Discussion

SEPAL vs. GEE

SEPAL	GEE
pro - quick to learn	con - takes more time to learn
pro - no programming experience needed	con - requires understanding of programming basics
con - complicated workflow using multiple different tools	pro - simple worflow that can be done in 2 scripts
con - limited choices for input data	pro - can import any data sets from personal computer or GEE Data Catalogue (PALSAR, Harmonized Landsat Sentinel)
con - limited choices for pre- and post-processing	pro - can access all pre- and post-processing GEE tools (smoothing, cloud masking, calculating indices)
con - limited options for altering model parameters	pro - many options for altering model parameters (multiprobability mode, variable importance)
con - difficult to rerun to test out different versions	pro - easy to rerun to test out different versions

increase the number of training and testing points
- overall and/or using a stratified approach with more points in classes with low accuracies
change or combine cloud masking strategies
composite imagery for more than 1 year
merge or separate classes of interest (e.g. one combined forest class instead of separated forest classes, separate wetlands class)

add and remove predictor variables based on variable importance and accuracy (e.g. different combinations of imagery sources or indices)
increase the number of trees in the random forest model
generate more training points in the areas of highest uncertainty and retrain the model with the addition of these new points