scholarly journals Harmonizing the Landsat Ground Reference with the Sentinel-2 Global Reference Image Using Space-Based Bundle Adjustment

2020 ◽  
Vol 12 (19) ◽  
pp. 3132
Author(s):  
Rajagopalan Rengarajan ◽  
James C. Storey ◽  
Michael J. Choate
Keyword(s):  
European Space Agency ◽  
Bundle Adjustment ◽  
Reference Image ◽  
Landsat 8 ◽  
Control Points ◽  
Remotely Sensed Data ◽  
Space Agency ◽  
Data Products ◽  
Four Blocks ◽  
Sentinel 2

There is an ever-increasing need to use an accurate and consistent geometric ground reference in the processing of remotely sensed data products, as this reduces the burden on the end-users to account for the differences between the data products from different missions. In this regard, the U.S. Geological Survey (USGS) initiated an effort to harmonize the Landsat ground reference with the Sentinel-2 Global Reference Image (GRI) to improve the co-registration between the data products of the two global medium-resolution missions. In this paper, we discuss the process, results, and the improvements expected from this harmonization of two ground references using space-triangulation-based bundle adjustment techniques. The ground coordinates of the Landsat reference library, consisting of five million Ground Control Points (GCPs) were adjusted in a series of four simultaneous bundle block adjustments using thousands of Landsat-8 (L8) scenes anchored with more than 300,000 control points extracted from the GRI dataset. The net adjustments to each of the four blocks, namely, Australia, Americas, Eurasia, and Islands, varied anywhere from 1 to 13 m, depending on the accuracy of the GCPs in these blocks. The use of the GRI dataset in our bundle adjustment not only improved the absolute accuracy of the Landsat ground reference, but will also improve the co-registration between Sentinel-2 and Landsat terrain corrected products, as the European Space Agency plans to process the Sentinel-2 products using the GRI dataset. Independent validation of the Landsat products processed using harmonized GCPs with the GRI dataset indicated a global misregistration error of less than 8 m Circular Error Probable at 90 % (CE90), an improvement from the 25 m prior to harmonization. The improvements to the Landsat products using the harmonized GCPs are expected to be available to the public as part of Landsat Collection-2 processing by the end of 2020.

scholarly journals IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

2019 ◽  
Vol 13 (2) ◽  
pp. 179-186
Author(s):  
Paul Macarof ◽  
Florian Statescu ◽  
Cristian Iulian Birlica ◽  
Paul Gherasim
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
European Space Agency ◽  
Condition Index ◽  
Landsat 8 ◽  
Natural Phenomena ◽  
Space Agency ◽  
Vegetation Condition Index ◽  
Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

scholarly journals SENTINEL-2 GLOBAL REFERENCE IMAGE VALIDATION AND APPLICATION TO MULTITEMPORAL PERFORMANCES AND HIGH LATITUDE DIGITAL SURFACE MODEL

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 447-454 ◽  
Author(s):  
A. Gaudel ◽  
F. Languille ◽  
J. M. Delvit ◽  
J. Michel ◽  
M. Cournet ◽  
...  
Keyword(s):  
Image Quality ◽  
European Space Agency ◽  
Reference Image ◽  
Surface Model ◽  
Space Agency ◽  
Northern Latitudes ◽  
Quality Aspects ◽  
Multi Temporal ◽  
Temporal Registration ◽  
Sentinel 2

In the frame of the Copernicus program of the European Commission, Sentinel-2 is a constellation of 2 satellites with a revisit time of 5 days in order to have temporal images stacks and a global coverage over terrestrial surfaces. Satellite 2A was launched in June 2015, and satellite 2B will be launched in March 2017.<br><br> In cooperation with the European Space Agency (ESA), the French space agency (CNES) is in charge of the image quality of the project, and so ensures the CAL/VAL commissioning phase during the months following the launch. This cooperation is also extended to routine phase as CNES supports European Space Research Institute (ESRIN) and the Sentinel-2 Mission performance Centre (MPC) for validation in geometric and radiometric image quality aspects, and in Sentinel-2 GRI geolocation performance assessment whose results will be presented in this paper. The GRI is a set of S2A images at 10m resolution covering the whole world with a good and consistent geolocation. This ground reference enables accurate multi-temporal registration of refined Sentinel-2 products.<br><br> While not primarily intended for the generation of DSM, Sentinel-2 swaths overlap between orbits would also allow for the generation of a complete DSM of land and ices over 60° of northern latitudes (expected accuracy: few S2 pixels in altimetry). This DSM would benefit from the very frequent revisit times of Sentinel-2, to monitor ice or snow level in area of frequent changes, or to increase measurement accuracy in areas of little changes.

Modification of a groundwater prospect zone index using remotely sensed data and the analytic network process in the eastern part of Lesvos island, Greece.

