Spinning Enhanced Visible And Infrared Imager Imagery Research Articles

A comprehensive geometric quality assessment approach for MSG SEVIRI imagery

Geometric Quality Assessment (GQA) of Earth Observation satellites is crucial for monitoring the system performance and anticipate the accuracy of their products. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensors aboard the geostationary Meteosat satellites of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) acquire multispectral images at 5 min (rapid-scan) and 15 min (full-disk scan) intervals in whiskbroom fashion. The existing GQA method used for the SEVIRI images involves matching of landmark points located on the shorelines, which prevents from comprehensive analysis inland. Limitations on detecting further landmarks (keypoints) in the images arise from low spatial resolution (1 and 3 km), large radiometric differences between the spectral bands and low textural content, which is even poorer for night time thermal infrared images. Within the present study initiated by EUMETSAT, a new approach for SEVIRI Level 1.5 imagery was developed for the relative, absolute and interband GQA by using dense image matching with sub-pixel precision. In the study, various image filtering and texture extraction methods were evaluated to obtain dense and reliable keypoints over the full-disk image area. Global image mosaics with superior spatial resolution and accuracy were employed as external reference. 2D shifts between search and reference images were determined the using a sub-pixel image matching technique together with a robust outlier elimination strategy. The results show that the textural information in SEVIRI images can be enhanced greatly by using various filtering methods, such as Laplacian, Sobel, Wallis and Local Binary Patterns. The Laplacian filter was found more suitable for the absolute GQA of infrared band images, which was the most difficult to band to assess. Depending on the cloud cover, thousands of well-distributed keypoints can be detected and employed as independent check points in the absolute GQA process. With the developed GQA Tool, new acquisitions can be assessed rapidly and various accuracy measures on the image geometric quality can also be achieved.

An Assessment of SEVIRI Imagery at Various Temporal Resolutions and the Effect on Accurate Dust Emission Mapping

This paper evaluates the use of the ‘Dust red/green/blue (RGB)’ product derived from Spinning Enhanced Visible and Infrared Imager (SEVIRI) data at 15-min, 30-min, and 60-min temporal resolutions, for monitoring dust emissions in the Middle East. From January 2006 to December 2006, observations of dust emission point sources were recorded at each temporal resolution across the Middle East. Previous work has demonstrated that using SEVIRI data is a major improvement on other remote sensing methods for mapping dust sources in the Sahara, by enabling dust-storm observations through sequential images, back to the point of first emission. However, the highest temporal resolution available (15-min observations) produces 96 images per day, resulting in significantly higher data management requirements than data provided at 30-min and 60-min intervals. To optimize future research workflows, this paper investigates the effect of lowering the temporal resolution on the number and spatial distribution of observed dust emission events in the Middle East. The results show that the number of events observed reduced by 17% for 30-min resolution and 50% for 60-min resolution. These differences change seasonally, with the highest reduction observed in summer (34% and 64% reduction, respectively).

Novel WkNN-based technique to improve instantaneous rainfall estimation over the north of Algeria using the multispectral MSG SEVIRI imagery

For the estimation of rainfall in northern Algeria, a new method is proposed in this study. It based on the k nearest weighted neighbours (WkNN) classification algorithm using the dataset from the radiometer-imager SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) boarded on the satellite MSG (Meteosat Second Generation) to determine the pixel rainfall rate among the 16 predefined intensity levels observed in the Sétif meteorological radar.The implementation of the classification consists in using the spectral characteristics of a new sample (pixel) as input variables of the WkNN classifier to predict his class of membership based on the weighted distances separating him from the samples of the set of learning. The results thus obtained are validated with respect to the rainfall intensity classes observed and co-located by ground radar. The results showed a significant improvement for instantaneous estimate of precipitation during the day, with a correlation coefficient r=0.87 and statistical parameters RMSE=2.29mm/h,Bias=0.45mm/h,MAE=0.35mm/h, and we have obtained the results similar overnight.

The influence of dust optical properties on the colour of simulated MSG-SEVIRI Desert Dust infrared imagery

