Fusion of SeaWiFS and TOMS Satellite Data with Surface Observations and Topographic Data during Extreme Aerosol Events

Falke, Husar, and Schichtel

Volume 51 November 2001 Journal of the Air & Waste Management Association 1579

ISSN 1047-3289 J. Air & Waste Manage. Assoc. 51:1579-1585

Copyright 2001 Air & Waste Management Association

TECHNICAL PAPER

ABSTRACTSpaceborne sensors allow near-continuous aerosol moni-toring throughout the world. This paper illustrates the fu-sion of Sea-Viewing Wide Field-of-View Sensor (SeaWiFS)and TOMS satellite data with surface observations and to-pographic data during four extreme aerosol events: (1) theApril 1998 Asian dust storm that impacted the west coastof North America, (2) the May 1998 Central American for-est fire smoke that impacted eastern North America, (3)the intense fall 1999 northern California fires, and (4) themassive February 2000 Sahara dust storm. During these dustand smoke events, the aerosol was visualized on true colorSeaWiFS images as a distinct yellowish dye, the result ofthe aerosol increasing the reflectance of darker surfaces(ocean and land) and decreasing the reflectance of clouds.TOMS imagery also indicated increased aerosol absorptionin the affected areas, while surface monitors measuredmajor reductions in visual range. Fusing these data aids inthe determination of the aerosol’s spatial, temporal, andoptical properties and provides supporting evidence forcharacterizing what is being visualized as dust or smoke. A3-dimensional perspective of the events is obtained whenincorporating topographic data and provides insight intothe vertical properties of the aerosol plumes.

INTRODUCTIONExtreme biogeochemical events, such as forest fires anddust storms, tend to produce large quantities of dust,smoke, or haze dispersed over regional or global scales.The dense aerosol plumes are easily observable and visu-alized through satellite images and surface observations,allowing monitoring of their transport and transforma-tion. Satellite images offer greater spatial coverage thando surface observations and are of particular benefit inareas with limited numbers of observations. High spatialresolution satellite images displayed in true color allowthe detection of atmospheric particles. When fused withsurface data, they can better describe the aerosol and pro-vide a greater understanding of the spatial, temporal, andchemical characteristics of the aerosol than can any singleimage or surface observation. This paper presents the fu-sion of data obtained during extreme dust and smokeevents over Asia, North America, and Africa. Satellite im-agery was obtained from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the TOMS sensor and wascombined with surface observations from visibility net-works. Topographic information from a digital elevationmodel (DEM) was included in the data fusion to achievea 3-dimensional view of the aerosol events.

DATA FUSIONIntegrating or merging available data from multiple sen-sors generates knowledge of aerosol characteristics andtransport that is not readily discernible from a single sen-sor. The integration of data from multiple sources is re-ferred to as data fusion. Data fusion is a relatively newfield in its application to environmental data analysis.However, the field has a rich history in physiology andneural sciences, as well as in their artificial counterpart inrobotics, where automated mechanisms combine infor-mation from multiple knowledge sources to improve theunderstanding of a given scene.1

Different levels of data fusion have been classified aspixel fusion, feature fusion, and object identification.2-4

Pixel-level fusion merges measured data, such as satellite

Fusion of SeaWiFS and TOMS Satellite Data with SurfaceObservations and Topographic Data during ExtremeAerosol Events

Stefan R. Falke, Rudolf B. Husar, and Bret A. SchichtelCenter for Air Pollution Impact and Trend Analysis, Washington University, St. Louis, Missouri

IMPLICATIONSThe fusion of satellite imagery and surface-based dataprovides policy-makers with valuable contextual informa-tion of the spatial, temporal, and chemical characteris-tics of aerosol events and can be used in planning, regu-latory, and public health protection activities. Fused dataare effective in monitoring and controlling events such asprescribed forest burns. Images derived from integrateddata are important to early detection and warning sys-tems. The origins of aerosol events are often located out-side the jurisdiction of state or federal air pollution con-trol agencies. In such cases, data fusion can help deter-mine areas that are entitled to waivers for National Ambi-ent Air Quality Standards violations.


