###### Abstract  
Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe. title=1,..  
can have aerosols with relatively small particulate sizes that can be easily breathed in, often leading to respiratory infection, asthma, lung cancer, and cardiovascular disease among other human health impacts [7]. Smoke aerosols also influence near-ground visibility as reported in the comparison between satellite-detected smoke and the visibilities recorded by meteorological observers [8; 9]. Thus, fire smoke detection is necessary for multiple applications including improving climate change simulations [10], weather prediction [11; 12], and human-environment monitoring [8; 13]. To fully understand the local, regional, and global effects of smoke, methods and products for accurate, near real-time smoke plume detections are urgently needed on regional and global scales. A few methods have been developed to detect fire smoke plumes regionally or globally during recent decades [8; 14; 15; 16]. First, manually drawing smoke plumes has been a commonly used technique for small spatial areas and coarse temporal scales. This is labor- and time-consuming, subjective, and the accuracy of delineated smoke plumes depends on expert knowledge [14; 17; 18; 19; 20]. It is also challenging for this method to visually distinguish smoke from clouds, dust, and other aerosols based only on composited imagery, which is a known issue in the analyst labeled hazard mapping system (HMS) smoke product [21]. Second, multiple frames of satellite images, acquired before and after fire events, have been used to find smoke cores using image differences and then expanded to whole smoke plumes by identifying neighborhood pixels with similar spatial-and-spectral characteristics ([PERSON] et al., 2007). Although this method offers a novel way to detect smoke plumes under specific conditions, there are some disadvantages: (1) smoke cores can hardly be extracted when either the pre-fire or post-fire images are contaminated by smoke, clouds, or bright surfaces; (2) expanding smoke cores recursively is very time-consuming; (3) it is challenging to apply such algorithms to large scales; and (4) false positive/negative smoke cores could result in much larger commission/omission errors after regional expanding.