scholarly journals STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

2020 ◽  
Vol 12 (19) ◽  
pp. 3209
Author(s):  
Yunan Luo ◽  
Kaiyu Guan ◽  
Jian Peng ◽  
Sibo Wang ◽  
Yizhi Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Data ◽  
Temporal Frequency ◽  
Fusion Product ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Land Surfaces ◽  
Fine Resolution ◽  
Sentinel 2

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.

scholarly journals Retrieval of aerosol optical depth over land surfaces from AVHRR data

2013 ◽  
Vol 6 (1) ◽  
pp. 2227-2251 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
A. A. Kokhanovsky ◽  
W. von Hoyningen-Huene ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Land Surfaces ◽  
Very High

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09) shows a strong correlation (R = 0.7835). Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR) data. A simplified Look-Up Table (LUT) method, adopted from Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE) = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to geostationary satellites.

Potential of Sentinel and high resolution EO data for monitoring nature protection in cities – the SeMoNa22 project

2021 ◽  
Author(s):  
Anna Iglseder ◽  
Markus Immitzer ◽  
Christoph Bauerhansl ◽  
Hannes Hoffert-Hösl ◽  
Klaus Kramer ◽  
...  
Keyword(s):  
Time Series ◽  
Nature Conservation ◽  
High Resolution ◽  
Satellite Data ◽  
Laser Scanning ◽  
Object Oriented ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data ◽  
Sentinel 2

<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>

scholarly journals Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

2020 ◽  
Vol 12 (5) ◽  
pp. 833
Author(s):  
Rui Song ◽  
Jan-Peter Muller ◽  
Said Kharbouche ◽  
Feng Yin ◽  
William Woodgate ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Albedo ◽  
Spatial Scales ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Hemispherical Reflectance ◽  
Land Surfaces ◽  
Sentinel 2

Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).

Operational Validation of Space-based Albedo Products from Upscaled Tower-based Albedometer Measurements

2020 ◽  
Author(s):  
Rui Song ◽  
Jan-Peter Muller
Keyword(s):  
High Resolution ◽  
Surface Albedo ◽  
Atmospheric Correction ◽  
Ground Level ◽  
Correction Method ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces ◽  
Sentinel 2

<p>Surface albedo is a fundamental radiative parameter which controls the Earth’s energy budget by determining the amount of solar radiation which is either absorbed by the surface or reflected back to atmosphere. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their repeated global coverage. In this work, a new method of upscaling surface albedo from ground level footprints of a few tens of metres to coarse satellite scales (≈1km) is reported [1]. Tower-mounted albedometer measurements are upscaled and used to validate global space-based albedo products, including Copernicus Global Land Service (CGLS) 1km albedo data (from Proba-V and previously form VEGETATION-2), MODerate resolution Imaging Spectroradiometer (MODIS) 500m albedo data, and Multi-angle Imaging SpectroRadiometer (MISR) 1.1km albedo data. MODIS albedo retrievals show the closest agreement with tower measurements, followed by the MISR retrievals, and then followed by the CGLS retrievals. The upscaling method uses high-resolution surface reflectance retrievals (from Landsat-8, Sentinel-2) to fill the spatial gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. High-resolution surface albedo products are generated by combining high-resolution surface reflectance data and MODIS bi-directional reflectance distribution function (BRDF) climatology data. This upscaling framework also uses a novel Sensor Invariant Atmospheric Correction (SIAC) method [2] to improve the accuracy of upscaled tower albedo values. Total uncertainties of upscaled albedo products are estimated by considering uncertainties from both the tower albedometer raw measurements and SIAC atmospheric corrections. This surface albedo upscaling method can be used over both heterogenous and homogenous land surfaces, and has been examined at the SURFRAD, BSRN and FLUXNET tower sites.</p><p><strong>Keywords</strong>: surface albedo, upscale, CGLS, MODIS, MISR, SIAC</p><p>[1] Song, R.; Muller, J.-P.; Kharbouche, S.; Woodgate, W. Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements. Remote Sens. 2019, 11, 644, doi:10.3390/rs11060644.</p><p>[2] Yin, F., Lewis, P. E., Gomez-Dans, J., & Wu, Q. A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv 2019, https://doi.org/10.31223/osf.io/ps957.</p>

