Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery

2016 ◽  
Author(s):  
Giannis Lantzanakis ◽  
Zina Mitraka ◽  
Nektarios Chrysoulakis
Keyword(s):  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Sentinel 2

Atmospheric Correction Method for Sentinel-2 Satellite Imagery

2018 ◽  
Vol 38 (1) ◽  
pp. 0128001 ◽  
Author(s):  
苏伟 Su Wei ◽  
张明政 Zhang Mingzheng ◽  
蒋坤萍 Jiang Kunping ◽  
朱德海 Zhu Dehai ◽  
黄健熙 Huang Jianxi ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Correction Method ◽  
Sentinel 2

scholarly journals Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

2021 ◽  
Vol 13 (18) ◽  
pp. 3550
Author(s):  
David Moravec ◽  
Jan Komárek ◽  
Serafín López-Cuervo Medina ◽  
Iñigo Molina
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Atmospheric Correction ◽  
Correction Method ◽  
Landsat 8 ◽  
Substantial Impact ◽  
Corrected Image ◽  
Normalised Difference Vegetation Index ◽  
Urban Rural ◽  
Sentinel 2

Sentinel-2 and Landsat 8 satellites constitute an unprecedented source of freely accessible satellite imagery. To produce precise outputs from the satellite data, however, proper use of atmospheric correction methods is crucial. In this work, we tested the performance of six different atmospheric correction methods (QUAC, FLAASH, DOS, ACOLITE, 6S, and Sen2Cor), together with atmospheric correction given by providers, non-corrected image, and images acquired using an unmanned aerial vehicle while working with the normalised difference vegetation index (NDVI) as the most widely used index. We tested their performance across urban, rural, and vegetated land cover types. Our results show a substantial impact from the choice of the atmospheric correction method on the resulting NDVI. Moreover, we demonstrate that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes

2018 ◽  
Vol 73 ◽  
pp. 63-76 ◽  
Author(s):  
Ion Sola ◽  
Alberto García-Martín ◽  
Leire Sandonís-Pozo ◽  
Jesús Álvarez-Mozos ◽  
Fernando Pérez-Cabello ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Mediterranean Landscapes ◽  
Sentinel 2

Sentinel-2 mission Contribution for Supporting Bathymetric layers of SAFAGA coastal zone, Egypt

2021 ◽  
Author(s):  
Ramez Saeed ◽  
Saad Abdelrahman ◽  
Andrea Scozari ◽  
Abdelazim Negm
Keyword(s):  
Red Sea ◽  
Satellite Imagery ◽  
Data Science ◽  
New Technologies ◽  
Coefficient Of Determination ◽  
Change Environment ◽  
Bathymetric Data ◽  
Work Supports ◽  
Sentinel 2

&lt;p&gt;&lt;strong&gt;ABSTRACT&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;With the fast and highly growing demand for all possible ways of remote work as a result of COVID19 pandemic, new technologies using Satellite data were highly encouraged for multidisciplinary applications in different fields such as; agriculture, climate change, environment, coastal management, maritime, security and Blue Economy.&lt;/p&gt;&lt;p&gt;This work supports applying Satellite Derived Bathymetry (SDB) with the available low-cost multispectral satellite imagery applications, instruments and readily accessible data for different areas with only their benthic parameters, water characteristics and atmospheric conditions.&amp;#160; The main goal of this work is to derive bathymetric data needed for different hydrographic applications, such as: nautical charting, coastal engineering, water quality monitoring, sediment movement monitoring and supporting both green carbon and marine data science. &amp;#160;Also, this work proposes and assesses a SDB procedure that makes use of publicly-available multispectral satellite images (Sentinel2 MSI) and applies algorithms available in the SNAP software package for extracting bathymetry and supporting bathymetric layers against highly expensive traditional in-situ hydrographic surveys. The procedure was applied at SAFAGA harbor area, located south of Hurghada at (26&amp;#176;44&amp;#8242;N, 33&amp;#176;56&amp;#8242;E), on the Egyptian Red Sea coast.&amp;#160; SAFAGA controls important maritime traffic line in Red Sea such as (Safaga &amp;#8211; Deba, Saudi Arabia) maritime cruises. &amp;#160;SAFAGA depths change between 6 m to 22m surrounded by many shoal batches and confined waters that largely affect maritime safety of navigation.&amp;#160; Therefore, there is always a high demand for updated nautical charts which this work supports.&amp;#160; The outcome of this work provides and fulfils those demands with bathymetric layers data for the approach channel and harbour usage bands electronic nautical chart of SAFAGA with reasonable accuracies. &amp;#160;The coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) differs between 0.42 to 0.71 after applying water column correction by Lyzenga algorithm and deriving bathymetric data depending on reflectance /radiance of optical imagery collected by sentinel2 missions with in-situ depth data values relationship by Stumpf equation.&amp;#160; The adopted approach proved to give&amp;#160; highly reasonable results that could be used in nautical charts compilation. Similar methodologies could be applied to inland water bodies. &amp;#160;This study is part of the MSc Thesis of the first author and is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: Algorithm, Bathymetry, Sentinel 2, nautical charting, Safaga port, satellite imagery, water depth, Egypt.&lt;/strong&gt;&lt;/p&gt;

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