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Lidar-based Studies of Aerosol Optical Properties Over Coastal Areas

Tymon Zielinski, Bringfried Pflug
2007 Sensors  
Aerosol size distribution and concentration strongly depend on wind speed, direction, and measuring point location in the marine boundary layer over coastal areas. The marine aerosol particles which are found over the sea waves in high wind conditions affect visible and near infrared propagation for paths that pass very close to the surface as well as the remote sensing measurements of the sea surface. These particles are produced by various air sea interactions. This paper presents the results
more » ... of measurements taken at numerous coastal stations between 1992 and 2006 using an FLS-12 lidar system together with other supporting instrumentation. The investigations demonstrated that near-water layers in coastal areas differ significantly from those over open seas both in terms of structure and physical properties. Taking into consideration the above mentioned factors, aerosol concentrations and optical properties were determined in the marine boundary layer as a function of offshore distance and altitude at various coastal sites in two seasons. The lidar results show that the remote sensing algorithms used currently in coastal areas need verification and are not fully reliable.
doi:10.3390/s7123347 pmid:28903298 pmcid:PMC3841899 fatcat:5kmixf7qizd4lff3wtsjlltnva

Comparison of Masking Algorithms for Sentinel-2 Imagery

Viktoria Zekoll, Magdalena Main-Knorn, Jerome Louis, David Frantz, Rudolf Richter, Bringfried Pflug
2021 Remote Sensing  
Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask ("Function of mask" implemented in FORCE), ATCOR ("Atmospheric Correction") and Sen2Cor ("Sentinel-2 Correction") on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere
more » ... flectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or "truth". The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that "same area" is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.
doi:10.3390/rs13010137 fatcat:3g5hufrrqnbuvn5utaplfiudqy

PACO: Python-Based Atmospheric COrrection

Raquel de los de los Reyes, Maximilian Langheinrich, Peter Schwind, Rudolf Richter, Bringfried Pflug, Martin Bachmann, Rupert Müller, Emiliano Carmona, Viktoria Zekoll, Peter Reinartz
2020 Sensors  
The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of
more » ... Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.
doi:10.3390/s20051428 pmid:32151105 pmcid:PMC7085641 fatcat:sk7miyzpfjh7lbuib27el6tkry

Influence of the Solar Spectra Models on PACO Atmospheric Correction

Raquel De Los Reyes, Rudolf Richter, Martin Bachmann, Kevin Alonso, Bringfried Pflug, Bruno Lafrance, Peter Reinartz
2022 Remote Sensing  
The solar irradiance is the source of energy used by passive optical remote sensing to measure the ground reflectance and, from there, derive the ground properties. Therefore, the precise knowledge of the incoming solar irradiance is fundamental for the atmospheric correction (AC) algorithms. These algorithms use the simulation results of a model of the interactions of the atmosphere with the incoming solar irradiance to determine the atmospheric contribution of the remote sensing observations.
more » ... This study presents the differences in the atmospherically corrected ground reflectance of multi- and hyper-spectral sensors assuming three different solar models: Thuillier 2003, Fontenla 2011 and TSIS-1 HRS. The results show no difference when the solar irradiance model is preserved through the full processing chain. The differences appear when the solar irradiance model used in the atmospheric correction changes, and this difference is larger between some irrradiance models (e.g., TSIS and Thuillier 2003) than for others (e.g., Fontenla 2011 and TSIS).
doi:10.3390/rs14174237 fatcat:vmug757l55dtfp5mrp2xf2jhhe

