Evaluating Classifier Performance in Mapping Paddy Rice Fields Using Multi-Temporal SAR Data on Google Earth Engine

Mapping paddy rice fields is crucial for monitoring rice production, which is vital for food security and economic stability. Prior studies have utilized remote sensing data along with various machine learning algorithms to accomplish this task. However, the performance of these classifiers under different conditions remains an area worth investigating. In this study, rice-growing areas are delineated from other crops and land cover types using multi-temporal synthetic aperture radar (SAR) imageries recorded by Sentinel-1 and classification algorithms—Naïve Bayes, CART, and Random Forest—on the Google Earth Engine (GEE) platform. Results show that the random forest classifier outperforms the others with an overall accuracy of 85.90% and a kappa coefficient of 0.7180. This superior performance can be attributed to its capability to handle complex, high-dimensional data, which aligns well with the properties of the multi-temporal SAR data used to observe the growth cycle of paddy rice. The differing performances of the three classifiers underscored the importance of selecting an appropriate algorithm, especially when leveraging the distinctive radar signatures of paddy rice over time, to enhance the accuracy of mapping paddy rice fields.