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Context

• Dave's summary of the context: DavesNotes
• Jeff's Proposal

Plan

The microwave estimate of SWE is not a very good approximation of the reconstructed SWE, so we either need to improve the microwave estimate or add more features.

Possible features to add:

• MODIS snow cover % (mean and sd)
• (Other land cover?)
• topography (elevation/slope/aspect/more?)
• daily temperature, radiation

Possible ways to improve the microwave data:

• compute estimate based on the mean daily reading after removing outliers, rather than interpolating between maxima.

Possible ways to frame the regression:

• predict the reconstructed SWE of each pixel on each day independently
  • • not very much information available
  • • pixels correlate spatially and temporally, so CV accuracy evaluations will be biased high
  • • simple model
• predict the mean or max reconstructed SWE per pixel, aggregated over a month, use time series of daily features as predictors
• use the entire season of features up until a critical date (say April 1st) to predict the max SWE for the season for each pixel

Progress

Per Pixel Per Day Method

• We added the MODIS snow covered area mean and sd. It turns out that these are great features:
• snow covered area is actually a better predictor of SWE than the microwave SWE estimate
• Using linear regression we achieved an R^2 of 0.7, and it generalized well to a test dataset gathered a week after the training data set.
• Using a regression decision tree forest we achieved a test set R^2 of 0.75.

Per Pixel Per Month Method

• Using linear regression we achieved an R^2 of 0.85.

Next Steps

• The major issue with the analyses above is that a large fraction of the data has no snow cover. This is inflating the R^2 scores. We'll need to filter out the snowless pixels and try again.
• Explore the data to better understand the link between the feature and the predictors over time.
• Experiment with different regressors and functions of the existing features, as the relationships are obviously non-linear.
• Add more features.
• Try modeling pixels differently based on their snow covered area.