ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA

Abstract

Crop classification is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data has an advantage in crop classification because of its all-weather imaging capabilities. The objective of this study was to investigate the capability of SAR data for estimation of cotton and maize area in Perambalur district of Tamil Nadu. The multi-temporal Sentinel-1 SAR data was acquired from 2nd September, 2017 to 24th January, 2018. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data was used. Ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. Sixty per cent of the ground truth data were used for training and remaining forty per cent were utilized for validation. The temporal backscattering coefficient (σ0) for cotton and maize were extracted using the training datasets.. The mean backscattering values for cotton crop during the entire cropping period had a range from −11.729 dB to −8.827 dB and from −19.167 dB to −14.186 dB for VV and VH polarization respectively. For maize crop it ranged from −11.248 dB to −8.878 dB and from −19.043 dB to −14.753 dB for VV and VH polarized data respectively. The Spectral Angle Mapper (SAM) and Decision Tree classifier (DT) methods were adopted for cotton and maize area estimation. SAM classified 73259 and 51489 hectares (ha) as cotton and maize respectively in VV polarization. DT classified the area of 61501 and 64530 ha for cotton and maize respectively in VH polarization. The accuracy measures, such as overall accuracy, producer’s accuracy and user’s accuracy and kappa coefficient were estimated. SAM classifier exhibits the overall accuracy of 73.3% for VV Decision tree classifier reported the overall accuracy of 75.0% for VH. It is evident from the present study, that the multi-temporal Sentinel-1 SAR sensor can be well used for the discrimination of cotton and maize crops because of its high temporal resolution which captures the complete phenology of the crops during the cropping period.