scholarly journals PENGENALAN CITRA SATELIT SENTINEL-2 UNTUK PEMETAAN KELAUTAN

OSEANA ◽  
2017 ◽  
Vol 42 (3) ◽  
pp. 40-55
Author(s):  
Nadya Oktaviani ◽  
Hollanda A Kusuma
Keyword(s):  
Spatial Resolution ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Image Data ◽  
Processing Parameters ◽  
Cloud Detection ◽  
Space Agency ◽  
Thin Cloud ◽  
Sentinel 2

RECOGNITION AND UTILIZATION OF SATELLITE IMAGE SENTINEL-2 FOR MARINE MAPPING. Sentinel-2 is a satellite launched by a collaboration between The European Commission and the European Space Agency in the Global Monitoring for Environment and Security (GMES) program. The satellite has a mission to scan the Earth’s surface simultaneously at an angle of 180 each satellite with a 5-day temporal resolution with the same appearance on the equator and has a spatial resolution of 10 m, 20 m, and 60 m. There are 13 multispectral channels including VNIR and SWIR. Four channels with 10 m spatial resolution adapt with SPOT 4/5 and user’s comply requirements for land cover classification. Six channels with 20 m spatial resolution becomes a requirement for other Level 2 processing parameters. Channels with 60 m spatial resolution are specified for atmospheric correction and cloud filtering (443 nm for aerosols, 940 nm for moisture, and 1375 for thin cloud detection). Based on these specifications, Sentinel-2 can be an alternative for users to obtain image data with spatial, temporal, radiometric, and spectral resolution is better than SPOT and Landsat. Sentinel-2 can be downloaded for free and easy by the general public. The existence of image by Sentinel-2 is expected to be used optimally, especially for remote sensing analysis in marine field.

scholarly journals Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure

2019 ◽  
Vol 11 (4) ◽  
pp. 433 ◽  
Author(s):  
Louis Baetens ◽  
Camille Desjardins ◽  
Olivier Hagolle
Keyword(s):  
Active Learning ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
The United States ◽  
United States Geological Survey ◽  
Shadow Mask ◽  
Cloud Detection ◽  
Basic Product ◽  
Space Agency ◽  
Sentinel 2

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor’s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.

scholarly journals Extracting multilayer networks from Sentinel-2 satellite image time series

2020 ◽  
Vol 8 (S1) ◽  
pp. S26-S42 ◽  
Author(s):  
Roberto Interdonato ◽  
Raffaele Gaetano ◽  
Danny Lo Seen ◽  
Mathieu Roche ◽  
Giuseppe Scarpa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Image ◽  
European Space Agency ◽  
Image Data ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Case Scenario ◽  
Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

scholarly journals Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 641 ◽  
Author(s):  
Joel Segarra ◽  
Maria Luisa Buchaillot ◽  
Jose Luis Araus ◽  
Shawn C. Kefauver
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Satellite Image ◽  
European Space Agency ◽  
Stress Monitoring ◽  
Biotic Stresses ◽  
Space Agency ◽  
Wide Range ◽  
Technical Features ◽  
Sentinel 2

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

scholarly journals Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach

2021 ◽  
Vol 13 (2) ◽  
pp. 300
Author(s):  
Kashyap Raiyani ◽  
Teresa Gonçalves ◽  
Luís Rato ◽  
Pedro Salgueiro ◽  
José R. Marques da Silva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Satellite Image ◽  
European Space Agency ◽  
Scene Classification ◽  
Continuous Increase ◽  
Main Research ◽  
Space Agency ◽  
High Resolution Satellite Imagery ◽  
Sentinel 2

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.

scholarly journals On the Use of Tri-Stereo Pleiades Images for the Morphometric Measurement of Dolines in the Basaltic Plateau of Azrou (Middle Atlas, Morocco)

2021 ◽  
Vol 13 (20) ◽  
pp. 4087
Author(s):  
Maria Teresa Melis ◽  
Luca Pisani ◽  
Jo De Waele
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
European Space Agency ◽  
Morphometric Study ◽  
Stereo Images ◽  
Space Agency ◽  
Middle Atlas ◽  
Mapping Techniques ◽  
Remote Sensing Techniques ◽  
Basaltic Plateau

Hundreds of large and deep collapse dolines dot the surface of the Quaternary basaltic plateau of Azrou, in the Middle Atlas of Morocco. In the absence of detailed topographic maps, the morphometric study of such a large number of features requires the use of remote sensing techniques. We present the processing, extraction, and validation of depth measurements of 89 dolines using tri-stereo Pleiades images acquired in 2018–2019 (the European Space Agency (ESA) © CNES 2018, distributed by Airbus DS). Satellite image-derived DEMs were field-verified using traditional mapping techniques, which showed a very good agreement between field and remote sensing measures. The high resolution of these tri-stereo images allowed to automatically generate accurate morphometric datasets not only regarding the planimetric parameters of the dolines (diameters, contours, orientation of long axes), but also for what concerns their depth and altimetric profiles. Our study demonstrates the potential of using these types of images on rugged morphologies and for the measurement of steep depressions, where traditional remote sensing techniques may be hindered by shadow zones and blind portions. Tri-stereo images might also be suitable for the measurement of deep and steep depressions (skylights and collapses) on Martian and Lunar lava flows, suitable targets for future planetary cave exploration.