2020 ◽  
Vol 11 (4) ◽  
pp. 126-143
Author(s):  
Terpsichori MITSI ◽  
◽  
Demetre ARGIALAS ◽  
Konstantinos VAMVOUKAKIS ◽  
◽  
...  
Keyword(s):  
Ground Water ◽  
Analytic Network Process ◽  
Remotely Sensed ◽  
Groundwater Potential ◽  
Landsat 8 ◽  
Groundwater Prospect ◽  
Remotely Sensed Data ◽  
Potential Index ◽  
Network Process ◽  
Sentinel 2

Because of climate change and overpopulation, the demand for water is increasing. Groundwater constitutes an alternative renewable source of aquifer, so the spatial distribution of ground water provides important information on its qualitative and quantitative status. This paper develops a methodology for delineating potential ground water zones using remotely sensed data and GIS. The developed methodology was based on the empirical index GPI (MGPI – Modified Groundwater Potential Index) and was applied to the eastern part of Lesvos Island, Greece. To evaluate the criteria used for the result, the Analytic Network Process (ANP) was applied to weight each parameter. The dataset used consists of satellite images derived from Sentinel 2 and Landsat 8, which were combined with vector and raster data, to create the necessary thematic layers. To validate the results, existing ground water zones from the Municipal Water Company of Lesvos were used.

scholarly journals OLCI A/B Tandem Phase Analysis, Part 1: Level 1 Homogenisation and Harmonisation

2020 ◽  
Vol 12 (11) ◽  
pp. 1804 ◽  
Author(s):  
Nicolas Lamquin ◽  
Sébastien Clerc ◽  
Ludovic Bourg ◽  
Craig Donlon
Keyword(s):  
European Space Agency ◽  
Companion Paper ◽  
The Other ◽  
Satellite Mission ◽  
Space Agency ◽  
The Future ◽  
Level 1 ◽  
Level 2 ◽  
Very High ◽  
Sentinel 2

Copernicus is a European system for monitoring the Earth in support of European policy. It includes the Sentinel-3 satellite mission which provides reliable and up-to-date measurements of the ocean, atmosphere, cryosphere, and land. To fulfil mission requirements, two Sentinel-3 satellites are required on-orbit at the same time to meet revisit and coverage requirements in support of Copernicus Services. The inter-unit consistency is critical for the mission as more S3 platforms are planned in the future. A few weeks after its launch in April 2018, the Sentinel-3B satellite was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit. Both satellites were flown only thirty seconds apart on the same orbit ground track to optimise cross-comparisons. This tandem phase lasted from early June to mid October 2018 and was followed by a short drift phase during which the Sentinel-3B satellite was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A satellite. In this paper, an output of the European Space Agency (ESA) Sentinel-3 Tandem for Climate study (S3TC), we provide a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instruments (OLCI) based on the tandem phase. Homogenisation adjusts for unavoidable slight spatial and spectral differences between the two sensors and provide a basis for the comparison of the radiometry. Persistent radiometric biases of 1–2% across the OLCI spectrum are found with very high confidence. Harmonisation then consists of adjusting one instrument on the other based on these findings. Validation of the approach shows that such harmonisation then procures an excellent radiometric alignment. Performed on L1 calibrated radiances, the benefits of harmonisation are fully appreciated on Level 2 products as reported in a companion paper. Whereas our methodology aligns one sensor to behave radiometrically as the other, discussions consider the choice of the reference to be used within the operational framework. Further exploitation of the measurements indeed provides evidence of the need to perform flat-fielding on both payloads, prior to any harmonisation. Such flat-fielding notably removes inter-camera differences in the harmonisation coefficients. We conclude on the extreme usefulness of performing a tandem phase for the OLCI mission continuity as well as for any optical mission to which the methodology presented in this paper applies (e.g., Sentinel-2). To maintain the climate record, it is highly recommended that the future Sentinel-3C and Sentinel-3D satellites perform tandem flights when injected into the Sentinel-3 time series.

scholarly journals ASSESSMENT OF THE GEOMETRIC QUALITY OF SENTINEL-2 DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 489-494 ◽  
Author(s):  
M. Pandžic ◽  
D. Mihajlovic ◽  
J. Pandžic ◽  
N. Pfeifer
Keyword(s):  
Visual Analysis ◽  
High Reliability ◽  
European Space Agency ◽  
Statistical Parameter ◽  
Quality Analysis ◽  
Line Segments ◽  
Total Length ◽  
Space Agency ◽  
New Perspective ◽  
Sentinel 2

High resolution (10 m and 20 m) optical imagery satellite Sentinel-2 brings a new perspective to Earth observation. Its frequent revisit time enables monitoring the Earth surface with high reliability. Since Sentinel-2 data is provided free of charge by the European Space Agency, its mass use for variety of purposes is expected. Quality evaluation of Sentinel-2 data is thus necessary. Quality analysis in this experiment is based on comparison of Sentinel-2 imagery with reference data (orthophoto). From the possible set of features to compare (point features, texture lines, objects, etc.) line segments were chosen because visual analysis suggested that scale differences matter least for these features. The experiment was thus designed to compare long line segments (e.g. airstrips, roads, etc.) in both datasets as the most representative entities. Edge detection was applied to both images and corresponding edges were manually selected. The statistical parameter which describes the geometrical relation between different images (and between datasets in general) covering the same area is calculated as the distance between corresponding curves in two datasets. The experiment was conducted for two different test sites, Austria and Serbia. From 21 lines with a total length of ca. 120 km the average offset of 6.031 m (0.60 pixel of Sentinel-2) was obtained for Austria, whereas for Serbia the average offset of 12.720 m (1.27 pixel of Sentinel-2) was obtained out of 10 lines with a total length of ca. 38 km.

