scholarly journals INTEGRATION OF SENTINEL-2 AND LANDSAT-8 DATA FOR SURFACE REFLECTANCE TIME-SERIES ANALYSIS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 205-210 ◽  
Author(s):  
G. Berdou ◽  
S. Shrestha ◽  
M. Hahn
Keyword(s):  
Atmospheric Correction ◽  
Surface Characteristics ◽  
Global Scale ◽  
Landsat 8 ◽  
Observation Data ◽  
Common Reference ◽  
Key Factor ◽  
Land Covers ◽  
Earth Observation Data ◽  
Sentinel 2

Abstract. Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 627-633 ◽  
Author(s):  
A. Nascetti ◽  
M. Di Rita ◽  
R. Ravanelli ◽  
M. Amicuzi ◽  
S. Esposito ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Global Scale ◽  
Google Earth ◽  
Observation Data ◽  
Geometric Accuracy ◽  
Computing Platform ◽  
Aster Gdem ◽  
Google Earth Engine ◽  
Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters.<br><br> In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing

2020 ◽  
Vol 12 (11) ◽  
pp. 1770 ◽  
Author(s):  
Ronald Estoque
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Global Scale ◽  
Full Potential ◽  
Observation Data ◽  
The Sustainable Development ◽  
Sustainability Concept ◽  
Development Goals ◽  
Earth Observation Data

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.

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

&lt;p&gt;Through Remote Sensing of Irish Surface Water (INFER) project, we are validating the algorithms to measure the &amp;#160;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

scholarly journals RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES

2020 ◽  
Vol V-2-2020 ◽  
pp. 845-852
Author(s):  
C. Hessel ◽  
R. Grompone von Gioi ◽  
J. M. Morel ◽  
G. Facciolo ◽  
P. Arias ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Correction ◽  
Linear Interpolation ◽  
Landsat 8 ◽  
Quality Metric ◽  
Multi Temporal ◽  
Reference Images ◽  
Random Sample Consensus ◽  
Affine Correction ◽  
Sentinel 2

Abstract. We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen by maximization of a quality metric. For each image, two affine correction models are robustly estimated using random sample consensus, using the two closest reference images; the final correction is obtained by linear interpolation. For each pair of source and reference images, pseudo-invariant features are obtained using a similarity measure invariant to radiometric changes. A final tone-mapping step outputs the images in the standard 8-bits range. This method is illustrated by the fusion of time series of Sentinel-2 at correction levels 1C, 2A, and Landsat-8 images. By using only the atmospherically corrected Sentinel-2 L2A images as anchors, the full output series inherits this atmospheric correction.

scholarly journals Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

2019 ◽  
Vol 11 (8) ◽  
pp. 907 ◽  
Author(s):  
Vasileios Syrris ◽  
Paul Hasenohr ◽  
Blagoj Delipetrev ◽  
Alexander Kotsev ◽  
Pieter Kempeneers ◽  
...  
Keyword(s):  
Random Forests ◽  
Computational Models ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Computational Aspects ◽  
Learning Set ◽  
Earth Observation Data ◽  
Class Segmentation ◽  
Sentinel 2

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

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