Multispectral multi-temporal crop cover classification over Türkiye using random forest algorithm

Dönmez Altındal, Elif
Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. In this thesis, agricultural crop mapping is performed over Türkiye. Sentinel-2 Level-2A images with 10-meter spatial resolution acquired between March 15, 2019, and October 15, 2019, are reduced to 15-day median images. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-meter temperature data is averaged to divide the study area into three temperature zones as Low (LTZ), Medium (MTZ), and High-Temperature Zones (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes.


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Citation Formats
E. Dönmez Altındal, “Multispectral multi-temporal crop cover classification over Türkiye using random forest algorithm,” M.S. - Master of Science, Middle East Technical University, 2022.