Ali shamsoddini; Bahar Asadi
Abstract
Identifying and mapping crops provides important information for managing agricultural lands and estimating the area under cultivation of crops. This study investigates the importance of red edge bands for segregation of crops including wheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet ...
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Identifying and mapping crops provides important information for managing agricultural lands and estimating the area under cultivation of crops. This study investigates the importance of red edge bands for segregation of crops including wheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using random forest method and support vector machine. For this purpose, the time series of Sentinel-1 and 2 images in 2019 from the northwest of Ardabil was called in the Google Earth Engine (GEE) platform. In order to study the performance of spectral and temporal information, plant indices and backscatter information on the crop mapping, different band combinations were examined. Using the random forest feature selection method, important features were identified and introduced as the input of the random forest and the support vector machine classifiers. Random forest provided the best results for all scenarios. The results showed that the addition of red edge wavelengths and red edge-based vegetation indices are more useful than other bands and vegetation indices for mapping barley, beans, broad beans and flax. The best result among different combinations of features was related to the time series of spectral features of Sentinel-2 images fused with the time series of Sentinel-1 images for that the overall accuracy and the kappa coefficient were 84.67% and 82.31%, respectively. Moreover, the results showed that red edge bands and red edge-based vegetation indices are efficient to identify crops from each other.