1580 Journal of the Air & Waste Management Association Volume 51 November 2001

sensor radiance values, instrument calibration, geomet-ric correction, or atmospheric information. Different sat-ellite sensors can identify different properties, or features,of aerosols, and when fused can identify the aerosol withmore certainty. The resulting images are analyzed for fea-ture extraction, such as the spatial extent of the aerosolplume or its size distribution. These features from mul-tiple data sources are then combined to formulate thecharacteristics of the aerosol being measured (Figure 1).Feature-level fusion is needed when different sensors pro-vide complementary data, one sensor augmenting theother. In such cases, quantitative “correlation” betweenthe sensors is often meaningless because the measures arenot compatible. The features extracted in this paper’s fu-sion examples primarily define the aerosol’s spatial char-acteristics in x, y, and z dimensions.

DATA SOURCES AND PROCESSINGThe data used in the fusion efforts presented in this workconsist of satellite images, surface observations, and DEMs.Each data source was subject to preprocessing as outlinedbelow before being judged suitable for inclusion in thefusion process.

SeaWiFSLaunched in 1997, SeaWiFS was designed as an oceancolor sensor to measure sea surface color and other oceanbio-optical properties. However, its daily visible colorimages can provide revealing images of non-oceanevents, such as dust storms and smoke plumes.5,6 Spec-tral reflectance data from the SeaWiFS sensor provide adetailed spatial pattern of a swath of earth (~2800 kmwide) at local noon each day. The raw (Level 1A) LocalArea Coverage (LAC), 1-km resolution SeaWiFS data were

downloaded from the SeaWiFS Program6 and processedat Washington University in St. Louis, MO.

The first stage of processing removed the scatter-ing by air molecules from the total reflectance using aprocedure by Vermote and Tanré7 that also includednominal corrections for ozone and water vapor absorp-tion. Next, the pixel radiance values were transformedto reflectance. The calculated spectral reflectance val-ues (fraction of radiation reflected) represented the com-bined reflectance from the land, clouds, and ambientaerosol. A true color SeaWiFS image was then gener-ated and georeferenced with ENVI software8 by com-bining the red (0.670 µm), green (0.550 µm), and blue(0.412 µm) wavelengths. In some cases, the region ofinterest spanned multiple SeaWiFS swaths that had tobe merged into a single image. Because SeaWiFS’ truecolor images often clearly displayed vegetation andwater bodies, they served as base layers on which theother data sources were superimposed.

TOMSTOMS has been in use since 1978 on the Nimbus-7 plat-form, scanning at ultraviolet wavelengths. TOMS is mostwell known for mapping ozone. However, the sensor issensitive to absorbing aerosols and can be used to moni-tor aerosols. Retrievals of the absorbing aerosol indexfrom the TOMS satellite9 provided useful informationon the daily (local noon) spatial distribution of dust andsmoke, covering a swath of ~3000 km. The TOMS ab-sorbing aerosol signal is a semi-quantitative index of thecolumnar absorption by aerosols at 0.34 µm. The aero-sol index (AI) is dependent on aerosol altitude such thathigher aerosol indices are measured when aerosol existsat higher altitudes.

The daily gridded aerosol absorption index data wereobtained from the NASA TOMS project Web site. For datafusion purposes, the TOMS grid was converted to a contourline plot so that it could be superimposed with the SeaWiFSimage. The contoured TOMS grid was converted to a shapefile using ArcView10 and was then imported to ENVI, wherefusion with SeaWiFS imagery could be accomplished.

DMSP Fire ObservationsThe Defense Meteorological Satellite Program (DMSP)Operational Linescan System uses a light-intensified vis-ible wavelength channel to detect fires at night on a dailybasis. Fires are distinguished from the nighttime city lightsby their transient behavior.11 The DMSP fire locations werebrought into the fusion process by importing them as animage into the ArcView environment and georeferencingthem using ArcView Spatial Analyst. The fire locationscould then be exported as a shape file and imported intoENVI, where they were combined with other data.Figure 1. Three levels of data fusion: pixel, feature, and object.



