scholarly journals Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms

2008 ◽  
Vol 25 (4) ◽  
pp. 501-518 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Thomas J. Kopp ◽  
John M. Jackson
Keyword(s):  
Satellite System ◽  
Classification Algorithms ◽  
Cloud Classification ◽  
Infrared Imager ◽  
Other Information ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
New Procedures

Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

scholarly journals Differentiating between Clouds and Heavy Aerosols in Sun-Glint Regions

2010 ◽  
Vol 27 (6) ◽  
pp. 1085-1094 ◽  
Author(s):  
Keith D. Hutchison ◽  
Bruce Hauss ◽  
Barbara D. Iisager ◽  
Hiroshi Agravante ◽  
Robert Mahoney ◽  
...  
Keyword(s):  
Satellite System ◽  
Sensor Data ◽  
Infrared Imager ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Data Dropouts ◽  
New Procedures

Abstract An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sun-glint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

A Geometry-Based Approach to Identifying Cloud Shadows in the VIIRS Cloud Mask Algorithm for NPOESS

2009 ◽  
Vol 26 (7) ◽  
pp. 1388-1397 ◽  
Author(s):  
Keith D. Hutchison ◽  
Robert L. Mahoney ◽  
Eric F. Vermote ◽  
Thomas J. Kopp ◽  
John M. Jackson ◽  
...  
Keyword(s):  
Satellite System ◽  
Spectral Signatures ◽  
Infrared Imager ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
New Procedures ◽  
Global Datasets ◽  
Operational Time

Abstract A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the implementation of the MOD09 procedures to meet NPOESS latency requirements in the VCM algorithm. In this paper, the geometry-based approach used to predict cloud shadows is presented, differences are highlighted between the heritage MOD09 algorithm and new VIIRS cloud shadow algorithm, and results are shown for both these algorithms plus cloud shadows generated by the spectral-based approach. The comparisons show that the geometry-based procedures produce cloud shadows far superior to those predicted with the spectral procedures. In addition, the new VCM procedures predict cloud shadows that agree well with those found in the MOD09 product while significantly reducing the execution time as required to meet the operational time constraints of the NPOESS system.

scholarly journals NASA MODIS Previews NPOESS VIIRS Capabilities

2006 ◽  
Vol 21 (4) ◽  
pp. 649-655 ◽  
Author(s):  
Thomas F. Lee ◽  
Steven D. Miller ◽  
Carl Schueler ◽  
Shawn Miller
Keyword(s):  
Satellite System ◽  
Infrared Imager ◽  
Defense Meteorological Satellite Program ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Using Data ◽  
Color Simulation ◽  
True Color ◽  
Real Time Simulations

Abstract The Visible/Infrared Imager Radiometer Suite (VIIRS), scheduled to fly on the satellites of the National Polar-orbiting Operational Environmental Satellite System, will combine the missions of the Advanced Very High Resolution Radiometer (AVHRR), which flies on current National Oceanic and Atmospheric Administration satellites, and the Operational Linescan System aboard the Defense Meteorological Satellite Program satellites. VIIRS will offer a number of improvements to weather forecasters. First, because of a sophisticated downlink and relay system, VIIRS latencies will be 30 min or less around the globe, improving the timeliness and therefore the operational usefulness of the images. Second, with 22 channels, VIIRS will offer many more products than its predecessors. As an example, a true-color simulation is shown using data from the Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS), an application current geostationary imagers cannot produce because of a missing “green” wavelength channel. Third, VIIRS images will have improved quality. Through a unique pixel aggregation strategy, VIIRS pixels will not expand rapidly toward the edge of a scan like those of MODIS or AVHRR. Data will retain nearly the same resolution at the edge of the swath as at nadir. Graphs and image simulations depict the improvement in output image quality. Last, the NexSat Web site, which provides near-real-time simulations of VIIRS products, is introduced.

scholarly journals Integrated Satellite System for Fire Detection and Prioritization

2022 ◽  
Vol 14 (2) ◽  
pp. 335
Author(s):  
Giuseppe Mazzeo ◽  
Fortunato De Santis ◽  
Alfredo Falconieri ◽  
Carolina Filizzola ◽  
Teodosio Lacava ◽  
...  
Keyword(s):  
Fire Detection ◽  
Satellite System ◽  
Affected Area ◽  
Satellite Systems ◽  
Usable Information ◽  
Infrared Imager ◽  
Synthetic Indicator ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

2016 ◽  
Vol 55 (11) ◽  
pp. 2529-2546 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou
Keyword(s):  
Near Infrared ◽  
Extended System ◽  
Clear Sky ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Very High ◽  
Lower False Alarm Rate ◽  
Infrared Channel

AbstractAssimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

scholarly journals Snow Albedo Seasonality and Trend from MODIS Sensor and Ground Data at Johnsons Glacier, Livingston Island, Maritime Antarctica

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3569
Author(s):  
Calleja ◽  
Corbea-Pérez ◽  
Fernández ◽  
Recondo ◽  
Peón ◽  
...  
Keyword(s):  
Moving Average ◽  
Data Sets ◽  
Snow Albedo ◽  
Livingston Island ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions ◽  
Cloud Mask

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day−1 (in-situ/MOD10A1) for the 2006–2007 season and a minimum of 0.016/0.016 day−1 for the 2009–2010 season. The duration of the decay varies from 85 days (2007–2008) to 167 days (2013–2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006–2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

Comparison of NOAA's Operational AVHRR-Derived Cloud Amount to Other Satellite-Derived Cloud Climatologies

