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 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 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 Changes in Landscape Structure in the Municipalities of the Nitra District (Slovak Republic) Due to Expanding Suburbanization

2021 ◽  
Vol 13 (3) ◽  
pp. 1205
Author(s):  
Zuzana Pucherová ◽  
Regina Mišovičová ◽  
Gabriel Bugár ◽  
Henrich Grežo
Keyword(s):  
Landscape Structure ◽  
Slovak Republic ◽  
Satellite Image ◽  
Analytical Data ◽  
European Space Agency ◽  
Rural Landscape ◽  
Landscape Features ◽  
Main Research ◽  
Spatial Changes ◽  
The Impact

Suburbanization, as a set of several factors, influences and changes the landscape structure of smaller municipalities in the hinterland of larger cities. The purpose of this paper is to evaluate the built-up areas related to suburbanization within three time horizons—in 2002, 2005, and 2020—in 62 municipalities of the district (including two cities, Nitra and Vráble). This study examines the process of spatial changes in landscape features (residential, industrial, agricultural, transport) related to suburbanization between 2002 and 2005 and between 2002 and 2020. The input analytical data were digital orthophotomaps from 2002 and 2005 and the current orthophotomosaics of the Slovak Republic from 2017 (GKÚ, Bratislava), updated for the year 2020 using Sentinel 2 satellite image data (European Space Agency). The impact of suburbanization processes between 2002 and 2005 did not reach the dimensions of the changes that occurred due to suburbanization processes between 2002 and 2020 or 2005 and 2020. The main research objective of the article is the identification and assessment of these changes. We determined which landscape features related to suburbanization affected spatial changes in municipalities of the district Nitra. The total area affected by one of the suburbanization processes monitored by us reached 92.52 ha in the period between 2002 and 2005. Between the years 2002 and 2020, the area reached a total of 2272.82 ha, which is an increase of 2180.30 ha in 2020 compared to 2002. This included mainly the expansion of settlements or housing (60.15%), industrial areas (29.31%), transport facilities (4.35%), agricultural areas (0.73%), and other areas (5.46%). These results show expanding suburbanization for the period from 2002 to 2020 and that this process has been gaining momentum in the municipalities of the Nitra district, especially in recent years, which changes the look of rural municipalities and the character of a typical rural landscape.

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.

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 Recognition of Maize Phenology in Sentinel Images with Machine Learning

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 94
Author(s):  
Alvaro Murguia-Cozar ◽  
Antonia Macedo-Cruz ◽  
Demetrio Salvador Fernandez-Reynoso ◽  
Jorge Arturo Salgado Transito
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Spatial Association ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Models ◽  
Phenological Stages ◽  
Area Index ◽  
Stage Of Development ◽  
Sentinel 2

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

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.

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.

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