Surface ObservationsThe strong relationship between aerosol concentrationsand degraded visibility make visual range observations agood indicator of high particulate matter (PM) concen-trations. However, visual range is inversely related to aero-sol concentration, so a more suitable measure for com-paring visibility and aerosol concentrations is the extinc-tion coefficient, bext. The National Weather Surface(NWS) maintains a surface observation database thatcontains visibility observations at more than 200 air-ports across the United States. Significant preprocess-ing was required for using the visibility database. Thelimitations of the visibility data include threshold vis-ibility, maximum visibility reported, lack of suitabledark targets, visual acuity of human observers, andmeteorological influences (rain, fog, snow) on visualrange. A description of the methodologies used in vis-ibility data preprocessing, including precipitation fil-tering and relative humidity corrections, is outlined inHusar et al.12 The calculated extinction coefficients atthe airports were spatially interpolated to a grid andconverted to contour line plots. The contoured fileswere incorporated into the fusion process following thesame method used for the TOMS data.

Another source of surface visibility observations isa database of synoptic surface weather reports main-tained by the Naval Research Laboratory (NRL).13 Inaddition to extracting observations of poor visibilityfrom the NRL database, surface wind speed measure-ments were also used as supporting data for the analy-sis of the Asian dust storm. The point surface weatherdata were spatially interpolated and displayed as con-tours. The contoured files were converted to shape fileformat for data fusion.

DEMGTOPO30 is a global DEM with a spatial resolution of~30 arc seconds (~1 km). The data were downloaded fromthe U.S. Geologic Survey EROS data center Web site.14 Theelevation data were available in regional tiles that had tobe merged for this work.

The topographic data were the only data not super-imposed on SeaWiFS images during data fusion describedin this paper. Instead, the elevation data were used to en-hance the visualization of the SeaWiFS imagery. The el-evation data were rendered in three dimensions usingENVI, and the SeaWiFS images were “wrapped” onto the3-dimensional surface.

AEROSOL EVENT CASE STUDIESThe application of data fusion to the analysis of atmo-spheric phenomena is demonstrated through the exami-nation of extreme aerosol events, including (1) the April

1998 Asian dust storm that impacted the west coast ofNorth America, (2) the May 1998 Central American for-est fire smoke that impacted eastern North America, (3)the intense fall 1999 northern California fires, and (4)the massive February 2000 Sahara dust storm.

1998 Asian Dust StormDuring April 1998, large dust storms occurred over eastAsia. Dust clouds from two separate storms on April 15and April 19 are recognized in SeaWiFS images by theirbright yellow color, partial transparency, and smooth spa-tial texture (Figures 2 and 3). The yellow coloration in theSeaWiFS images is apparently due to the thick atmosphericdust increasing the soil reflectance more in the red wave-lengths (from 0.25 to 0.55 µm) than in the blue wave-lengths (from 0.05 to 0.3 µm). Consequently, the dustappears brighter and more yellow then the underlying

Figure 2. Integrated image of dust over the Gobi desert on (a) April15 and (b) April 19, 1998. The underlying color image is the surfacereflectance derived from SeaWiFS. The TOMS absorbing aerosol index(level 2.0) is superimposed as green contours. The image for April 19contains two additional data sets from the NRL surface observationdatabase: the red contours represent the surface wind speed, and theblue circles indicate locations where dust was observed. The high windspeeds generated the large dust front seen in the SeaWiFS, TOMS,and surface observation data.



soil. Fusion of the SeaWIFS image with related data pro-vides multiple perspectives to the dust storms and allowsa comparison and analysis of the April 15 and April 19dust events.

On the SeaWiFS image depicting the April 15 cloud,the sources can be identified as yellowish streaks of dustplumes originating from specific patches of land on bothsides of the Mongolia-China border in the Gobi desert.The superposition of the TOMS and SeaWiFS data in Fig-ure 2 indicates that on April 15, the dust patterns fromTOMS and SeaWiFS did not coincide geographically. Thisis an indication that the fresh dust layer was near theground where the TOMS sensor is less sensitive to dust.

