scholarly journals KappaMask: AI-Based Cloudmask Processor for Sentinel-2

2021 ◽  
Vol 13 (20) ◽  
pp. 4100
Author(s):  
Marharyta Domnich ◽  
Indrek Sünter ◽  
Heido Trofimov ◽  
Olga Wold ◽  
Fariha Harun ◽  
...  
Keyword(s):  
Land Surface ◽  
European Space Agency ◽  
Optical Data ◽  
Dice Coefficient ◽  
Test Dataset ◽  
Space Agency ◽  
Northern Latitudes ◽  
Agricultural Applications ◽  
Cloud Mask ◽  
Sentinel 2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

scholarly journals Oil Spill Index (OSI) to Sentinel-2 Satellite Data: QU in International Contribution

2021 ◽  
Author(s):  
S Rajendran ◽  
AS Fahad ◽  
FN Sadooni ◽  
HAS Al-Kuwari ◽  
P Vethamony ◽  
...  
Keyword(s):  
Oil Spill ◽  
Satellite Data ◽  
Emergency Preparedness ◽  
Oil Spills ◽  
European Space Agency ◽  
Optical Data ◽  
Spectral Absorption ◽  
Spectral Bands ◽  
Space Agency ◽  
Sentinel 2

An Oil Spill Index (OSI = (B3+B4)/B2) was developed and applied to Sentinel-2 optical satellite data of the European Space Agency (ESA) to map marine oil spills using spectral absorption characters of spectral bands of the Sentinel-2. The potential application of OSI and derived indices [i. (5+6)/7, (3+4)/2, (11+12)/8 and ii. 3/2, (3+4)/2, (6+7)/5] were demonstrated to the oil spills that occurred off Mauritius, Indian Ocean, on August 06, 2020, and Norilsk region, Russia on May 29, 2020, and the results were published in the peer-reviewed research journals. Recently (August 19, 2021), our methodology was recognized by the Sentinel-Hub (a repository of custom scripts) https://custom-scripts.sentinel-hub.com/sentinel-2/oil-spill-index/ for OSI calculation. We validated the remote sensing results with the drone images taken during the incident. Our OSI index is the first to be applied to Sentinel-2 optical data to map oil spills. We proved the potential of indices and the capability of Sentinel sensors to detect, map, monitor, and assess the oil spill, which can be used for emergency preparedness of oil spills.

scholarly journals Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

2019 ◽  
Vol 11 (16) ◽  
pp. 4454 ◽  
Author(s):  
Stefano Morelli ◽  
Matteo Del Soldato ◽  
Silvia Bianchini ◽  
Veronica Pazzi ◽  
Ervis Krymbi ◽  
...  
Keyword(s):  
European Space Agency ◽  
Temporal Analysis ◽  
Radar Data ◽  
Optical Data ◽  
Future Perspective ◽  
Management Service ◽  
Surface Mapping ◽  
Water Index ◽  
Space Agency ◽  
Sentinel 2

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

Status of Current and Expansion Missions of the Copernicus Optical Imaging Family

2020 ◽  
Author(s):  
Ferran Gascon ◽  
Anja Stromme ◽  
Michael Rast ◽  
Jens Nieke ◽  
Benjamin Koetz ◽  
...  
Keyword(s):  
Natural Resources ◽  
Surface Temperature ◽  
Temporal Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
European Space Agency ◽  
Earth Observation ◽  
Space Agency ◽  
Spatio Temporal ◽  
Sentinel 2