Sen2Cor for Sentinel-2

Magdalena Main-Knorn, Bringfried Pflug, Jerome Louis, Vincent Debaecker, Uwe Müller-Wilm, Ferran Gascon, Lorenzo Bruzzone, Francesca Bovolo, Jon Atli Benediktsson
2017 Image and Signal Processing for Remote Sensing XXIII  
In the frame of the Copernicus programme, ESA has developed and launched the Sentinel-2 optical imaging mission that delivers optical data products designed to feed downstream services mainly related to land monitoring, emergency management and security. The Sentinel-2 mission is the constellation of two polar orbiting satellites Sentinel-2A and Sentinel-2B, each one equipped with an optical imaging sensor MSI (Multi-Spectral Instrument). Sentinel-2A was launched on June 23 rd , 2015 and
more » ... l-2B followed on March 7 th , 2017. With the beginning of the operational phase the constellation of both satellites enable image acquisition over the same area every 5 days or less. To use unique potential of the Sentinel-2 data for land applications and ensure the highest quality of scientific exploitation, accurate correction of satellite images for atmospheric effects is required. Therefore the atmospheric correction processor Sen2Cor was developed by Telespazio VEGA Deutschland GmbH on behalf of ESA. Sen2Cor is a Level-2A processor which main purpose is to correct single-date Sentinel-2 Level-1C Top-Of-Atmosphere (TOA) products from the effects of the atmosphere in order to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product. Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification (SCL) map with Quality Indicators for cloud and snow probabilities. Telespazio France and DLR have teamed up in order to provide the calibration and validation of the Sen2Cor processor. Here we provide an overview over the Sentinel-2 data, processor and products. It presents some processing examples of Sen2Cor applied to Sentinel-2 data, provides up-to-date information about the Sen2Cor release status and recent validation results at the time of the SPIE Remote Sensing 2017.
doi:10.1117/12.2278218 fatcat:lusbqmshubdevnsfjn6kqvghrq

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

Katja Dörnhöfer, Anna Göritz, Peter Gege, Bringfried Pflug, Natascha Oppelt
2016 Remote Sensing  
Author Contributions: Katja Dörnhöfer, Peter Gege, Bringfried Pflug, Anna Göritz and Natascha Oppelt conceived and designed the study.  ...  Bringfried Pflug conducted Sen2Cor processing. Peter Gege and Anna Göritz performed and analysed in situ measurements. All authors contributed equally in reviewing and finalizing the manuscript.  ... 
doi:10.3390/rs8110941 fatcat:b2ljrn57hzc7lghxgoavby7atq

Atmospheric Correction Inter-Comparison Exercise

Georgia Doxani, Eric Vermote, Jean-Claude Roger, Ferran Gascon, Stefan Adriaensen, David Frantz, Olivier Hagolle, André Hollstein, Grit Kirches, Fuqin Li, Jérôme Louis, Antoine Mangin (+3 others)
2018 Remote Sensing  
The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of an AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate this challenge, the inter-comparison
more » ... col and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be investigated in future ACIX experiments.
doi:10.3390/rs10020352 pmid:32704392 pmcid:PMC7376715 fatcat:tko2mssr4zbhjmyld7dca2q6qy

Comparing Atmospheric Correction Performance for Sentinel-2 and Landsat-8 Data

Bringfried Pflug, Rudolf Richter, Raquel de los Reyes, Peter Reinartz
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
CORRECTION PERFORMANCE FOR SENTINEL-2 AND LANDSAT-8 DATA Fig. 1: Spectral band position of Sentinel-2-MSI and Landsat-8-OLI; Radiometric accuracy of both imaging instruments agrees within 3% [1] Contact: Bringfried  ...  Pflug ( CONCLUSION Atmospheric correction with ATCOR gives consistent results between Sentinel-2 and Landsat-8 data for the investigated example.  ... 
doi:10.1109/igarss.2019.8900095 dblp:conf/igarss/PflugRRR19 fatcat:5fr3qicisra7flvvljw2iwj5rm

Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data

Sébastien Saunier, Bringfried Pflug, Italo Moletto Lobos, Belen Franch, Jérôme Louis, Raquel De Los Reyes, Vincent Debaecker, Enrico G. Cadau, Valentina Boccia, Ferran Gascon, Sultan Kocaman
2022 Remote Sensing  
Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this
more » ... text, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes.
doi:10.3390/rs14163855 fatcat:sejb5i4vbjhtzeozibnkyelzy4

Evolutions of Sentinel-2 Level-2A Cloud Masking Algorithm Sen2Cor Prototype First Results