The superposition of the TOMS and SeaWiFS data forApril 19 shows that the dust patterns from TOMS andSeaWiFS coincided geographically over the Gobi desert.This implies that the dust layer was at a higher altitudethan that generated from the April 15 storm because, inthis case, the TOMS sensor detected it. However, TOMSindicates the presence of aerosol that was not captured asdust on the true color SeaWiFS image over the easternpart of China and Korea. The TOMS data also indicatethat on April 19 (Figure 2b), remnants of the April 15 dustcloud were present over the Yellow Sea and Korea, but theSeaWiFS data do not indicate a substantial dust presence.

The analysis of the April 19 event is further enhancedby the fusion of surface data. The surface wind speed ob-servations from the NRL in the vicinity of the dust stormindicate that winds were greater than 18 m/sec, as shownin the red wind speed contours on Figure 3. This was wellabove the generally assumed threshold wind speed (5–6m/sec) for dust suspension.15 Surface pressure and windfield data indicate that the dust storm was driven by alow-pressure cold front that swiftly moved eastward.16

The NRL surface observations of low visibility alsocoincide with the area of dense dust. A dense dust front isclearly visible at the leading edge of the dust cloud fromthe SeaWiFS, TOMS, and NRL visibility observations. Thedust cloud would eventually be transported across the Pa-cific and reach the North American continent, where itcontributed to aerosol concentration near the range ofhealth standards (daily PM10 > 150 µg/m3). A detailed analy-sis of the Asian dust storms is provided in Husar et al.16

1998 Central American FiresIn 1998, the annual springtime fires in Central Americawere more intense than usual. From May 7 to May 17,smoke from numerous widespread fires drifted northwardand caused severe perturbation of the atmospheric envi-ronment over parts of eastern North America. After a pe-riod of stagnation in the Gulf of Mexico, a dense smokecloud was transported northward into the central andeastern United States. Advances in satellite imagery and

Figure 3. Surface reflectance derived from the SeaWiFS satellite datafor (a) May 14 and (b) May 16, 1998. The TOMS absorbing aerosolindex (green, levels 1.2 and 3.0) and the visibility-derived extinctioncoefficients (red, levels 0.2 and 0.4 km–1) are superimposed as contours.Figure 3(c) is a 3-dimensional perspective of the smoke on May 14with the fire locations superimposed as red dots.



analysis have made the monitoring of forest fires withremotely sensed imagery common practice.17,18 On theSeaWiFS images for May 14 and May 16, the smoke cloudsare recognized by their yellow coloration near the source,partial transparency, and smooth spatial texture comparedwith the white-gray and highly textured clouds (see Fig-ure 3). The maps presented in Figures 3a and 3b representthe spatial fusion of three complimentary aerosol obser-vations: SeaWiFS, TOMS, and surface-based extinction co-efficient. The true color SeaWiFS image was used as thebase, with the TOMS aerosol absorption index (green lines)and the extinction coefficient derived from surface-basedairport visual range observations (red lines) superimposed.The fusion of the three data sources allows the visual char-acterization of the smoke plume and is a useful techniquefor qualitatively identifying its spatial distribution, sourcelocations, and height.

The smoke plume was positioned over the central por-tion of the United States on May 14. The contours of thesmoke plume derived from the TOMS data correspondclosely to the hazy smoke plume derived from the SeaWiFSdata. The visibility-derived extinction coefficients alsobroadly correspond to the SeaWiFS and TOMS aerosolpattern. However, the highest surface extinction coeffi-cients (bext > 0.4 km–1, which corresponds to a visual rangeof ~5 km) on May 14 appear further north, closer to theleading edge of the plume, while the highest TOMS sig-nal (AI > 3.0) is closer to the source in Central America.The causes of these slight deviations in the two aerosolmeasures are not clear but could stem from a deeper smokelayer near the source.

