Statistical cloud detection from SEVIRI multispectral images

Amato, Umberto; Antoniadis, Anestis; Cuomo, V.; Cutillo, Luisa; Franzese, Monica; Murino, Loredana; Serio, Carmine

doi:10.1016/j.rse.2007.06.004

Cited by 64 publications

(30 citation statements)

References 24 publications

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“…In the same case the position as well as the length of these clouds is misinterpreted and the CFC is overestimated. Amato et al (2008) performed a statistical analysis of cloud detection from SEVIRI imagery. Their discriminant analysis showed a good performance in cloud detection.…”

Section: A Werkmeister Et Al: Comparing Cloud Coverage -A Case Studymentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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Abstract. In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively.Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14 % for AVHRR and 8 ± 16 % for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22 %) than instantaneous SEVIRI cloud fraction estimates (8 ± 29 %) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94 % and false alarm rates between 21 and 29 % for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76 %. We find poor skill, however, whenever broken clouds occur (probability of detection is 32 % for AVHRR and 12 % for SEVIRI in Hannover, Germany).In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0 %) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each "cloud-contaminated" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.

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Section: A Werkmeister Et Al: Comparing Cloud Coverage -A Case Studymentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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“…Supervised methods have been used in the task of cloud detection, such as discriminant analysis [190], support vector machines [191] and neural networks [192]. Unsupervised classification has been applied to this task using features such as brightness, whiteness, oxygen and water vapour absorption [193].…”

Section: Clouds and Adjacency Effectsmentioning

confidence: 99%

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

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Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

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“…The CDA is extended in this paper to more statistics assuming their independence. To partially overcome the approximation of independence, a principal component analysis prior transform has been applied as in Amato et al (2008), giving rise to a very fast and accurate methodology. By its very construction, the methodology naturally provides a quality indicator of the retrieved status for each pixel (clear or cloudy).…”

Section: U Amato: Cloud Mask Via Cumulative Discriminant Analysismentioning

confidence: 99%

Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

Amato¹,

Lavanant

Liuzzi

et al. 2014

Atmos. Meas. Tech.

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Abstract. We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

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Statistical cloud detection from SEVIRI multispectral images

Cited by 64 publications

References 24 publications

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

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