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 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 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 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 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 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 Crop Water Condition Mapping by Optical Remote Sensing

2021 ◽  
pp. 11-17
Author(s):  
M.V. Wojtaszek ◽  
I. Abdurahmanov
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Soil Water ◽  
Precision Agriculture ◽  
Water Status ◽  
Vegetation Indices ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
Stress Monitoring ◽  
Wide Range

Crop water stress monitoring represents a fundamental step in agricultural production. In order to increase water savings and enhance agricultural sustainability, implementation of suitable irrigation scheduling methods is essential, and requires early detection of water stress in crops, before it causes irreversible damage and yield loss. There are different methods to measure water stress, some of them are based on soil moisture measurements while others are based on calculations of vegetation indices, evapotranspiration or soil water balance. Currently, the use of remote sensing technologies for the analysis of plant water status comprises a wide range of available methods such as infrared thermometry for canopy temperature measures, microwave radiation for soil water content assessment, and spectral vegetation indices for the study of the reflectance responses of canopies to different environmental conditions. The aim of the presented work is to investigate the applicability of the optical trapezoid model (OPtical TRApezoid Model) in mapping the moisture content within agricultural field. The model ability to provide vegetation characteristics, and crop water status at the canopy scale can improve the site-specific decision-making process in a precision agriculture.

scholarly journals AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

2021 ◽  
Vol 13 (6) ◽  
pp. 1219
Author(s):  
Ramona Magno ◽  
Leandro Rocchi ◽  
Riccardo Dainelli ◽  
Alessandro Matese ◽  
Salvatore Filippo Di Gennaro ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Sustainable Management ◽  
European Space Agency ◽  
Commission Errors ◽  
Access Policy ◽  
Detection Tool ◽  
Space Agency ◽  
Sentinel 2A ◽  
Omission Errors ◽  
Sentinel 2

Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA.

A virtual test environment for validating spacecraft optical navigation

2013 ◽  
Vol 117 (1197) ◽  
pp. 1075-1101 ◽  
Author(s):  
S. M. Parkes ◽  
I. Martin ◽  
M. N. Dunstan ◽  
N. Rowell ◽  
O. Dubois-Matra ◽  
...  
Keyword(s):  
Small Body ◽  
European Space Agency ◽  
Ground Truth ◽  
Camera Motion ◽  
Test Environment ◽  
Space Applications ◽  
Vision Guidance ◽  
Space Agency ◽  
Wide Range ◽  
And Performance

Abstract The use of machine vision to guide robotic spacecraft is being considered for a wide range of missions, such as planetary approach and landing, asteroid and small body sampling operations and in-orbit rendezvous and docking. Numerical simulation plays an essential role in the development and testing of such systems, which in the context of vision-guidance means that realistic sequences of navigation images are required, together with knowledge of the ground-truth camera motion. Computer generated imagery (CGI) offers a variety of benefits over real images, such as availability, cost, flexibility and knowledge of the ground truth camera motion to high precision. However, standard CGI methods developed for terrestrial applications lack the realism, fidelity and performance required for engineering simulations. In this paper, we present the results of our ongoing work to develop a suitable CGI-based test environment for spacecraft vision guidance systems. We focus on the various issues involved with image simulation, including the selection of standard CGI techniques and the adaptations required for use in space applications. We also describe our approach to integration with high-fidelity end-to-end mission simulators, and summarise a variety of European Space Agency research and development projects that used our test environment.

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 The Rosetta Mission to Primitive Bodies of the Solar System

1994 ◽  
Vol 160 ◽  
pp. 381-394
Author(s):  
Yves Langevin
Keyword(s):  
Remote Sensing ◽  
Solar System ◽  
European Space Agency ◽  
Time Span ◽  
Main Belt ◽  
Space Agency ◽  
Rosetta Mission ◽  
One Year ◽  
Definition Of

The European Space Agency (ESA) has selected Rosetta as the next cornerstone mission, to be launched in 2003. The goal is to perfom one or more fly-bys to main belt asteroids, followed by a rendez-vous with an active comet. Advanced in situ analysis, both in the coma and on the surfaces of the nucleus, will be possible, as well as monitoring by remote sensing instruments of the nucleus and of the inner coma for a time span of more than one year, until perihelion. This paper outlines the scientific and technological choices done in the definition of the mission.

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