Jerome Louis, Bringfried Pflug, Vincent Debaecker, Uwe Mueller-Wilm, Rosario Quirino Iannone, Valentina Boccia, Ferran Gascon
2021 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS  
Reliable cloud screening remains a critical issue for the analysis of optical imagery. In this work we present the recent evolutions of the Cloud Screening and Scene Classification module of Sen2Cor processor for the Copernicus Sentinel-2 mission. In addition to a Level-2A surface reflectance product, Sen2Cor provides a Scene Classification (SCL) map divided into 11 classes. The information provided by this map is of great interest for automated processing chains. It can be used to mask out
more » ... ds, cloud shadow, water, snow/ice from the Sentinel-2 imagery, so that downstream processing can be performed on clear land pixels, suitable for time-series analysis and quantitative remote sensing. The performance and limitations of the current algorithm are recalled. The updates aimed at improving the overall accuracy of the cloud screening are described (efficient topographic shadows computation, mitigation of false snow detection in clouds and mitigation of false clouds detection on bright targets). Preliminary results based on a Sen2Cor prototype are discussed.
doi:10.1109/igarss47720.2021.9553445 fatcat:7cmbd5z43rhkhjlzfwa35idfc4

Next updates of atmospheric correction processor Sen2Cor

Bringfried Pflug, Jérôme Louis, Vincent Debaecker, Uwe Müller-Wilm, Carine Quang, Ferran Gascon, Valentina Boccia, Claudia Notarnicola, Fabio Bovenga, Lorenzo Bruzzone, Francesca Bovolo, Jon Atli Benediktsson (+2 others)
2020 Image and Signal Processing for Remote Sensing XXVI  
The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry. The mission is also used to monitor coastal and inland waters and is useful for risk and disaster mapping. The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging
more » ... r MSI (Multi-Spectral Instrument) which acquires optical data products with spatial resolution up to 10 m. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process monotemporal single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map for further processing. The paper will give an overview of the Level-2A product content and up-to-date information about the data quality of the Level-2A products generated with Sen2Cor 2.8 in terms of Cloud Screening and Atmospheric Correction. In addition the paper gives an outlook on the next updates of Sen2Cor and their impact on Level-2A Data Quality.
doi:10.1117/12.2574035 fatcat:pvj6ncizaneepaogw7w3updoaa

Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor

Jerome Louis, Bringfried Pflug, Magdalena Main-Knorn, Vincent Debaecker, Uwe Mueller-Wilm, Rosario Quirino Iannone, Enrico Giuseppe Cadau, Valentina Boccia, Ferran Gascon
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
Sen2Cor is a Level-2A processor designed to correct Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product. ESA has been using Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe since June 2017. It has since then been successfully integrated into Sentinel-2 ground segment (PDGS) with a global production over the World started in December 2018. In this manuscript, the Level-2A product and algorithm
more » ... are presented. The performance of this operational Level-2A product is described in terms of cloud screening accuracy and atmospheric correction accuracy. Finally, the on-going parallel developments aimed at improving the product quality at global scale in terms of cloud screening and atmospheric correction are discussed.
doi:10.1109/igarss.2019.8898540 dblp:conf/igarss/LouisPMDMICBG19 fatcat:5z7braebujashbb2ubfi5mpbny

Validation of atmospheric correction algorithm ATCOR

Bringfried Pflug, Magdalena Main-Knorn, Adolfo Comerón, Evgueni I. Kassianov, Klaus Schäfer, Richard H. Picard, Karin Stein, John D. Gonglewski
2014 Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII  
Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The largest uncertainty in atmospheric correction arises out of spatial and temporal variation of aerosol amount and type. Therefore validation of aerosol estimation is one important step in validation of atmospheric correction algorithms. Our ground-based measurements of aerosol-optical thickness spectra (AOT) were performed
more » ... y to overpasses of satellites Rapid-Eye and Landsat. Validation of aerosol retrieval by the widely used atmospheric correction tool ATCOR 1,2 was then realized by comparison of AOT derived from satellite data with the ground-truths. Mean uncertainty is ΔAOT550 ≈ 0.04, corresponding approximately to uncertainty in surface albedo of Δρ ≈ 0.004. Generally, ATCOR-derived AOT values are mostly overestimated when compared to the ground-truth measurements. Very little differences are found between Rapid-Eye and Landsat sensors. Differences between using rural and maritime aerosols are negligible within the visible spectral range.
doi:10.1117/12.2067435 fatcat:tqddwkzas5huhhnnmtfpho3pj4

