Raster values NoData category not recognized as factor by caret random forest model

Question

I want to predict a Random Forest model onto the values from a raster stack of 20 different variables, some of which are categorical and treated as factors in the model. I uses values() to extract data from the raster stack and then as.factor() for the columns needing to be factors. But the levels of the factors do not match the levels of the new data - because of the NA values in the new data not recognized as a factor level by the model. I tried removing NA and replacing with "" nothing and that did not work.

I am not clear on using NoData versus NA or "" for raster values with no data. I guess I could add NA to factor levels in the model (rfcaret_1000) but worry that the model would then consider NA as a legitimate value?

#new data from raster stack
levels(val$LU2005)
[1] "NA" "1"  "10" "11" "12" "13" "14" "15" "2"  "3"  "4"  "5" 
[13] "6"  "7"  "8"  "9" 

#tree learning model levels
rfcaret_1000$xvalues
[1] "1"  "10" "11" "12" "13" "14" "15" "2"  "3"  "4"  "5" 
[13] "6"  "7"  "8"  "9"