I am new in Machine Learning and in R. I am working in my Master thesis. I am trying to estimate the LULC of the years 1984, 1990, 2000, 2011, 2014 for characterizing the forest dynamics. I have for each year the bands corresponding to Landsat imageries. The images were corrected and I have calculated vegetation index (NDVI, EVI, SR) and Tasseled Cap Components(Brightness, Greenness, Wetness). All this was performed using i.landsat.toar, i.vi and i.tasscap commands in Grass 7, respectivelly. 

I have as reference the year 2009 since there is a orthophoto and LiDAR data. Using the orthophoto I digitized training polygons for 5 classes (1. Crops, artificial, bareland; 2. pinus forest; 3. Mixed forest; 4. Quercus and 5. Shrubs). 

I fitted a model using Random forest and my intention is to classify the other year datasets wich have 5 reflective bands, 3 vegetation indexes and 3 tasseled Cap Components as predictor variables. 

The performance of the model is quite good, OOB 10.45% but within classes the Mixed forest achieved about 30 % of error. Thus, when I use the model to classify the other years the misclassification is very high. 

Am I doing anything worng? Is the Random forest useful for this purpose? Is there any software, method or algorithm that i could use to estimate the landuse?