Copernicus Sentinel-2A Calibration and Products Validation Status

Ferran Gascon, Catherine Bouzinac, Olivier Thépaut, Mathieu Jung, Benjamin Francesconi, Jérôme Louis, Vincent Lonjou, Bruno Lafrance, Stéphane Massera, Angélique Gaudel-Vacaresse, Florie Languille, Bahjat Alhammoud (+11 others)
2017 Remote Sensing  
As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results
more » ... d in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects. between minimizing cloud cover and ensuring suitable sun illumination. An overview of the MSI imaging payload is provided in the following section. The Sentinel-2 satellites will systematically acquire observations over land and coastal areas from −56 ° to 84 ° latitude including islands larger 100 km 2 , EU islands, all other islands less than 20 km from the coastline, the whole Mediterranean Sea, all inland water bodies and all closed seas. Over specific calibration sites, for example DOME-C in Antarctica, additional observations will be made. The two satellite units will work on opposite sides of the orbit. Sentinel-2A launch took place in June 2015 and Sentinel-2B is foreseen beginning 2017. Therefore, this paper focuses only on the performances achieved by Sentinel-2A. The availability of products with good data quality performances (both in terms of radiometry and geometry accuracies) has a paramount importance for many applications. This is indeed a key enabling factor for an easier exploitation of time-series, inter-comparison of measurements from different sensors or detection of changes in the landscape. Calibration and validation (Cal/Val) corresponds to the process of updating and validating on-board and on-ground configuration parameters and algorithms to ensure that the product data quality requirements are met. This paper provides a description of the calibration activities and the current status, one year after Sentinel-2A launch, of the mission products validation activities. Measured performances, derived from the validation activities, have been estimated for both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products (referred respectively as Level-1 and Level-2A and further described later in this paper). . Multi-Spectral Instrument Overview This section provides a brief overview of Sentinel-2 Multi-Spectral Instrument (MSI). It aims at giving to the reader the basis required to fully understand the measured performances and the Calibration and Validation (Cal/Val) approach. MSI Design The MSI instrument design has been driven by the large swath requirement together with the demanding geometrical and spectral performances of the measurements. It is based on a push-broom concept, featuring a Three-Mirror Anastigmatic (TMA) telescope feeding two focal planes spectrally separated by a dichroic filter, as shown on Figure 1 . One focal plane includes the Visible and Near-Infrared (VNIR) bands and the other one the Short-Wave Infrared (SWIR) bands. Figure 1. MSI internal configuration. On the left, full instrument view and optical path construction to the SWIR/VNIR (see §2.2) splitter and focal planes. Spectral Bands and Resolution Preprints ( | NOT PEER-REVIEWED | Posted: Overlapping area = 98 pixels @ 20m 10 bands B/H cross-detector B/H cross-band Nb pixels / detector module: 2592 (10m) or 1296 (20m -60m) Satellite track YLOS XLOS ZLOS ψX ψY Viewing direction Preprints ( | NOT PEER-REVIEWED | Posted:
doi:10.3390/rs9060584 fatcat:fglsduu6g5bjpd6nmxpspa2hfy

Ground based measurements of aerosol properties using Microtops instruments

Bringfried Pflug
The contribution of aerosols to the signals at top of the atmosphere must be accounted for remote sensing of the ocean and land surface, which is known as atmospheric correction. Validation of atmospheric correction procedures require ground based measurements of aerosol properties. Ground based measurements of aerosol properties give also a basis for validation of the aerosol models used by atmospheric correction algorithms. Ground based measurements of aerosol properties have been performed
more » ... the coastal area of the southern Baltic Sea and near Berlin with a Microtops II Sunphotometer and a Microtops II ozone monitor, both onboard a ship and on the land surface. The present paper gives examples of atmospheric parameters which can be obtained from Microtops measurements and reports some experience how to perform and analyze these measurements. Then application of these results is demonstrated with 2 examples. Validation of atmospheric correction algorithms is demonstrated with a comparison of aerosol optical thickness resulting from satellite data with total column aerosol optical thickness from ground based measurements at time of satellite overpass. The agreement is better than 0.03 at 750 nm. Another example uses the aerosol properties found from ground based measurements as input to radiative transfer modeling of the signals received at satellite. The agreement between modeled and measured signals is fine within the expectable uncertainty.
doi:10.1063/1.4804838 fatcat:ohnl2h54e5brzmndvjjr4q7j3y
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