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Spectral reflectance and backscattering’s of remotely-sensed data presents significant details of different objects on the Earth's surface in numerous applications of space technology. In this study, satellite datasets such as Sentinel-1 i.e. synthetic aperture radar (SAR) and Sentinel-2 satellite datasets i.e. multispectral data is used in order to effectively classify crop patterns of rainfed regions of India. The study areas have been selected in Jhansi district of Uttar Pradesh, Buldhana district of Maharashtra and Ballari district of Karnataka. These three (3) selected districts contains rainfed and irrigated agricultural lands. In the initial stage baseline map of cropland has been prepared by the following concepts; a baseline map is a map including the total cropland area i.e. Rabi and Kharif and non-cropped area’s i.e. man-made (infrastructures + urban), permanent water, permanent vegetation (forest), and other land cover’s depending on the area (for instance wetlands, etc.). This is generated by using annual temporal descriptors (TD). In the next, Sentinel-2 MSI high-resolution satellite data has been further used for crop pattern map derived by applying the supervised classification following the machine learning algorithms (MLA) techniques. The accuracy level of crop pattern map of Ballari district is 84.04%, Jhansi district is 89.3%, and of Buldhana district is 83.09%. The total crop land area in Ballari district is 0.582 million ha, with the total crop types covered area is cotton 32%, groundnut 23%, maize 29%, pearl millet 10% and paddy 6%. The total cropland area in Jhansi district is 0.507 million ha, with the total crop types covered area is black gram 65%, paddy 10%, groundnut 17%, and peppermint 8%. The total cropland area in Buldhana district is 0.686 million ha, with the total crop types covered area is cotton 24%, maize 7%, pigeon pea 16%, sorghum 25% and soybean 28%, respectively.
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