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A Step Towards Digital Agriculture for Development of Object Based Phenology Approach to Classify Sugarcane and Paddy Crops Using Multisensor Data

Crop classification is an important information for agriculture monitoring. Crop classification using synthetic aperture radar (SAR) has been performed in many studies, but for agricultural areas like in India that are very fragmented, it becomes difficult to distinguish between two crop types that lie next to each other. Therefore, in this paper, an attempt has been made to classify sugarcane and paddy crops based on object based crop phenology approach for which object based segmentation is explored. The proposed methodology is developed and tested on Sentinel-1 and Sentinel-2 data. It is observed that the proposed methodology has a potential to classify the crop types with an accuracy of 82%.