MSG/SEVIRI cloud mask and type from SAFNWC

Data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first Meteosat Second Generation (MSG) satellite have been available since February 2003. Four MSG satellites are planned to ensure an operational service until at least 2018. A software package, which derives from MSG/SEVIRI imagery a set of 12 products useful for nowcasting purposes, has been developed cooperatively by the Satellite Application Facility for supporting NoWCasting and very short range forecasting (SAFNWC) and is distributed by EUMETSAT.

This paper describes the cloud mask (CMa) and type (CT) algorithms implemented in this SAFNWC/MSG software package. A multispectral thresholding technique has been used: the test sequence depends on illumination conditions and geographical location whereas most thresholds are dynamically computed from ancillary data (atlas, climatology maps, numerical weather prediction (NWP) model forecast fields) using radiative transfer models. These algorithms have been prototyped using GOES‐8 and MODIS imagery before being applied to MSG‐1/SEVIRI. The cloud mask and type can be extracted in any area inside the MSG full disk. Preliminary validation results obtained from a comparison with surface observations using a few months of MSG‐1/SEVIRI data show good performances.     

Remote sensing of water cloud properties from MSG/SEVIRI nighttime imagery

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements from the Meteosat Second Generation (MSG) satellites enable global monitoring of the distribution of clouds during day and night, with a spatial, temporal and spectral resolution that allows for better understanding of the role of clouds in global radiation budget and in climate in general. A method to retrieve cloud properties from nighttime SEVIRI measurements is described in this paper. The method is applicable to single-layer water clouds over sea surfaces and it is based on the inversion of a forward theoretical radiative transfer model, that simulates the radiances reaching the SEVIRI infrared detectors from a specified configuration of the earth-cloud-atmosphere system. This model accounts for scattering and absorption processes in the assumed horizontally homogeneous adiabatic cloud layer. For the inversion of this model, artificial neural networks techniques have been used in this work. The main advantage that these techniques provide is their low computational cost, which makes them suitable for the implementation of operational retrieval procedures. Results obtained by the proposed method are compared with the values provided by the CloudSat derived 2B-TAU product, and those derived from NOAA-AVHRR nighttime imagery, obtaining good agreements.     

Automatic cloud-shadow removal from flood/standing water maps using MSG/SEVIRI imagery

Cloud shadows are a major problem in the detection of flood/standing water using satellite data. Because cloud shadows and flood/standing water have similar spectral characteristics, the traditional means of detection based on spectral properties may fail to distinguish them from each other accurately. Because clouds cast shadows over land, this phenomenon can be analysed using the geometric correlations between clouds and cloud shadows; thus, this method might detect cloud shadows. Based on this concept, geometric relationships were established between clouds and their shadows using satellite data and satellite-solar geometries. Furthermore, an iterative method combining geometric and spectral properties was developed to automatically remove cloud shadows from flood/standing water in satellite maps. This method was applied and tested using MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager) data and continues to show promising and consistent results.     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8–3.9, 6.2–10.8, 8.7–10.8, and 10.8–12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8–3.9 and 8.7–10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2–10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively.     

Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb,Namibia

Land surface temperature (LST) derived from Meteosat Second Generation/?Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.     

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.     

Development and evaluation of SST algorithms for GOES-R ABI using MSG SEVIRI as a proxy

Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incremental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms.     

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.     

Statistical cloud detection from SEVIRI multispectral images

Cloud detection from geostationary satellite multispectral images through statistical methodologies is investigated. Discriminant analysis methods are considered to this purpose, endowed with a nonparametric density estimation and a linear transform into principal and independent components. The whole methodology is applied to the MSG-SEVIRI sensor through a set of test images covering the central and southern part of Europe. “Truth” data for the learning phase of discriminant analysis are taken from the cloud mask product MOD35 in correspondence of passages of MODIS close to the SEVIRI images. Performance of the discriminant analysis methods is estimated over sea/land, daytime/nighttime both when training and test datasets coincide and when they are different. Discriminant analysis shows very good performance in detecting clouds, especially over land. PCA and ICA are effective in improving detection.     

Land surface temperature retrieval from MSG1-SEVIRI data

We have developed a physical-based split-window Land Surface Temperature (LST) algorithm for retrieving the surface temperature from SEVIRI/MSG1 (Spinning Enhanced Visible and Infrared Imager/Meteosat Second Generation1) data in two thermal infrared bands (IR 10.8 and IR 12.0). The proposed algorithm takes into account the SEVIRI angular dependence. The numerical values of the split-window coefficients have been obtained from a statistical regression method, using synthetic data. The look-up tables for atmospheric transmission, path radiance, and downward thermal irradiance are calculated with the MODTRAN3 code. The new LST algorithm has been tested with simulated SEVIRI/MSG1 data over a wide range of atmospheric and surface conditions. Comprehensive sensitivity and error analyses have been undertaken to evaluate the performance of the proposed LST algorithm and its dependence on surface properties, the ranges of atmospheric conditions and surface temperatures, and on the noise-equivalent temperature difference. The results show that the algorithm is capable of producing LST with a standard deviation lower than 1.5 K for viewing zenith angles lower than 50°. Since MSG1 is becoming fully operational in 2004, the proposed algorithm will allow MSG1 users to obtain surface temperatures immediately.     