By May 16, the smoke plume shifted eastward andhad reached Ohio, Pennsylvania, and West Virginia. Thespatial correspondence of columnar and surface-basedaerosol signals indicate that the bulk of the smoke plumewas not separated from the surface but was transportedlargely within the planetary boundary layer. Unfortu-nately, more detailed vertical aerosol profiles are not avail-able for this smoke event. Husar et al.19 provide furtherdescription and analysis of the Central American fires.

It is difficult to discern the influence of the SierraMadre Mountains in Mexico on the dispersion of thesmoke in the 2-dimensional image in Figure 3a. To in-vestigate this, the SeaWiFS image was rendered on a3-dimensional elevation surface derived from GTOPO30DEM data (Figure 3c). The smoke dispersion plumes overCentral America appear over low-elevation terrain, whilehigh-elevation regions remain mostly smoke-free, suggest-ing that the high-elevation terrain acts as a barrier to thedispersion of smoke. The daily fire map derived from theDMSP sensor was not available for May 14, but the May15 fire locations are fused along with the SeaWiFS imageand topographical data in Figure 3c. The hundreds of fire

spots provide a visual context as to the number of fires inthe region, but based on the currently available satellite“fire products,” it is not possible to estimate the magni-tude of the smoke emissions.

The figure is not a true 3-dimensional image be-cause the SeaWiFS image is simply “wrapped” onto the3-dimensional surface. To truly exhibit the characteris-tics of a 3-dimensional map, the smoke in the SeaWiFSimage would need to include a vertical component. A dis-play like Figure 3c is technically rendered in only 2.5 di-mensions because it does not truly depict the verticaldimension, but for the purposes of this paper, we willcontinue to refer to such renderings as 3-dimensional.

1999 California FiresIn September and October 1999, several major forest firesoccurred throughout California. In addition to the directfire damage, the thick smoke plumes required the evacu-ation of inhabitants in some affected downwind areas.The smoke plumes also caused “exceptional events” forsome counties and resulted in the exceedance of the PMstandard. A particularly vivid example of the smoke plumeis displayed through the SeaWiFS image on October 18:the southern two-thirds of the San Joaquin Valley are clear,while the top third is filled with smoke (Figure 4a). Thesmoke is identified as the yellow coloration across north-ern California and is clearly confined to the borders ofthe San Joaquin Valley until it reaches an area just northof San Francisco.

A 3-dimensional rendering highlights the influenceof elevation barriers on the transport of smoke and en-hances the interpretation of the 2-dimensional figure (Fig-ure 4b). The smoke is confined to the low elevations, whilethe mountains are evidently outside the smoke layer. Thesmoke is seen to cover the San Joaquin Valley and is pre-vented from flowing west until it reaches lower terrainjust north of San Francisco, where it escapes from the val-ley and moves out to the Pacific Ocean. Animations of daily3-dimensional images like the one in Figure 4b are usefulfor understanding smoke transport and identifying areasthat exceed PM standards due to exceptional events.

2000 Saharan DustIn late February 2000, a massive Saharan sandstorm trans-ported a dense dust cloud across northwest portions ofAfrica and across the Atlantic Ocean. On February 26,the Sahara dust cloud passed over the Canary Islands,covering them with thick yellow-colored dust (Figure 5a).A 3-dimensional rendering of the Sahara dust event aidsthe visualization and analysis of dust transport, particu-larly the vertical distribution of the dust cloud. The is-lands of Fuerteventura (peak elev. 800 m) and Lanzarote(peak elev. 650 m) appear to be fully immersed within



the dust layer, as their surfaces are completely coveredby the yellow-brown dust, while the higher elevationson the islands of Tenerife (peak elev. 3700 m) and GranCanaria (peak elev. 1950 m) appear to protrude into thedust-free air because they exhibit a dark green vegeta-tion color in the SeaWiFS image. This indicates that theterrain on Gran Canaria and Tenerife acts as a barrier todust transport that causes the dust to flow around theislands. In Figure 5a, the dust is thicker on the upwind

side of the islands than on the downwind side and, infact, the deep blue color of the Atlantic Ocean is visibleon the downwind side because the dust was forced out-ward by the islands. This information allows an initialassessment that the dust layer over the Canary Islands isevidently within the lower stratum of the atmosphere,with an upper bound between 800 and 1950 m.