<p>The Copernicus EU program started in 1998 with the overarching aim to become Europe’s operational Earth Observation monitoring system providing data and information services. An essential part of the program is the Copernicus Space Component (CSC), which is managed by the European Space Agency (ESA) as responsible for the Copernicus Sentinels satellite constellations.</p><p>The presentation will include an overview of the CSC Optical Imaging Family (OIF) currently operated missions, namely Sentinel-2 and Sentinel-3, and candidate potential missions being developed, namely Copernicus Hyperspectral Imaging Mission for Environment (CHIME) and High Spatio-Temporal Resolution Land Surface Temperature Monitoring Mission (LSTM). The next generation missions are not included here.</p><p>Sentinel-2 is an Earth Observation mission developed by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. The mission consists on a Multi-Spectral Instruments (MSI) on board a constellation of two satellites: Sentinel-2A launched in June 2015 and Sentinel-2B launched in March 2017. It covers the Earth’s land surfaces and coastal waters every five days under the same viewing conditions and every three days at mid-latitudes with high spatial resolution and a wide field of view.</p><p>5 day revisit (i.e. under same viewing conditions) is met at all latitudes of observations (not only at equator), and with the swath overlap and the S2 orbit repeat pattern (14+3/10 rev/day, i.e. a 3 day sub-cycle), 3 day geometric coverage is achieved at mid latitudes.</p><p>Sentinel-3 mission is measuring sea surface topography, sea and land surface temperature, and ocean and land surface colour with high accuracy and reliability to support ocean forecasting systems, environmental monitoring and climate monitoring. The Sentinel-3 mission is jointly operated by ESA and EUMETSAT to deliver operational ocean and land observation services.</p><p>CHIME, identified as one of the Copernicus Expansion High Priority Candidate Missions (HPCM), will provide routine observations through the Copernicus Programme for managing natural resources and assets in support of EU policy, and will complement currently flying multi-spectral missions such as Sentinel-2. Compared to multi-spectral missions, CHIME will have an increased number of narrow spectral bands (spectral resolution of 10nm with no gaps between bands) in the visible-to-shortwave infrared range (400-2500nm), which will allow for a more accurate determination of biochemical and biophysical variables.</p><p>LSTM, also identified as one of the HPCM, will provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring. High spatio-temporal resolution thermal infrared observations are considered fundamental to sustainable management natural resources in the context of water and food security of a global society. Operational land surface temperature (LST) measurements and derived evapotranspiration (ET) are key variables in understanding and responding to climate variability, managing water resources for agricultural production, predicting droughts but also addressing land degradation, natural hazards, coastal and inland water management as well as urban heat island issues.</p>

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.

scholarly journals Coseismic displacements of the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake using an optical cubesat constellation

2017 ◽  
Author(s):  
Andreas Kääb ◽  
Bas Altena ◽  
Joseph Mascaro
Keyword(s):  
New Zealand ◽  
Land Surface ◽  
Near Infrared ◽  
Sar Interferometry ◽  
Optical Methods ◽  
Optical Data ◽  
Surface Displacements ◽  
Before And After ◽  
Optical Satellite Images ◽  
Sentinel 2

Abstract. Satellite measurements of coseismic displacements are typically based on Synthetic Aperture Radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very-high resolution satellites, or airphotos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m resolution visible and near-infrared frame images of approximately 20–30 km × 9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake we observe surface displacements of up to almost 10 m and estimate a matching accuracy from PlanetScope data of up to ±0.2 pixels (~ ±0.6 m). This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat8 and Sentinel-2 data.

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.

A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Spatio Temporal

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;:&lt;/p&gt;&lt;p&gt;Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as &amp;#8220;The third pole of the world&amp;#8221; and &amp;#8220;Asia&amp;#8217;s water towers&amp;#8221;, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002&amp;#8211;2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25&amp;#176; resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R&lt;sup&gt;2&lt;/sup&gt; larger than 0.65, and RMSE less than 0.08 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;-3&lt;/sup&gt; and Bias less than 0.07 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;-3 &lt;/sup&gt;at 0.25&amp;#176; and 1&amp;#176; spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial&amp;#160;and&amp;#160;temporal&amp;#160;characteristics and trend of SM over TP were analyzed based on this dataset.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau&lt;/p&gt;

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 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.

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