This paper uses multi-sensor remote sensing data to study the type and spatio-temporal variability of aerosols emitted from forest fires. The study is based on the Okefenokee Swamp fire that ravaged parts of Georgia and Florida between May and June of 2007. Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to study the aerosol type and its spatial distribution. Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data is used to study the vertical distribution of aerosols. The results show that there is a high concentration of fine mode aerosols during the fire episode. It is also observed that the 24 hour averaged PM2.5 concentration was above unhealthy levels on several occasions, in some instances reaching values over 50µg/m3. The PM10 concentration on the other hand was below unhealthy levels although there were numerous instances of episodic non attainment of the PM10 air quality standard. The study shows that the vertical reach of the aerosol plume over the land ranged from 2 to 3 kilometers.
The retrieval of Live Fuel Moisture Content (LFMC) over fire prone grasslands is important for fire risk and drought assessment. Radiative transfer (RT) model based inversion of measured reflectances for retrievals of LFMC offers a promising method for estimating LFMC. This paper evaluates the extent to which inverse RT model based LFMC retrievals over grasslands can be improved by the use of prior information on soil moisture and LAI. However due to the uncertainty in the procedures used in obtaining the pre-retrieval information about LAI and soil moisture, the prior information is more likely to be in terms of an expected range for LAI and soil moisture rather than exact values. This study uses simulations from coupled soil-leaf-canopy radiative transfer models to investigate the extent to which such categorical prior information may reduce the uncertainty in LFMC retrievals. Results show that under the experimental conditions used in this study, prior information on LAI and soil moisture improves LFMC estimation on the average by about 2.3 to 3.4% (absolute LFMC) depending on the quality and accuracy of the prior information. This can be equivalent to a relative improvement of about 18-27%. This can be significant, since at the dry conditions represented by this study, when fire spread is highly sensitive to LFMC, such improvements in LFMC could considerably improve fire spread predictions and aid fire management decision making. Uncertainty analysis in terms of prediction intervals and standard deviation of errors also show that improvements are significant.