Mr. Ruwanda Prasetya Profile

Ruwanda Prasetya

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Proceedings Article | 24 December 2019 Paper

Paddy and non-paddy crops mapping using multi-temporal data of Sentinel-1A in part of Bantul Regency

Ruwanda Prasetya, Projo Danoedoro

Proceedings Volume 11372, 113720G (2019) https://doi.org/10.1117/12.2540635

KEYWORDS: Polarization, Synthetic aperture radar, Agriculture, Backscatter, Image classification, Sensors, Satellites

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Monitoring of rice field, as a place for producing rice is very important to realize one aspect of food security, namely food availability. Modern agriculture has been widely utilize remote sensing data, especially optical images for monitoring agricultural land in various aspects of land management. However, the use of optical images is hampered by cloud cover when monitoring rice fields because most of them located in tropical countries, so there is an alternative to using SAR imagery that has ability to penetrate clouds. One of the SAR image products is Sentinel-1A with band C on its sensors which was launched in 2014 and the data can be utilized by the wider community for free. The purpose of this study was to determine the ability of multitemporal Sentinel-1A SAR imagery in identifying paddy and non-paddy in Bantul Regency’s agriculture field which was measured through its mapping accuracy. Sentinel-1A multi-temporal images with ten recording dates from February to May 2018 were used as the main data for this study. The method used is a digital classification with two approaches i.e. parametric with MLC algorithm and non-parametric with k-NN algorithm. In addition, the Sentinel-1A, which consists of VV and VH polarization, performed in three classification schemes (VV multi-temporal, VH multi-temporal, and VV and VH multi-temporal). The classification results show that multi-temporal Sentinel-1A can be used to identify paddy and non-paddy fields with an accuracy of 77.69% (VV multitemporal-MLC), 82.15% (VH multi-temporal-MLC), 88.45% (VV and VH multi-temporal-MLC), 76.64% (VV multitemporal-kNN), 78.47% (VH multi-temporal-kNN) and 79.52% (VV and VH multi-temporal-kNN).

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