SST derivation from MSG for PUMA Pilot Projects in Fisheries

This study presents the technical and scientific results obtained through a collaboration between the Institute for Environment and Sustainability (Joint Research Centre of the European Commission) and two PUMA Pilot Projects, namely the Western Indian Ocean Satellite Application Project (WIOSAP), and the Monitoring of the Oceanographic and Meteorological Environment in support of the Management of Fisheries in Senegal (SEGEPS). Both projects are aimed at an optimized use of MSG (Meteosat Second Generation) satellite images to improve the management of regional fisheries and the current scientific knowledge on the natural variability of the fish stocks. A Sea Surface Temperature product from MSG is generated operationally at the native SEVIRI resolution (3 km at the sub‐satellite point) and can be used to locate upwelling regions and thermal fronts for the identification of Potential Fishing Zones (PFZs).     

Extracting Group Transformations from Image Moments

Image distortion induced by the relative motion between an observer and the scene is an important cue for recovering the motion and the structure of the scene. It is known that the distortion in images can be described by transformation groups, such as Euclidean, affine, and projective groups. In this paper, we investigate how the moments of image curves are changed by group transformations, and we derive a relationship between the change in image moments and the invariant vector fields of the transformation groups. The results are used to formalize a method for extracting invariant vector fields of affine transformations from changes in the moments of orientation of curve segments in images. The method is applied to a realtime robot visual navigation task.     

Biometric template extraction from a heartbeat signal captured from fingers

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Generation of realistic saddle trajectories from captured horseback motion

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Aerosol retrieval over ocean from SEVIRI for the use in GERB Earth''s radiation budget analyses

To study the effect of aerosols on the Earth's radiation budget (ERB), the Royal Meteorological Institute of Belgium (RMIB) has integrated spectral aerosol optical depth (AOD) measurements over the ocean from the Spinning Enhanced Visible and Infra-Red Scanner (SEVIRI) into its Geostationary Earth's Radiation Budget, or GERB, processing system referred to as the RGP. Aerosols affect the ERB both directly (when radiation interacts with an aerosol particle) and indirectly (when aerosols act as cloud condensation nuclei). Quantifying the indirect effect is challenging as it requires accurate aerosol retrievals in the close proximity to clouds, where aerosol retrievals may be biased due to leakages from the cloud mask (CM). The initial focus of the RGP project was on the direct effect using confidently clear scenes.

A single channel CM exploiting the SEVIRI temporal sampling was developed at the RMIB for the use in the RGP project. In this study, that single channel mask was evaluated against two multi-channel CMs, one from the Meteorological Products Extraction Facility (MPEF) at the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the other from the Satellite Application Facility for Supporting NoWCasting and Very Short Range Forecasting (SAFNWC), respectively. The NOAA/NESDIS Advanced Very High Resolution Radiometer (AVHRR) single channel aerosol algorithm was adjusted to SEVIRI spectral bands and consistently applied to the pixels identified as cloud-free. The aerosol products corresponding to the three CMs were compared, and the RMIB CM was found to be sufficiently accurate and conservative, for RGP applications.

Comparisons with independent AODs derived from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites show that the RMIB CM-based SEVIRI aerosol product compares well with its MODIS counterpart. However, a small fraction of cloud-contaminated pixels may still remain in the SEVIRI AOD imagery, chiefly within one to two SEVIRI pixels of the cloud boundary, thus limiting its use for indirect forcing studies. Also, the RMIB CM may screen high AOD non-dust aerosol events (e.g., smoke from biomass burning) as cloud. The potential of the new SEVIRI aerosol product is illustrated by generating 9 km-resolution seasonal maps of AODs and ´ÅǺngström Exponents, and by using the GERB radiative flux measurements for a preliminary quick assessment of the direct aerosol forcing.     

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm^?2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil.     

Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data

The accuracy of the Land Surface Temperature (LST) product generated operationally by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) from the data registered by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary METEOSAT Second Generation 2 (MSG2, Meteosat 9) satellite was assessed on two test sites in Eastern Spain: a homogeneous, fully vegetated rice field and a high-plain, homogeneous area of shrubland. The LSA SAF LSTs were compared with ground LST measurements in the conventional temperature-based (T-based) method. We also validated the LSA SAF LST product by using an alternative radiance-based (R-based) method, with ground LSTs calculated from MSG-SEVIRI channel 9 brightness temperatures (at 10.8 μm) through radiative transfer simulations using atmospheric temperature and water vapor profiles together with surface emissivity data. Two lakes were also used for validation with the R-based method. Although the LSA SAF LST algorithm works mostly within the uncertainty expectation of ± 2 K, both validation methods showed significant biases for the LSA SAF LST product, up to 1.5 K in some cases. These biases, with the LSA SAF LST product overestimating reference values, were also observed in previous studies. Nevertheless, the present work points out that the biases are related to the land surface emissivities used in the operational generation of the product. The use of more appropriate emissivity values for the test sites in the LSA SAF LST algorithm led to better results by decreasing the biases by 0.7 K for the shrubland validation site. Furthermore, we proposed and checked an alternative algorithm: a quadratic split-window equation, based on a physical split-window model that has been widely proved for other sensors, with angular-dependent coefficients suitable for the MSG coverage area. The T-based validation results for this algorithm showed LST uncertainties (robust root-mean-squared-errors) from 0.2 K to 0.5 K lower than for the LSA SAF LST algorithm after the emissivity replacement. Nevertheless, the proposed algorithm accuracies were significantly better than those obtained for the current LSA SAF LST product, with an average accuracy difference of 0.6 K.