Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine.

ABSTRACT: Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The norm

SUBMITTER: Magidi J 

PROVIDER: S-EPMC7616265 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature