A CrIS Cloud Detection Method Based on CrIS and ATMS Measurements

2020 ◽  
Author(s):  
Li Guan ◽  
Qiumeng Xue
Keyword(s):  
Detection Method ◽  
Principal Component ◽  
Field Of View ◽  
Advanced Technology ◽  
Inversion Method ◽  
Cloud Detection ◽  
High Quality ◽  
Thermal Contrast ◽  
Cross Track ◽  
Cloud Mask

<p>The Suomi National Polar-orbiting Partnership (SNPP) satellite carrying the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments can provide high quality hyperspectral infrared (IR) data and microwave (MW) measurements. It is very important to ensure the accuracy of cloud detection in the infrared hyperspectral measurements before they are used for geophysical retrievals or data assimilation. Therefore, a cloud detection method using the CrIS hyperspectral radiances at longwave (709.5-746.0 cm<sup>-1</sup>) and shortwave (2190-2250 cm<sup>-1</sup>) bands and the ATMS measurements is introduced in this paper. Four steps are included in this algorithm: identifying clear FOV, estimating the number of cloud formations, thermal contrast, and cloud mask classification. Specifically, each CrIS field-of-view (FOV) is preliminarily assigned as clear or cloudy by comparing the measured IR radiances and simulated IR clear radiances which are generated from the MW-retrieved geophysical state vector based on a physical inversion method. Secondly, the number of cloud formations within one CrIS field-of-regard (FOR) is estimated using the principal component analysis (PCA). Next, CrIS radiances at longwave channels and shortwave bands are used to evaluate the thermal contrast within the FOR. Based on the above informations each FOR will finally be assigned a cloud mask classification. The cloud mask results derived from this technique are also analyzed.</p>

scholarly journals Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

2020 ◽  
Vol 13 (12) ◽  
pp. 6459-6472
Author(s):  
Larysa Istomina ◽  
Henrik Marks ◽  
Marcus Huntemann ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Cloud Detection ◽  
High Quality ◽  
Melt Pond ◽  
Resolution Imaging ◽  
Cloud Mask ◽  
Cloud Screening

Abstract. The historic MERIS (Medium Resolution Imaging Spectrometer) sensor on board Envisat (Environmental Satellite, operation 2002–2012) provides valuable remote sensing data for the retrievals of summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI (Ocean and Land Colour Instrument) on board Sentinel 3A and 3B (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the resulting product. The presence of 15 visible and near-infrared spectral channels of MERIS allows high-quality retrievals of sea ice albedo and melt pond fraction, but it makes cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud Screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high-quality screening throughout the whole swath. A total of 3 years of reference cloud mask from AATSR (Advanced Along-Track Scanning Radiometer) (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS (Moderate Resolution Imaging Spectroradiometer) cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behavior is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF–albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artifacts, which were clearly visible in the previous version of the dataset. The developed cloud screening routine can be applied to address cloud contamination in remote sensing data over sea ice. The resulting cloud mask for the MERIS operating time, as well as the improved MPF–albedo datasets for the Arctic region, is available at https://www.seaice.uni-bremen.de/start/ (Istomina et al., 2017).

Cloud Detection with MODIS. Part II: Validation

2008 ◽  
Vol 25 (7) ◽  
pp. 1073-1086 ◽  
Author(s):  
S. A. Ackerman ◽  
R. E. Holz ◽  
R. Frey ◽  
E. W. Eloranta ◽  
B. C. Maddux ◽  
...  
Keyword(s):  
Optical Depth ◽  
Great Plains ◽  
Cloud Amount ◽  
Department Of Energy ◽  
Field Of View ◽  
High Spectral Resolution ◽  
Polar Regions ◽  
Cloud Detection ◽  
Laser Altimeter ◽  
Cloud Mask

Abstract An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach. In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

A cloud detection method for GaoFen-6 wide field of view imagery based on the spectrum and variance of superpixels

2021 ◽  
Vol 42 (16) ◽  
pp. 6315-6332
Author(s):  
Zhipeng Dong ◽  
Yanxiong Liu ◽  
Wenxue Xu ◽  
Yikai Feng ◽  
Yilan Chen ◽  
...  
Keyword(s):  
Detection Method ◽  
Field Of View ◽  
Wide Field ◽  
Cloud Detection ◽  
Wide Field Of View

scholarly journals Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

2020 ◽  
Author(s):  
Larysa Istomina ◽  
Henrik Marks ◽  
Marcus Huntemann ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
The Arctic ◽  
Cloud Detection ◽  
High Quality ◽  
Melt Pond ◽  
Cloud Mask ◽  
Cloud Screening ◽  
Arctic Summer ◽  
Melting Season

Abstract. The historic MERIS sensor onboard Envisat (2002–2012) provides valuable remote sensing data for the retrievals of the summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI onboard Sentinel 3 (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the end product. The presence of 15 VIS and NIR spectral channels of MERIS allow high quality retrievals of sea ice albedo and melt pond fraction, but make cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high quality screening throughout the whole swath. Three years of reference cloud mask from AATSR (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behaviour is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF/albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artefacts, which were clearly visible in the previous version of the dataset. The resulting cloud mask for the MERIS operating time, as well as the improved MPF/albedo datasets are available as swath data and daily means on the ftp server of the University of Bremen https://seaice.uni-bremen.de/data/meris/gridded_cldscr/.

