Flooded Rice Paddy Detection Using Sentinel-1 and PlanetScope Data: A Case Study of the 2018 Spring Flood in West Java, Indonesia

Abstract

This study aims to detect flooded rice paddies in Indonesia using remotely sensed data from a relatively extensive flood that occurred in the Tegalluar area of Bojongsoang in the spring of 2018, which was observed by the Sentinel-1 and PlanetScope satellites. We propose an automatic thresholding method for the detection of flooded areas in rice paddy fields using Sentinel-1 C-band synthetic aperture radar (SAR) data acquisitions from before and during flooding. The flood-detection accuracy was verified using visible and near-infrared images acquired by the PlanetScope satellites. The proposed method showed that the VV (transmit V and receive V polarizations) data outperformed the VH (transmit V and receive H polarizations) data in terms of correlation ratio and discriminant accuracy. The overall classification accuracy of the nonflooded and flooded areas reached 84.7% with the VV data and 80.6% with the VH data, including the error that resulted from the time difference in the data acquired by Sentinel-1 and PlanetScope. Utilizing speckle-reducing filters with SAR data was found to improve the overall classification accuracy by 5%.

Keywords

• Agricultural insurance
• flood
• gamma naught
• linear discriminant analysis (LDA)
• normalized difference water index (NDWI)