Tech Briefs

Accurate dust-source characterizations are critical for effectively modeling dust storms and their associated hazards.

Dust storms (5 to 100 km across) often originate from multiple dust-emission sources (1 to 10 km across). Remote-sensing-based dust-source identification is a challenge. A previous study developed a subjective approach for mapping dust sources by using enhanced MODIS satellite imagery; therefore, this study conducted mapping exercises to assess the reproducibility of this technique amongst multiple analysts and in different regions.

Atmospheric soil and mineral dust can significantly influence a variety of global-scale processes, such as the radiation budget of the atmosphere, biogeochemical reactions that occur in the ocean, and global climate. On regional and local scales, lofted dust can negatively affect visibility, mobility, communication, and human health. As a result, understanding the processes that control spatial and temporal patterns of atmospheric dust occurrence has become a priority for the research, military, operational forecasting, and hazard mitigation communities.

Accurate dust-source characterizations are critical for effectively modeling dust storms and their associated hazards. Numerous remote-sensing studies have attempted to identify dust-emission-source locations. Historically, satellite-based approaches have struggled with dust-plume origin detection. This is because synoptic and mesoscale dust storms on the order of 5 to 100 km rarely originate from a single source but rather from the amalgamation of several point-source dust plumes. These smaller plumes stem from multiple point sources (i.e., plume heads 1 to 10 km across) that can evade coarser-resolution satellite detection.

Advances in satellite image resolution (1 km or better) have greatly enhanced the ability to detect plume heads remotely. A technique was even developed to manually map plume-head point sources in a geospatial information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through a dust-enhancement algorithm. With this technique, a location is digitized and archived if the analyst observes an unobscured plume head in the imagery.

MODIS satellite imagery of the Southwest United States and Mexico. Panels A, B, and C depict the area outlined by the red box within the inset of Panel A. Panel A is a True Color image that depicts white clouds against a brown landscape; a bird's eye representation of Earth. Panel B is a MILLER image that depicts aqua-colored clouds against a dark blue/green landscape. Lofted dust appears pink while high-relief topography appears red. The white dashed box depicts a region that does not contain any lofted dust. Panel C is a EUMETSAT image that depicts red clouds against a purple landscape. Lofted dust appears hot pink. The white circle in each panel depicts a dust event. Note the pink dust that can be seen in Panels B and C is undetectable in Panel A.

Although analysts are able to map dust sources at the satellite-image grid scale, location analysis error is almost unavoidable. Airborne dust must be sufficiently elevated for overland dust enhancement algorithms to work; otherwise, thermal and reflectance contrast signals between the dust and the underlying surface may not be discernable. By the time the dust has reached a sufficient altitude, it is likely to have traveled downstream from its deflating source. As such, errors in digitized source location may be on the order of 1 to 10 km.

Even with this inherent uncertainty, this technique is a popular approach for dust-source identification and is often used as a proxy for dust-source observations. The methodology is also currently being used to incorporate dust sources into the Navy's Coupled Ocean-Atmosphere Mesosphere Prediction System, which to date remains the highest-resolution dust-source treatment in an operational military weather forecast model.

A group of analysts independently mapped plume-head point sources for two predetermined case-study dust events, one in the Southwest United States and one in Southwest Asia. For each case-study region, a single analyst downloaded Automated Surface Observing System (ASOS) weather data to identify dusty days reported in local weather station records. The same analyst then downloaded and processed true-color and dust-enhanced MODIS satellite imagery associated with each day of interest and selected a single time period for each regional case study based on a perceived potential for numerous mappable plume-head point sources. After reviewing mapping procedures and techniques, a group of additional analysts then individually mapped plume-head point sources in the images selected for each region by using ArcMap 10.3 and assigned a quality score (confidence level) to each mapped point. Results from each analyst were subsequently aggregated together and assessed for reproducibility.

This work was done by Samantha N. Sinclair and Sandra L. Jones for the Army Corps of Engineers. ERDC-0002

This Brief includes a Technical Support Package (TSP).

Subjective Mapping of Dust-Emission Sources by Using MODIS Imagery (reference ERDC-0002) is currently available for download from the TSP library.

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