Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day-night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9-16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.     

Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI

This work addressed the retrieval of Land Surface Emissivity (LSE) from combined mid-infrared and thermal infrared data of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the geostationary satellite—Meteosat Second Generation (MSG). To correct for the atmospheric effects in satellite measurements, a new atmospheric correction scheme was developed for both Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels. For the MIR channel, because it is less sensitive to the change of water vapor content, the clear-sky and time-nearest European Centre for Median-range Weather Forecast (ECMWF) atmospheric data were used for the images where no atmospheric data are available. For TIR channels, a modified model of Diurnal Temperature Cycle (DTC) used by Göttsche and Olesen [Göttsche, F. M., and Olesen, F. S. (2001). Modeling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348.] and Schädlich et al. [Schädlich, S., Göttsche, F. M., and Olesen, F. S. (2001). Influence of land surface parameters and atmosphere on METEOSAT brightness Temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75, 39-46.] was adopted. The separation of Land Surface Temperature (LST) and LSE is based on the concept of the Temperature Independent Spectral Indices (TISI) [Becker, F., and Li, Z. L. (1990a). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.] constructed with one channel in MIR and one channel in TIR. The results of two different combinations (combination of channels 4 and 9 and of channels 4 and 10) and two successive days at six specific locations over North Africa show that the retrievals are consistent. The range of emissivity in MSG-SEVIRI channel 4 goes from 0.5 for bare areas to 0.96 for densely vegetated areas, whereas the emissivities in MSG-SEVIRI channels 9 and 10 are usually from 0.9 to 0.95 for bare areas and from 0.95 to 1.0 for vegetated areas. For densely vegetated areas, the emissivities in MSG-SEVIRI channel 9 are larger than the ones in channel 10, whereas the opposite is observed over bare areas. The rms differences between two combinations over the whole studied region are 0.017 for emissivity in channel 4, 0.008 for emissivity in channel 9 and 0.007 for emissivity in channel 10.     

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.     

Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval

A strategy is presented with the aim of achieving an operational accuracy of 2.0 K in land-surface temperature (LST) from METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The proposed method is based on a synergistic usage of the split-window (SW) and the two-temperature method (TTM) and consists in combining the use of a priori land-surface emissivity (LSE) estimates from emissivity maps with LST estimates obtained from SW method with the endeavour of defining narrower and more reliable ranges of admissible solutions before applying TTM. The method was tested for different surface types, according to SEVIRI spatial resolution, and atmospheric conditions occurring within the MSG disc. Performance of the method was best in the case of relatively dry atmospheres (water-vapour content less than 3 g cm^?2), an important feature since in this case SW algorithms provide the worst results because of their sensitivity to uncertainties in surface emissivity. The hybrid method was also applied using real MSG/SEVIRI data and then validated with the Moderate resolution Imaging Spectroradiometer (MODIS)/Terra LST/LSE Monthly Global 0.05° geographic climate modeling grid (CMG) product (MOD11C3) generated by the day/night algorithm. The LST and LSE retrievals from the hybrid-method agree well (bias and root mean square error (RMSE) of??0.2 K and 1.4 K for LST, and around 0.003–0.02 and 0.009–0.02 for LSE) with the MOD11C3 product. These figures are also in conformity with the MOD11C3 performance at a semi-desert where LST (LSE) values is 1–1.7 K (0.017) higher (less) than the ground-based measurements.     

Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends

Abstract

Land surface temperature (LST) and emissivity for large areas can only be derived from surface-leaving radiation measured by satellite sensors. These measurements represent the integrated effect of the surface and are, thus, for many applications, superior to point measurements on the ground, e.g. in Earth's radiation budget and climate change detection. Over the years, a substantial amount of research was dedicated to the estimation of LST and emissivity from passive sensor data. This article provides the theoretical basis and gives an overview of the current status of this research. Sensors operating in the visible, infrared and microwave range onboard various meteorological satellites are considered, e.g. Meteosat-MVIRI, NOAA-AVHRR, ERS-ATSR, Terra-MODIS, Terra-ASTER and DMSP-SSM/I. Atmospheric effects on measured brightness temperatures are described and atmospheric corrections using radiative transfer models (RTM) are explained. The substitution of RTM with neural networks (NN) for faster forward calculations is also discussed. The methods reviewed for LST estimation are the single-channel method, the split-window techniques (SWT), and the multi-angle method, and, for emissivity estimation, the normalized emissivity method (NEM), the thermal infrared spectral indices (TISI) method, the spectral ratio method, alpha residuals, normalized difference vegetation index (NDVI )-based methods, classification-based emissivity and the temperature emissivity separation (TES) algorithm.     

