Paddy rice mapping in Red River Delta, Vietnam, using Sentinel 1/2 data and machine learning algorithms

ABSTRACT

This study focuses on building a paddy rice map for the Red River Delta region (Vietnam) during the spring crop of 2019. Experiments were conducted with traditional machine learning models (XGBoost, LightGBM) and deep learning models (U-Net, Linknet, DeeplabV3+) based on satellite data from Sentinel 1, Sentinel 2, and topographic maps. The experimental models all gave good evaluation results. The rice maps have good agreements with statistics from the government. The results highlighted that the combination of synthetic aperture radar and optical data with machine learning, deep learning models is an effective approach for short-term high-resolution paddy rice mapping.

KEYWORDS:

• Paddy rice mapping
• Red River Delta
• Sentinel 1
• Sentinel 2
• machine learning
• deep learning

Disclosure statement

No potential conflict of interest was reported by the authors.