An analysis of the elevations at the border of thetransition between dust-colored and vegetation-coloredareas on the islands reveals that Gran Canaria is dust-free at ~1200 m, and we can more accurately approxi-mate that the dust layer extends to ~1000 m. Pinker etal.20 provide further description and analysis of the 2000African dust episode. The fusion of SeaWiFS and eleva-tion allows for a crude, yet enlightening, estimation ofthe aerosol layer height over the Canary Islands. How-ever, some aspects of the data fusion method requirequalification. The SeaWiFS image was simply draped onto

Figure 4. SeaWiFS image on October 18, 1999. Figure 4(a) is a2-dimensional view of the San Joaquin Valley with fires on the easternside of the valley and smoke dispersed to the west and heading outover the Pacific just north of San Francisco. Figure 4(b) is a fusion oftopographical data with the SeaWiFS image to illustrate the effect oftopographic barriers on smoke dispersion.

Figure 5. SeaWiFS image of a massive dust cloud emitted from theSahara desert. (a) The dust is transported off the west coast of Africaand across the Canary Islands. (b) A 3-dimensional view of the imageindicates that the Fuerteventura and Lanzarote Islands are fullyblanketed by the murky, yellow-colored dust plume. Gran Canaria andTenerife are partly covered by the dust layer, but their higher elevationsappear to stick out above the dust layer at ~1200 m, as indicated bytheir dark green color.



a 3-dimensional topographic surface so that land, water,dust, and clouds were all projected directly onto the to-pographic surface. A more realistic and revealing render-ing scheme would account for the appropriate heights ofeach atmospheric component. In the 3-dimensional im-ages, the elevations were exaggerated to emphasize thedust layer height dependence. This example only focusedon a limited area and cannot be used for synoptic scaleanalysis. The estimation of the dust layer height was doneusing only a visual analysis of the SeaWiFS and DEM im-ages. A more rigorous approach using pixel spectral analy-sis may provide more accurate height estimates.

SUMMARYThe visualization and analysis of extreme smoke and dustevents over Asia, North America, and Africa were enhancedby the fusion of satellite, surface, and model results. Ben-efits gained by an integrated view of multiple data includethe characterization of the aerosol’s spatial distribution,temporal behavior, optical properties, vertical distribution,emission source locations, and transport. Merging of dataprovides insight into not only when the independent datasources correspond but also where they display discrepan-cies. For instance, when TOMS detects aerosols primarilyin the upper atmosphere, areas where SeaWiFS does notdetect them, the aerosols exist only at higher altitudes.Similarly, when SeaWIFS detects aerosols and TOMS doesnot, the aerosols are isolated near the surface. More recentlydeployed satellite sensors are designed specifically for moni-toring aerosols and have the potential for being applied indata fusion processes to derive other, more detailed char-acteristics of aerosols, such as chemical composition andsize distribution.

ACKNOWLEDGMENTSThis research was funded in part by the U.S. Environmen-tal Protection Agency through CX-825834 (OAR-OAQPS).Mention of trade names or commercial products does notconstitute endorsement or recommendation for use. Theauthors thank the reviewers for their insight and usefulsuggestions in improving the description of fused dataanalysis and assessment.

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About the AuthorsStefan R. Falke (corresponding author) is an AAAS Science& Technology Policy Fellow at the Office of EnvironmentalInformation at the U.S. Environmental Protection Agency(2831R), 1200 Pennsylvania Ave., NW, Washington, DC20460; e-mail: [email protected]. Bret A. Schichtel is aresearch associate for Colorado State University, Coopera-tive Institute for Research in the Atmosphere, Foothills Cam-pus, Fort Collins, CO 80523-1375. Rudolf B. Husar is thedirector for the Center of Air Pollution Impact and TrendsAnalysis, Box 1124, Washington University, One BrookingsDrive, St. Louis, MO 63130-4899.

Documents

Fusion of SeaWiFS and TOMS Satellite Data with Surface Observations and Topographic Data during Extreme Aerosol Events