scholarly journals A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images

2015 ◽  
Vol 7 (3) ◽  
pp. 2668-2691 ◽  
Author(s):  
Olivier Hagolle ◽  
Mireille Huc ◽  
David Villa Pascual ◽  
Gerard Dedieu
Keyword(s):  
Spectral Method ◽  
Optical Thickness ◽  
Atmospheric Correction ◽  
Aerosol Optical Thickness ◽  
Multi Temporal ◽  
Sentinel 2

scholarly journals AEROSOL PLUMES CHARACTERIZATION BY HYPERSPECTRAL IMAGES COUPLED WITH SENTINEL-2 PRODUCTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 791-797
Author(s):  
G. Calassou ◽  
P.-Y. Foucher ◽  
J.-F. Leon
Keyword(s):  
Optical Thickness ◽  
Atmospheric Correction ◽  
Principal Component ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Multispectral Data ◽  
The Difference ◽  
Industrial Aerosol ◽  
Sentinel 2

Abstract. In this paper, we focus on the retrieval of microphysical and optical properties of industrial aerosol plumes through a process using airborne hyperspectral and Sentinel-2 multi-spectral images. The process allows first to perform atmospheric correction and second to determine background aerosols thanks to a comparison between hyperspectral and Sentinel-2 reflectances. Hyperspectral methodologies use the radiance differential between the measurement in the plume and the corresponding measurements out of the plume to estimate plume properties. To retrieve the surface reflectance under the plume, a principal component analysis coupling hyperspectral and multispectral data class by class is achieved. The developed method aims to compare the difference between measured and estimated reflectance with a radiative transfer model accounting for plume properties (particle radius and aerosol optical thickness of the plume). We have applied the method to a steel plant in the south of France. The retrieved plume show an aerosol mean radius between 0.05 and 0.2 µm with a mean aerosol optical thickness about 0.05 along the plume.

scholarly journals Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018

2019 ◽  
Vol 11 (14) ◽  
pp. 1649 ◽  
Author(s):  
María Ángeles Obregón ◽  
Gonçalo Rodrigues ◽  
Maria Joao Costa ◽  
Miguel Potes ◽  
Ana Maria Silva
Keyword(s):  
Water Vapour ◽  
Root Mean Square ◽  
Optical Thickness ◽  
Spectral Response ◽  
Aerosol Optical Thickness ◽  
Mean Bias Error ◽  
Mean Square ◽  
Response Functions ◽  
Mean Square Errors ◽  
Sentinel 2

This study presents a validation of aerosol optical thickness (AOT) and integrated water vapour (IWV) products provided by the European Space Agency (ESA) from multi-spectral imager (MSI) measurements on board the Sentinel-2 satellite (ESA-L2A). For that purpose, data from 94 Aerosol Robotic Network (AERONET) stations over Europe and adjacent regions, covering a wide geographical region with a variety of climate and environmental conditions and during the period between March 2017 and December 2018 have been used. The comparison between ESA-L2A and AERONET shows a better agreement for IWV than the AOT, with normalized root mean square errors (NRMSE) of 5.33% and 9.04%, respectively. This conclusion is also reflected in the values of R2, which are 0.99 and 0.65 for IWV and AOT, respectively. The study period was divided into two sub-periods, before and after 15 January 2018, when the Sentinel-2A spectral response functions of bands 1 and 2 (centered at 443 and 492 nm) were updated by ESA, in order to investigate if the lack of agreement in the AOT values was connected to the use of incorrect spectral response functions. The comparison of ESA-L2A AOT with AERONET measurements showed a better agreement for the second sub-period, with root mean square error (RMSE) values of 0.08 in comparison with 0.14 in the first sub-period. This same conclusion was attained considering mean bias error (MBE) values that decreased from 0.09 to 0.01. The ESA-L2A AOT values estimated with the new spectral response functions were closer to the correspondent reference AERONET values than the ones obtained using the previous spectral response functions. IWV was not affected by this change since the retrieval algorithm does not use bands 1 and 2 of Sentinel-2. Additionally, an analysis of potential uncertainty sources to several factors affecting the AOT comparison is presented and recommendations regarding the use of ESA-L2A AOT dataset are given.

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 Simultaneous determination of aerosol optical thickness and water leaving radiance from multispectral measurements in coastal waters

2017 ◽  
Author(s):  
Chong Shi ◽  
Teruyuki Nakajima
Keyword(s):  
Simultaneous Determination ◽  
Optical Thickness ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Ocean Surface ◽  
Aerosol Optical Thickness ◽  
Surface Model ◽  
Spectral Measurements ◽  
Mean Square Errors

Abstract. Retrieval of aerosol optical properties and water leaving radiance over ocean is changeling since the latter mostly accounts for ~10% of satellite observed signal and can be easily contaminated by the atmospheric scattering. Such an effort would be more difficulty in turbid coastal waters due to the existence of optically complex oceanic substances or high aerosol loading. In an effort to solve such problems, we present an optimization approach for the simultaneous determination of aerosol optical thickness (AOT) and normalized water leaving radiance (nLw) from multi-spectral measurements. In this algorithm, a coupled atmosphere-ocean radiative transfer model combined with a comprehensive bio-optical oceanic module is used to jointly simulate the satellite observed reflectance at the top of atmosphere and water leaving radiance just above the ocean surface. Then a full-physical nonlinear optimization method is adopted to retrieve AOT and nLw in one step. The algorithm is validated using Aerosol Robotic Network Ocean Color (AERONET-OC) products selected from eight OC sites distributed over different waters, consisting of observation cases covered both in and out of sun glint from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Results show a good consistency between retrieved and in situ measurements in each site. It is demonstrated that more accurate AOT are determined based on the simultaneous retrieval method, particularly in shorter wavelengths and sun glint conditions, where the averaged percentage difference (APD) of retrieved AOT generally reduce by approximate 10 % in visible bands compared with those derived from the standard atmospheric correction (AC) scheme. It is caused that all the spectral measurements can be used jointly to increase the information content in the inversion of AOT and the wind speed is also simultaneously retrieved to compensate the specular reflectance error estimated from the rough ocean surface model. For the retrieval of nLw, over atmospheric correction can be avoided to have a significant improvement for the inversion of nLw at 412 nm. Furthermore, generally better estimates of band ratios of nLw(443)/nLw(554) and nLw(488)/nLw(554), which are employed in the inversion of chlorophyll a concentration (Chl), are obtained using simultaneous retrieval approach with less root mean square errors and relative differences than those derived from the standard AC approach in comparison to the AERONET-OC products, as a result that the APD value of retrieved Chl decreases by about 5 %. On the other hand, the standard AC scheme yields a more accurate retrieval of nLw at 488 nm, prompting a further optimization of oceanic bio-optical module of current model.

scholarly journals SENTINEL-2 SURFACE REFLECTANCE PRODUCTS GENERATED BY CNES AND DLR: METHODS, VALIDATION AND APPLICATIONS

2021 ◽  
Vol V-1-2021 ◽  
pp. 9-15
Author(s):  
O. Hagolle ◽  
J. Colin ◽  
S. Coustance ◽  
P. Kettig ◽  
P. D’Angelo ◽  
...  
Keyword(s):  
Western Europe ◽  
Atmospheric Correction ◽  
Weighted Average ◽  
Data Sets ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
In Situ Data ◽  
Multi Temporal ◽  
Cloud Mask ◽  
Sentinel 2

Abstract. To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.

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