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.     

Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Temperature and emissivity separation from ASTER data for low spectral contrast surfaces

The performance of Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) thermal infrared (TIR) data product algorithms was evaluated for low spectral contrast surfaces (such as vegetation and water) in a test site close to Valencia, Spain. Concurrent ground measurements of surface temperature, emissivity, and atmospheric radiosonde profiles were collected at the test site, which is a thermally homogeneous area of rice crops with nearly full vegetation cover in summer. Using the ground data and the local radiosonde profiles, at-sensor radiances were simulated for the ASTER TIR channels and compared with L1B data (calibrated at-sensor radiances) showing discrepancies up to 3% in radiance for channel 10 at 8.3 μm (equivalently, 2.5 °C in temperature or 7% in emissivity), whereas channel 13 (10.7 μm) yielded a closer agreement (maximum difference of 0.5% in radiance or 0.4 °C in temperature). We also tested the ASTER standard products of land surface temperature (LST) and spectral emissivity generated with the Temperature-Emissivity Separation (TES) algorithm with standard atmospheric correction from both global data assimilation system profiles and climatology profiles. These products showed anomalous emissivity spectra with lower emissivity values and larger spectral contrast (or maximum-minimum emissivity difference, MMD) than expected, and as a result, overestimated LSTs. In this work, a scene-based procedure is proposed to obtain more accurate MMD estimates for low spectral contrast materials (vegetation and water) and therefore a better retrieval of LST and emissivity with the TES algorithm. The method uses various gray-bodies or near gray-bodies with known emissivities and assumes that the calibration and atmospheric correction performed with local radiosonde data are accurate for channel 13. Taking the channel 13 temperature (atmospherically and emissivity corrected) as the true LST, the radiances for the other channels were simulated and used to derive linear relationships between ASTER digital numbers and at-ground radiances for each channel. The TES algorithm was applied to the adjusted radiances and the resulting products showed a closer agreement with the ground measurements (differences lower than 1% in channel 13 emissivities and within ± 0.3 °C in temperature for rice and sea pixels).     

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.     

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.     

TM热红外波段等效比辐射率估算

地表比辐射率是热红外遥感获取地表温度必不可少的参数。目前,实验室或野外实际测量的都是8~14um热红外波段范围内的地表比辐射率,这与Landsat 5 TM热红外波段10.4~12.5um范围内的地表比辐射率还存在着一定的差异。本文将着重探讨TM热红外范围内地表比辐射率的估算方法,然后根据估算出的地表比辐射率,利用覃志豪等提出的单窗算法[1~2],对北京城八区进行地表温度反演。结果表明,该方法能获得较为合理的地表温度反演结果。     

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.     

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT

Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R²) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.     

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.     

The extension of the split window technique to passive microwave surface temperature assessment

The theoretical study of both land and sea surface temperature remote sensing is treated through investigating the extension to the microwave region (1-100GHz) of the split window technique, usually used in the thermal infrared region for sea surface temperature measurements. The study of land surface temperature shows that, in both regions (infrared and microwave), the influence (of atmospheric water vapor content and surface emissivity) is critical. The theory is based on the Radiative Transfer Equation, which assumed solutions can be given in both spectral regions, with respect to Wien's and Rayleigh-Jean's laws, respectively. The surface temperature determination is studied in connection with the surface emissivity in both infrared and microwave regions determined with an iterative process. Infrared data is provided by the sensor Advanced Very High Resolution Radiometer (AVHRR) and microwave data by Special Sensor Microwave/Imager (SSM/I), through the WETNET program, directed by NASA/HQS.     