scholarly journals Mapping Snow for Area Where was Detected Ground Deplacement: A Case Studi of Iaşi County

2018 ◽  
Vol 12 (2) ◽  
pp. 167-174
Author(s):  
Paul Macarof ◽  
Cezarina Georgiana Bartic Lazăr ◽  
Florian Statescu
Keyword(s):  
Ground Deformation ◽  
European Space Agency ◽  
Vertical Displacement ◽  
Landsat 8 ◽  
Ground Displacement ◽  
Space Agency ◽  
Numerical Indicator ◽  
Normalized Difference Snow Index ◽  
Differential Radar Interferometry ◽  
Wide Swath

Abstract The main goal of this paper is to detect snow in areas where was detecting and mapping, using Differential Radar Interferometry (DInSAR) technique, ground displacement. DInSAR is a powerful tool to detect and monitor ground deformation. Iaşi county is considered as study area in this research. Study area is geographically situated on latitude 46°48’N to 47°35’N and longitude 26°29’E to 28°07’E. For this paper, to detect and mapping grond displacement, was used Sentinel – 1 images, provided free by The European Space Agency (ESA), for January 2018, with vertical polarization (VV), ascending orbit and Interferometric Wide swath (IW) mode operated. SNAP was used to process the Sentinel – 1 images. Landsat-8 OLI was taken to detect areas cover with snow using Normalized Difference Snow Index (NDSI) - a numerical indicator that shows snow cover over land areas. ArcMap was used to create NDSI map after Landsat-8 data was preprocessed. The presence of snow has been observed both in the areas where it exists vertical displacement positive and negative.

scholarly journals QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 231-234
Author(s):  
M. A. Günen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Spatial Resolution ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Landsat 8 ◽  
Data Generation ◽  
Gis Applications ◽  
Assessment Metrics ◽  
Sentinel 2

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.

scholarly journals Fires Monitoring by Means of Spectral Indices from Sentinel-2 Data: the Case Study of the Vesuvius and Lattari Mountains, Campania (Italy)

2020 ◽  
Author(s):  
Domenico Antonio Giuseppe Dell'Aglio ◽  
Carmine Gambardella ◽  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Rosaria Parente ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
European Space Agency ◽  
Fire Risk ◽  
Fire Detection ◽  
Spectral Indices ◽  
Active Fire ◽  
Space Agency ◽  
Repeat Time ◽  
Sentinel 2

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

scholarly journals MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES

2021 ◽  
Vol V-3-2021 ◽  
pp. 101-107
Author(s):  
G. Fonteix ◽  
M. Swaine ◽  
M. Leras ◽  
Y. Tarabalka ◽  
S. Tripodi ◽  
...  
Keyword(s):  
Deep Learning ◽  
European Space Agency ◽  
External Source ◽  
Convolutional Network ◽  
Spectral Bands ◽  
Space Agency ◽  
Semantic Labeling ◽  
Multi Temporal ◽  
Correction Step ◽  
Sentinel 2

Abstract. The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.

Remote sensing of grassland communities in Mongolian Steppe combining multi-source data and machine learning classification algorithms

2020 ◽  
Author(s):  
Thanh Noi Phan ◽  
Yun Jäschke ◽  
Oyundari Chuluunkhuyag ◽  
Munkhzul Oyunbileg ◽  
Karsten Wesche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Training Sample ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Machine Learning Classification ◽  
Steppe Region ◽  
Training Samples ◽  
Grassland Communities ◽  
Sentinel 2

&lt;p&gt;In this study, we investigate the performance of machine learning classification approaches and different remotely sensed data sources for identifying and mapping three types of grassland communities found in the Mongolian Steppe region (Artemisia, Caragana and grass dominated steppes). The Mongolian steppe is intensively used as pasture and provides the economic basis for approximately 1 million herders. The grassland types differ in their forage values, which is why a spatially-explicit estimation of their occurrence is of high importance. We compared different sensors, classifiers, and training-sample strategies to identify the most effective approaches for mapping these communities. Ten datasets were used: Landsat 8 OLI (30 m), pan-sharpened Landsat 8 (15 m), Landsat 8 Surface Reflectance (30 m), Sentinel 2 (10 m), Sentinel 2 (20 m), Worldview 3 (0.5 m and 1.2 m), integrated Landsat 8 and Sentinel 2 (30 m), temporal Landsat 8, and temporal Sentinel 2. The two foremost classifiers at producing high accuracy of land cover classification, SVM and RF, were applied with the same training datasets. The training samples were collected in a manner so that they could be used for different spatial resolutions (i.e., ranging from 0.5 to 30 m) with the least effect from mixed training samples and spatial autocorrelation. The results of this study indicate that remote sensing is a viable method for the identification of different grassland communities in the Mongolian Steppe region.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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