"Snow gone" (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN stage. Those included: (i) enhanced vegetation index (EVI), (ii) normalized difference water index (NDWI) using the shortwave infrared (SWIR) spectral bands centered at 1.64 µm (NDWI 1.64µm ) and at 2.13 µm (NDWI 2.13µm ), and (iii) normalized difference snow index (NDSI). These were calculated using the 500 m 8-day gridded MODIS-based composites of surface reflectance data (i.e., MOD09A1 v.005) for the period 2006-08. We performed a qualitative evaluation of these indices over two forest fire prone natural subregions in Alberta (i.e., central mixedwood and lower boreal highlands). In the process, we generated and compared the natural subregion-specific lookout tower sites average: (i) temporal trends for each of the indices, and (ii) SGN stage using the ground-based observations available from Alberta Sustainable Resource Development. The EVI-values were found to have large uncertainty at the onset of the spring and unable to predict the SGN stages precisely. In terms of NDSI, it showed earlier prediction capabilities. On the contrary, both of the NDWI's showed distinct pattern (i.e., reached a minimum value before started to increase again during the spring) in relation to observed SGN stages. Thus further analysis was carried out to determine the best predictor by comparing the NDWI's predicted SGN stages with the ground-based observations at all of the individual lookout tower sites OPEN ACCESSRemote Sens. 2010, 2 1349(approximately 120 in total) across the study area. It revealed that NDWI 2.13µm demonstrated better prediction capabilities (i.e., on an average approximately 90% of the observations fell within ±2 periods or ±16 days of deviation) in comparison to NDWI 1.64µm (i.e., on an average approximately 73% of the observations fell within ±2 periods or ±16 days of deviation).

Snow water equivalent (SWE) and snow depth are some of the most important quantities in describing the properties of the accumulated snow during winter, which is a source of runoff during spring season. Here, our objective was to reconstruct the spatial dynamics of SWE and snow depth over a study area in eastern parts of the northern Alberta during the period 2007-09. The employed methods consisted of: (i) delineating snow presence from Moderate Resolution Imaging Spectroradiometer (MODIS)derived normalized difference snow index (NDSI)-images, (ii) calculating heating degree days (HDD) from MODIS-based surface temperature images, (iii) modelling net solar radiation, and (iv) integrating all of the above steps in the frame of a process based snow-melt model and SWE ground data as well. We used ~45% of the ground data (i.e., ~19 data points) in calibrating the values of base temperature and heating degree day coefficient for the model. Then the remaining ~55% of the ground data (i.e., 23 data points) were used in validation. It revealed that the agreement between the model and measured SWE-values were reasonable (i.e., 59%, 72%, and 62% of the time values were within ±20% deviations during 2007, 2008, and 2009 respectively). The root mean square deviation (RMSD) between the measured and modelled SWE-values were also reasonable and found to be ±24.75 mm in 2007, ±25.05 mm in 2008, and ±23.99 mm in 2009. Overall, the SWE-predictions at all of the measurement sites were on an average 7.5% higher in 2007, 10.2% lower in 2008, and 1.9% lower in 2009 than that of ground-based measurements. During the period 2007-2009, we found that the study area-specific average values of SWE and its depth were 177 mm and 694 mm respectively.

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