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.

Remote sensing of snow and atmosphere properties using Ocean and Land Colour Instrument on board Copernicus Sentinel-3 mission

2020 ◽  
Author(s):  
Alexander Kokhanovsky ◽  
Jason Box ◽  
Baptiste Vandecrux ◽  
Michael Kern
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Satellite Observations ◽  
Snow Properties ◽  
Retrieval Technique ◽  
Space Agency ◽  
Correction Technique ◽  
Cloud Screening

<p><span>In this work we propose a simple technique to derive snow and atmosphere properties from satellite top-of-atmosphere spectral reflectance observations using asymptotic radiative transfer theory valid for the case of weakly absorbing and optically thick media. The following snow properties are derived and analyzed: ice grain size, snow specific surface area, snow pollution load, snow spectral and broadband albedo. The developed retrieval technique includes both atmospheric correction and cloud screening routines and is based on Ocean and Land Colour Instrument (OLCI) measurements on board Sentinel-3A, B. The spectral aerosol optical thickness, total ozone and water vapour column are derived fitting the measured and simulated OLCI-registered spectral reflectances at 21 OLCI channels.</span></p><p><span>The derived results are validated using ground - based observations. It follows that satellite observations can be used to study time series of spectral and broadband albedo over Greenland. The deviations of satellite and ground observations are due to problems with cloud screening over snow and also due to different spatial scale of satellite and ground observations (Kokhanovsky et al., 2020).</span></p><p>Acknowledgements</p><p>The work has been supported by the European Space Agency in the framework of ESRIN contract No. 4000118926/16/I-NB ‘Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 Snow (Sentinel-3 for Science, Land Study 1: Snow’) and ESRIN contract 4000125043 – ESA/AO/1-9101/17/I-NB EO science for society ‘Pre-operational Sentinel-3 snow and ice products’.</p><p><span>References</span></p><p>Kokhanovsky, A.A., et al. (2020), The determination of snow albedo from satellite observations using fast atmospheric correction technique, Remote Sensing, 12 (2), 234,  https://doi.org/10.3390/rs12020234.</p>

In-Situ Validation of Water Quality Algorithms and Monitoring of Irish Lakes using Sentinel 2 Imagery

2020 ◽  
Author(s):  
Sita Karki ◽  
Kevin French ◽  
Valerie McCarthy ◽  
Jennifer Hanafin ◽  
Eleanor Jennings ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Window ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Weather Condition ◽  
Water Bodies ◽  
Observation Data ◽  
Field Validation ◽  
Field Samples ◽  
Sentinel 2

<p>Through Remote Sensing of Irish Surface Water (INFER) project, we are validating the algorithms to measure the  water quality using Sentinel 2 imagery, which comprises of two European Space Agency (ESA) terrestrial satellites with combined temporal resolution of 5 days. The project is focused on selection of optimal algorithms that will be applicable in Irish context in relation to the high cloud cover and relatively small sizes of the water bodies. The current procedure entails collection of reflectance data from the lakes during the Sentinel overpass as it helps to identify the correct atmospheric correction algorithm. Field radiometry tasks were carried out using TRIOS RAMSES radiometers. Standard field procedures were employed for acquiring glint free reflectance from the water bodies.</p><p>Historical data collected from the 11 lakes, which had field bathymetry survey data, were analysed in order to determine the influence of environmental conditions on the quality of samples. Based on the analysis, recommendations to collect field samples from areas deeper than 10 m and 30 m away from the shoreline were provided in order to avoid the reflectance from the bottom and the surrounding topography. A site selection process was undertaken during the spring of 2019 to shortlist appropriate sites for field validation of satellite-derived products. A total of fifteen lakes were identified for field validation based on several criteria so as to ensure lakes with varying size, depth, trophic status and Water Framework Directive (WFD) status . In addition, a timetable for proposed sampling was established by drawing up a timetable of satellite passes starting from summer of 2019. C2RCC and Acolite processors are being used to compute the chlorophyll and turbidity from identified lakes. Considering the fast changing weather condition of Ireland, it was difficult to obtain the exact overlap between the sentinel overpass and the field sampling. In order to address this issue, the field samples collected within 10 days from the sensor overpass were considered for the field validation. Study of the satellite derived water chemistry data showed that the data collected outside of that time window may not represent the natural fluctuation that occurs in the water bodies.</p><p>The end product of this project is the web platform with the access to Sentinel 2 MSI data products where users can visualize the water quality products for Ireland. This platform will promote the use of earth observation data for inland water quality monitoring and would enable sustainable utilization of the water resources.</p>

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 Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data

2020 ◽  
Author(s):  
Jaime Pitarch ◽  
Marco Bellacicco ◽  
Salvatore Marullo ◽  
Hendrik J. van der Woerd
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
European Space Agency ◽  
Sensor Data ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
The North ◽  
Space Agency ◽  
Free Data ◽  
Hue Angle

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel-Ule index, hue angle and Secchi disk depth. Source data comes from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region, as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in-situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ~4 km at the equator and are available at PANGAEA, https://doi.org/10.1594/PANGAEA.904266 (Pitarch et al., 2019a). Two smaller and easier to handle test datasets have been produced from the former: a global dataset at 1 degree spatial resolution and another one for the North Atlantic at 0.25 degree resolution.

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