. Sentinel-1 data were collected over the southern part of Estonia. Two SLC SAR images were acquired from both Sentinel-1A and Sentinel-1B with six days temporal difference. In this study, interferometric coherence and backscattering intensity processing chains have been set up and applied to Sentinel-1 SAR image pair. The Sentinel Application Platform (SNAP) has been used for processing of single pair for Sentinel-1 mission. The SNAP is an European Space Agency (ESA) software. The Sentinel-1 image pair processing has been done using Sentinel-1 Toolbox (S1TBX) which is a part of SNAP. Corine Land Cover (CLC) 2012 database has been used as a reference data with 20 m resolution. The CLC2012 contains land use/cover information for most of the European countries. A single optical image from Sentinel-2A was additionally used for feature extraction. An overall accuracy of 68% to 73% was achieved when performing classification into five classes (Urban, Field, Forest, Peat-land, Water) using supervised classification with k-nearest neighbour (kNN) algorithm. The accuracy assessment was done by using confusion matrices

Time series analysis of very slow landslides in the three Gorges region through small baseline SAR offset tracking

Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de -correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data, with sufficient density of observations, we estimate the precision of the SBAS offset tracking approach to be 2-3 cm on average. The results demonstrated accord well with corresponding GPS measurements

Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas

Sub-Pixel Offset Tracking (sPOT) is applied to derive high-resolution centimetre-level landslide rates in the Three Gorges Region of China using TerraSAR-X Hi-resolution Spotlight (TSX HS) space-borne SAR images. These results contrast sharply with previous use of conventional differential Interferometric Synthetic Aperture Radar (DInSAR) techniques in areas with steep slopes, dense vegetation and large variability in water vapour which indicated around 12% phase coherent coverage. By contrast, sPOT is capable of measuring two dimensional deformation of large gradient over steeply sloped areas covered in dense vegetation. Previous applications of sPOT in this region relies on corner reflectors (CRs), (high coherence features) to obtain reliable measurements. However, CRs are expensive and difficult to install, especially in remote areas; and other potential high coherence features comparable with CRs are very few and outside the landslide boundary. The resultant sub-pixel level deformation field can be statistically analysed to yield multi-modal maps of deformation regions. This approach is shown to have a significant impact when compared with previous offset tracking measurements of landslide deformation, as it is demonstrated that sPOT can be applied even in densely vegetated terrain without relying on high-contrast surface features or requiring any de-noising process

InSAR-based mapping of ground deformation caused by industrial waste disposals: the case study of the Huelva phosphogypsum stack, SW Spain

Close to the city of Huelva, SW Spain, and near the Atlantic Ocean, there is a phosphogypsum (PG) stack that accumulates 100 Mt of wastes and extends over 1000 ha. The stack lies directly over estuarine unconsolidated sediments with no protective layer in between. Here, we evaluate for the first time the structural stability of the PG stack, monitoring the deformation suffered by the salt-marsh basement. Through the web-based Geohazard Exploitation Platform (GEP) of the European Space Agency (ESA), a specific differential SAR interferometry (DInSAR) algorithm known as arallel Small Baseline Subset (P-SBAS) has been used to process 279 ESA Sentinel-1 images acquired between October 2016 and June 2021. Resulting displacement maps and time-series curves reveal vertical displacements of up to 16 cm/year. This vertical motion has been associated to subsidence. In parallel with subsidence, horizontal movements > 2.5 cm/year have been also accounted and linked to talus destabilization. The analysis also demonstrates that the Huelva PG stack is vulnerable to adverse weather condition. The present study demonstrates that the InSAR-based methods are effective tools for monitoring the stability and ground motion of large waste stockpiles.This work was financed by the ESA thorough a project covered by the NOR Sponsorship Program. The project (ID: Felipe González) was intended to use the Geohazards TEP service (https:// geoha zards- tep. eu/#!) for the analysis of the subsidence of SW Spain. Special thanks are extended to Hervé Caumont (Terradue Programme Manager) who patiently provided technical support during all the analysis. The original manuscript was significantly improved thanks to the valuable suggestions and comments of two anonymous reviewers. Aerial photograph in Figure 1 was provided by the Mesa de la Ría Association. Funding for open access charge: Universidad de Huelva / CBUA