Using MSG-SEVIRI Cloud Physical Properties and Weather Radar Observations for the Detection of Cb/TCu Clouds

2011 ◽  
Vol 50 (7) ◽  
pp. 1587-1600 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Maurice J. Schmeits ◽  
Hartwig Deneke ◽  
Rob A. Roebeling
Keyword(s):  
Physical Properties ◽  
Detection Method ◽  
Convective Cloud ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Radar Reflectivity ◽  
Cloud Detection ◽  
Radar Method ◽  
Cloud Mask ◽  
Group Memberships

AbstractA new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

scholarly journals A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1197-1201
Author(s):  
L. L. Jia ◽  
X. Q. Wang
Keyword(s):  
Detection Method ◽  
Texture Feature ◽  
New Method ◽  
Detection Methods ◽  
Cloud Detection ◽  
Segmentation Method ◽  
Optical Images ◽  
Routine Work ◽  
Cloud Mask ◽  
Better Than

Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90 %. The proposed method can meet the needs of routine work.

scholarly journals Study of seismic attributes recognition method of high quality igneous rock reservoir

2021 ◽  
Vol 329 ◽  
pp. 01058
Author(s):  
Wang Yitong ◽  
Li Jingsheng ◽  
Sam Zandong Sun ◽  
Qiao Wei ◽  
Li Yanjie ◽  
...  
Keyword(s):  
Igneous Rock ◽  
Time Window ◽  
Principal Component ◽  
P Wave ◽  
Inversion Method ◽  
Target Area ◽  
Reservoir Prediction ◽  
High Quality ◽  
Frequency Dependent ◽  
Avo Inversion

Aimed target area is deeply buried, complex lithology, dual media, reservoir development degree is controlled by a variety of factors, meanwhile, lateral thickness and lithofacies change rapidly, and strata formation is poor. Therefore, igneous rock reservoir has difficulty in predicting, since seismic is complicated to track trace, reservoir attribute analysis is hard to determine the time window, and inversion modeling requires sophisticated. By analyzing, the basalt in the target research area accounts for the principal component of the igneous rock, however, the igneous rocks with relatively developed reservoirs are mostly distributed in the trachyte breccia which has good productivity. The results of petrophysical study indicate that frequency-dependent AVO inversion method is an important means to identify fluid and reservoir prediction, notwithstanding it is difficult to distinguish high-quality reservoirs barely by P-wave impedance. Consequently, AVOF inversion method is appropriately proposed to identify igneous rock reservoir. Foremost, eliminating the effects of algorithm,frequency, spectrum balancing and other factors, then put the improved three-term Aki&Richards frequency-dependent AVO inversion method applying to distinguish igneous reservoir fluid and lithology, for the purpose of carrying out the identification of high-quality reservoirs.

A High Resolution Scanning Microscope

1968 ◽  
Vol 26 ◽  
pp. 356-357
Author(s):  
A. V. Crewe ◽  
J. Wall ◽  
L. M. Welter
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Spherical Aberration ◽  
Focal Length ◽  
Spot Size ◽  
Emission Source ◽  
Field Of View ◽  
Scanning Microscope ◽  
Magnetic Lens ◽  
High Quality

A scanning microscope using a field emission source has been described elsewhere. This microscope has now been improved by replacing the single magnetic lens with a high quality lens of the type described by Ruska. This lens has a focal length of 1 mm and a spherical aberration coefficient of 0.5 mm. The final spot size, and therefore the microscope resolution, is limited by the aberration of this lens to about 6 Å.The lens has been constructed very carefully, maintaining a tolerance of + 1 μ on all critical surfaces. The gun is prealigned on the lens to form a compact unit. The only mechanical adjustments are those which control the specimen and the tip positions. The microscope can be used in two modes. With the lens off and the gun focused on the specimen, the resolution is 250 Å over an undistorted field of view of 2 mm. With the lens on,the resolution is 20 Å or better over a field of view of 40 microns. The magnification can be accurately varied by attenuating the raster current.

scholarly journals Seasonal cycle of cloud cover analyzed using Meteosat images

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente
Keyword(s):  
Spatial Resolution ◽  
Seasonal Cycle ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Total Cloud Amount ◽  
Cloud Parameters ◽  
Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

Cloud Detection Method Based on Spectral Area Ratios in MODIS Data

2015 ◽  
Vol 41 (6) ◽  
pp. 561-576
Author(s):  
Feng Guo ◽  
Xiaohua Shen ◽  
Lejun Zou ◽  
Yupeng Ren ◽  
Yi Qin ◽  
...  
Keyword(s):  
Detection Method ◽  
Cloud Detection ◽  
Modis Data ◽  
Spectral Area
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