Paddy fields classification using a 2-dimensional scatterplot of growth phenological features from Sentinel-1 data

Abstract

Rice plays an essential role in ensuring the food security of Indonesia. Hence, rice (paddy) field monitoring using synthetic aperture radar (SAR) satellite data is critical, particularly in tropical regions. This study presents a new algorithm to detect paddy fields in Subang, West Java, using Sentinel-1 SAR with a 12-day revisit acquisition. Three temporal phenological features of paddy growth were used, namely, the minimum and maximum backscatter, as well as their differences. Paddy fields were discriminated from other land covers using a simple thresholding algorithm based on their specific pattern of low minimum, high maximum, and high difference of vertical transmithorizontal receive polarization (VH) backscatter on a 2-dimensional (2D) scatter plot. The results showed that the proposed algorithm had an accuracy of 94.02%, comparable to that of the random forest algorithm and other studies using 3-dimensional (3D) parameters. The proposed algorithm reduces the dimensionality from 3D to 2D and is practical for mapping and monitoring paddy fields. In this context, the application of the algorithm to the surrounding regions of Karawang, Indramayu, and Bekasi achieved high accuracy rates of 93.37%, 92.87%, and 88.13%, respectively.