A dynamic cloud masking and filtering algorithm for MSG retrieval of land surface temperature

A dynamic cloud masking and filtering algorithm is proposed for the Land Surface Temperature (LST) retrieval from infrared imagery of geostationary satellites. The algorithm uses a modified Kalman Filter (KF) to separate the non‐Gaussian error due to clouds from the reference cloud‐free LST retrieval error, in order to discriminate and possibly correct for different levels of cloud contamination. This approach was intended to make better use of the important features of the new generation of geostationary satellites, such as the Meteosat Second Generation (MSG) satellites, including their high sampling frequency and the extensive real‐time availability of images.

The reference surface energy balance model on which the KF is based was simplified to the extent that no information other than top‐of‐atmosphere solar radiation was required to force the system together with LST measurements. The overall accuracy of the new algorithm, named Cloud Masking with Kalman Filter (CMKF), was tested on the LST retrievals over the Italian peninsula for a 15‐day summer period from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the MSG satellite. As a verification dataset, analogous retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used. The higher spatial resolution of the MODIS LST maps and accompanying cloud masks also allowed us to analyse the results in terms of different levels of fractional cloud cover.

The results of these first verification experiments show that the application of the proposed dynamic algorithm improves the LST retrieval, with respect to cloud masking with a classical static algorithm, in two different ways: first, there is a more consistent identification of cloud‐free LST data; and second, and more importantly, there is a substantial increase in the quantity of final LST estimates, up to four times more in very cloudy conditions, with the use of prior model predictions at a cost of a very modest increase in the LST root mean squared error (RMSE). Moreover, the higher coefficient of determination in both cases indicates that the algorithm provides LST estimates over a wider range, as it is capable of reconstructing with some accuracy certain lower LST values under cloud cover.     

Improvement of cloud detection near sunrise and sunset by temporal-differencing and region-growing techniques with real-time SEVIRI

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), through the Satellite Application Facility for Nowcasting (SAFNWC), provides a software package suitable to generate real-time products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite. A cloud mask is one of its key derived products. An analysis of the behaviour of version v2008 of this software using animation loops indicates a discontinuity in the detection of low clouds near the day–night transition. Thus, its use may have a negative impact when the scene includes such conditions. Our study provides solutions for a smoother continuity. It shows that a temporal-differencing technique combined with a region-growing technique can improve low-level cloudiness detection in the day–night transition area at the expense of a small increase of false alarms. The method and the resulting improvement are described and illustrated. A comparison of statistics of the cloudiness over Europe reported in Surface Synoptic Observations (SYNOP) and retrieved from the SEVIRI with and without the enhancement estimates a decrease of 50% of the frequency of missed clouds while the false alarm ratio increases only marginally. A comparison with another EUMETSAT operational SEVIRI-based cloud mask confirms the usefulness of the proposed technique at oblique sun angles.     

An automated day-time cloud detection technique applied to MSG-SEVIRI data over Western Europe

Since February 2003, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the first Meteosat Second Generation (MSG) satellite has provided radiance data in 12 spectral bands for a full Earth hemisphere every 15 minutes. This high frame rate renders it an excellent tool for studies of atmospheric transport of pollutants, aerosol and clouds. TNO (Netherlands Organisation for Applied Scientific Research) is currently developing an algorithm for the retrieval of aerosol properties from MSG-SEVIRI observations over cloud-free scenes. This requires rigorous cloud screening for which a fast and stand-alone algorithm is developed. The detection technique described in this paper, which is based on the ATSR-2 (Along Track Scanning Radiometer 2) cloud screening algorithm, can be easily implemented, and satisfactorily identifies clouds. The study presented here focuses on Western Europe for the year 2006. Cloud detection results are compared to the KNMI/MF (Royal Netherlands Meteorological Institute/Meteo-France) and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection algorithms. According to the statistics, the results obtained with our algorithm show good agreement (>80%) with these data sets.