Automatic Mapping of Irrigated Areas in Mediteranean Context Using Landsat 8 Time Series Images and Random Forest Algorithm

Groundwater withdrawals by farmers, in Morocco, are very numerous and informal. Therefore, the need for information on the location of irrigated areas is becoming increasingly important. Our main objective, in this study, is to evaluate the use of high-resolution Landsat 8 (L8) time series images and Random forest (RF) method to produce a land cover map with a sufficient precision to monitor the extension of irrigated areas. In the first part of this study, four parameters were evaluated: Number of trees, min split samples, max features and max depth. The results proves that the last parameter is the most important and has more impact on the oob score, which can reach 91 %. The second part of this study was devoted to reduce furthermore the number of features taken as input in the classification process. This was done through feature reduction then selection. The computational time was highly reduced and the best level of classification accuracy was reached by using only Landsat 8 (L8) time series images, statistics on the temporal spectral indices (NDVI, MNDWI) and Range texture.