2004 ◽  
Vol 17 (24) ◽  
pp. 4805-4822 ◽  
Author(s):  
Sarah M. Thomas ◽  
Andrew K. Heidinger ◽  
Michael J. Pavolonis
Keyword(s):  
High Resolution ◽  
Phase 1 ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Noaa Avhrr ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual ◽  
Cloud Mask

Abstract A comparison is made between a new operational NOAA Advanced Very High Resolution Radiometer (AVHRR) global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the Clouds from AVHRR phase 1 (CLAVR-1) cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high-latitude performance and the inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1-derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVR-x cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than the International Satellite Cloud Climatology Project (ISCCP) and the University of Wisconsin High Resolution Infrared Radiation Sounder (UW HIRS). Comparisons to the Moderate Resolution Imaging Spectroradiometer (MODIS) for 1–8 April 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July–January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel-level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR-x cloud detection results have been operational since late 2003 and are available in real time from NOAA.

scholarly journals Radiometric Cross-Calibration of Tiangong-2 MWI Visible/NIR Channels over Aquatic Environments using MODIS

2018 ◽  
Vol 10 (11) ◽  
pp. 1803 ◽  
Author(s):  
Qu Zhou ◽  
Liqiao Tian ◽  
Jian Li ◽  
Qingjun Song ◽  
Wenkai Li
Keyword(s):  
Ocean Color ◽  
Aquatic Environments ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Analysis System ◽  
Cross Calibration ◽  
Data Analysis System ◽  
Mean Square Errors ◽  
Calibration Coefficients

The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality ocean color products for MWI observations. We proposed a simple and effective cross-calibration scheme for MWI data using well calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over aquatic environments. The path radiance of the MWI was estimated using the quasi-synchronized MODIS images as well as the MODIS Rayleigh and aerosol look up tables (LUTs) from SeaWiFS Data Analysis System 7.4 (SeaDAS 7.4). The results showed that the coefficients of determination (R2) of the calibration coefficients were larger than 0.97, with sufficient matched areas to perform cross-calibration for MWI. Compared with the simulated Top of Atmosphere (TOA) radiance using synchronized MODIS images, all errors calculated with the calibration coefficients retrieved in this paper were less than 5.2%, and lower than the lab calibrated coefficients. The Rayleigh-corrected reflectance (ρrc), remote sensing reflectance (Rrs) and total suspended matter (TSM) products of MWI, MODIS and the Geostationary Ocean Color Imager (GOCI) images for Taihu Lake in China were compared. The distribution of ρrc of MWI, MODIS and GOCI agreed well, except for band 667 nm of MODIS, which might have been saturated in relatively turbid waters. Besides, the Rrs used to retrieve TSM among MWI, MODIS and GOCI was also consistent. The root mean square errors (RMSE), mean biases (MB) and mean ratios (MR) between MWI Rrs and MODIS Rrs (or GOCI Rrs) were less than 0.20 sr−1, 5.52% and within 1 ± 0.023, respectively. In addition, the derived TSM from MWI and GOCI also agreed with a R2 of 0.90, MB of 13.75%, MR of 0.97 and RMSE of 9.43 mg/L. Cross-calibration coefficients retrieved in this paper will contribute to quantitative applications of MWI. This method can be extended easily to other similar ocean color satellite missions.

scholarly journals Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1387-1412
Author(s):  
Jonas Witthuhn ◽  
Anja Hünerbein ◽  
Hartwig Deneke
Keyword(s):  
Comprehensive Evaluation ◽  
Evaluation Studies ◽  
Earth Observing System ◽  
Infrared Imager ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Data Points ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Reference Measurements ◽  
Aerosol Type

Abstract. Reliable reference measurements over the ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Within the framework of the Maritime Aerosol Network, shipborne reference datasets have been collected over the Atlantic Ocean since 2004 with Microtops Sun photometers. These were recently complemented by measurements with the multi-spectral GUVis-3511 shadowband radiometer during five cruises with the research vessel Polarstern. The aerosol optical depth (AOD) uncertainty estimate of both shipborne instruments of ±0.02 can be confirmed if the GUVis instrument is cross calibrated to the Microtops instrument to account for differences in calibration, and if an empirical correction to account for the broad shadowband as well as the effects of forward scattering is introduced. Based on these two datasets, a comprehensive evaluation of aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on NASA's Earth Observing System satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat satellite, and the Copernicus Atmosphere Monitoring Service reanalysis (CAMS RA) is presented. For this purpose, focus is given to the accuracy of the AOD at 630 nm in combination with the Ångström exponent (AE), discussed in the context of the ambient aerosol type. In general, the evaluation of MODIS AOD from the official level-2 aerosol products of C6.1 against the Microtops AOD product confirms that 76 % of data points fall into the expected error limits given by previous validation studies. The SEVIRI-based AOD product exhibits a 25 % larger scatter than the MODIS AOD products at the instrument's native spectral channels. Further, the comparison of CAMS RA and MODIS AOD versus the shipborne reference shows similar performance for both datasets, with some differences arising from the assimilation and model assumptions. When considering aerosol conditions, an overestimation of AE is found for scenes dominated by desert dust for MODIS and SEVIRI products versus the shipborne reference dataset. As the composition of the mixture of aerosol in satellite products is constrained by model assumptions, this highlights the importance of considering the aerosol type in evaluation studies for identifying problematic aspects.

scholarly journals Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

2020 ◽  
Vol 12 (24) ◽  
pp. 4096 ◽  
Author(s):  
Kerry Meyer ◽  
Steven Platnick ◽  
Robert Holz ◽  
Steve Dutcher ◽  
Greg Quinn ◽  
...  
Keyword(s):  
Optical Properties ◽  
Infrared Imaging ◽  
Trend Detection ◽  
Adjustment Factors ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Water Cloud ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation ◽  
Cloud Mask

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

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