Abstract. Satellite imagery of atmospheric mineral dust is sensitive to the optical properties of the dust, governed by the mineral refractive indices, particle size, and particle shape. In infrared channels the imagery is also sensitive to the dust layer height and to the surface and atmospheric environment. Simulations of mineral dust in infrared Desert Dust imagery from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) have been performed, using the COSMO-MUSCAT (COSMO: COnsortium for Small-scale MOdelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) dust transport model and the Radiative Transfer for TOVS (RTTOV) program, in order to investigate the sensitivity of the imagery to assumed dust properties. This paper introduces the technique and performs initial validation and comparisons with SEVIRI measurements over North Africa for daytime hours during 6 months covering June and July of 2011–2013. Using T-matrix scattering theory and assuming the dust particles to be spherical or spheroidal, wavelength- and size-dependent dust extinction values are calculated for a number of different dust refractive index databases, along with several values of the particle aspect ratio, denoting the particle shape. The consequences for the infrared extinction values of both the particle shape and the particle orientation are explored: this analysis shows that as the particle asphericity increases, the extinctions increase if the particles are aligned horizontally, and decrease if they are aligned vertically. Randomly oriented spheroidal particles have very similar infrared extinction properties as spherical particles, whereas the horizontally and vertically aligned particles can be considered to be the upper and lower bounds on the extinction values. Inputting these values into COSMO-MUSCAT-RTTOV, it is found that spherical particles do not appear to be sufficient to describe fully the resultant colour of the dust in the infrared imagery. Comparisons of SEVIRI and simulation colours indicate that of the dust types tested, the dust refractive index dataset produced by Volz (1973) shows the most similarity in the colour response to dust in the SEVIRI imagery, although the simulations have a smaller range of colour than do the observations. It is also found that the thermal imagery is most sensitive to intermediately sized particles (radii between 0.9 and 2.6 µm): larger particles are present in too small a concentration in the simulations, as well as with insufficient contrast in extinction between wavelength channels, to have much ability to perturb the resultant colour in the SEVIRI dust imagery.

Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations.Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region.Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique.Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)

Abstract. Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery

In this work a technique for cloud detection and classification from MSG–SEVIRI (Meteosat Second Generation–Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region.

Airborne observations of the impact of a convective system on the planetary boundary layer thermodynamics and aerosol distribution in the inter‐tropical discontinuity region of the West African Monsoon

AbstractThe impact of a convective system downdraught and associated gravity current on the monsoon and harmattan flow structure as well as on aerosol vertical distribution over the Sahel and the Sahara is investigated using dropsondes and an airborne lidar. Complementary ground‐based and satellite observations, as well as European Centre for Medium‐range Weather Forecasts analyses are also used. The mission was conducted on 5 June 2006, in the framework of the African Monsoon Multidisciplinary Analysis Special Observing Period. The targeted area was the Saharan heat low region, and Mali in particular, over which the inter‐tropical discontinuity was strongly perturbed by the convective system. To the north, away from the influence of the gravity current, the atmosphere exhibited a two‐layer structure, with a well mixed 3 km deep internal boundary layer capped by a stable layer 2–2.5 km deep layer referred to as the Saharan aerosol layer (SAL). The aerosol loading in the internal boundary layer (as determined by lidar) was observed to be much less than in the SAL above. Plumes of dust generated by strong low‐level winds west of the Hoggar as observed in Spinning Enhanced Visible and Infra‐Red Imager (SEVIRI) images, and possibly injected in the upper SAL through orographic lifting, contributed to the dust loading of the elevated SAL over much of the domain observed by the lidar. In the northernmost part of the domain investigated, the depth of the SAL top was decreased as the result of subsidence enhanced by the presence of an elevated jet associated with the monsoon trough and/or related to the return circulation of plain‐mountain wind system involving the Atlas range. Associated with the gravity current passing over dust sources, a large aerosol plume was observed by means of lidar measurements and SEVIRI imagery. The plume was seen to reach heights of approximately 3 km above ground level a few tens of kilometres behind the leading edge. Lidar measurements suggest that a fraction of the dust lifted by the gravity current is mixed into the SAL, and in some instances injected above the SAL. Copyright © 2007 Royal Meteorological Society

Saharan dust in nighttime thermal imagery: Detection and reduction of related biases in retrieved sea surface temperature

Guided by radiative transfer modelling of the effects of Saharan dust (aerosol) on brightness temperatures at 3.9, 8.7, 11 and 12 μm measured by the Spinning Enhanced Visible and Infrared Imager (SEVIRI), we propose an indicator of the presence of Saharan dust in nighttime (infrared-only) SEVIRI imagery. Radiative transfer modelling is performed using a fast atmospheric transmittance model and a delta-Eddington approximation for scattering. Using aerosol single scattering properties appropriate to simulation of Saharan dust gives qualitatively good simulations of dust-affected brightness temperatures. From these simulations, we show that aerosol-free brightness temperatures are tightly clustered around the principal axis (explaining 99% of variance) in a 3-dimensional brightness-temperature difference space (the axes being 3.9 μm minus 8.7 μm, 3.9 μm minus 12 μm, and 11 μm minus 12 μm); in contrast, brightness temperatures affected by aerosol are simulated to be significantly off-axis when transformed into this space. Although the detailed effects of aerosol on brightness temperatures are not quantitatively reproduced by our simulations, this off-axis characteristic is empirically found to be also true of Saharan-dust-affected brightness temperatures in observations. The second principal component of the brightness-temperature difference space is identified as a useful index for Saharan dust. Comparisons with independent satellite measurements of aerosol optical depth (AOD, at 0.55 μm) show that the new Saharan dust index (SDI) is loosely correlated with AOD ( r = 0.34). The correlation is loose because the SDI is also sensitive to the height distribution of aerosol, which affects the aerosol's thermal emission. The SDI is used to develop an empirical correction scheme for SST retrievals affected by Saharan dust. Application of the SDI correction scheme removes an independent error of 0.2 K from SEVIRI SSTs validated against buoys from all latitudes.