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

<p>A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.</p>

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

2020 ◽  
Author(s):  
Victor Bacu ◽  
Teodor Stefanut ◽  
Dorian Gorgan
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Field Measurements ◽  
Soil Classification ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Spatial Features ◽  
Learning Techniques ◽  
Sentinel 2

&lt;p&gt;Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.&lt;/p&gt;&lt;p&gt;Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).&lt;/p&gt;&lt;p&gt;The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., &amp;#8220;Soil Classification Techniques in Transylvania Area Based on Satellite Data&amp;#8221;. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).&lt;/p&gt;&lt;p&gt;[2] Bacu V., Stefanut T., Gorgan D., &amp;#8220;Building soil classification maps using HorusApp and Sentinel-2 Products&amp;#8221;, Proceedings of the Intelligent Computer Communication and Processing Conference &amp;#8211; ICCP, in IEEE press (2019).&lt;/p&gt;&lt;p&gt;[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., &amp;#8220;Soil classification based on Sentinel-2 Products using HorusApp application&amp;#8221;, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).&lt;/p&gt;

scholarly journals Description and validation of an AOT product over land at the 0.6 μm channel of the SEVIRI sensor onboard MSG

2011 ◽  
Vol 4 (11) ◽  
pp. 2543-2565 ◽  
Author(s):  
E. Bernard ◽  
C. Moulin ◽  
D. Ramon ◽  
D. Jolivet ◽  
J. Riedi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Weather Prediction ◽  
Temporal Frequency ◽  
Reasonable Assumption ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Aerosol Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Level 2 ◽  
Good Agreement

Abstract. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) launched in 2003 by EUMETSAT is dedicated to the Nowcasting applications and Numerical Weather Prediction and to the provision of observations for climate monitoring and research. We use the data in visible and near infrared (NIR) channels to derive the aerosol optical thickness (AOT) over land. The algorithm is based on the assumption that the top of the atmosphere (TOA) reflectance increases with the aerosol load. This is a reasonable assumption except in case of absorbing aerosols above bright surfaces. We assume that the minimum in a 14-days time series of the TOA reflectance is, once corrected from gaseous scattering and absorption, representative of the surface reflectance. The AOT and the aerosol model (a set of 5 models is used), are retrieved by matching the simulated TOA reflectance with the TOA reflectances measured by SEVIRI in its visible and NIR spectral bands. The high temporal resolution of the data acquisition by SEVIRI allows to retrieve the AOT every 15 min with a spatial resolution of 3 km at sub-satellite point, over the entire SEVIRI disk covering Europe, Africa and part of South America. The resulting AOT, a level 2 product at the native temporal and spatial SEVIRI resolutions, is presented and evaluated in this paper. The AOT has been validated using ground based measurements from AErosol RObotic NETwork (AERONET), a sun-photometer network, focusing over Europe for 3 months in 2006. The SEVIRI estimates correlate well with the AERONET measurements, r = 0.64, with a slight overestimate, bias = −0.017. The sources of errors are mainly the cloud contamination and the bad estimation of the surface reflectance. The temporal evolutions exhibited by both datasets show very good agreement which allows to conclude that the AOT Level 2 product from SEVIRI can be used to quantify the aerosol content and to monitor its daily evolution with a high temporal frequency. The comparison with daily maps of Moderate Resolution Imaging Spectroradiometer (MODIS) AOT level 3 product shows qualitative good agreement in the retrieved geographic patterns of AOT. Given the high spatial and temporal resolutions obtained with this approach, our results have clear potential for applications ranging from air quality monitoring to climate studies. This paper presents a first evaluation and validation of the derived AOT over Europe in order to document the overall quality of a product that will be made publicly available to the users of the aforementioned research communities.

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