An algorithm for early rice area mapping from satellite remote sensing data in southwestern Guangdong in China based on feature optimization and random Forest.

Estimating of rice areas using images obtained from satellite remote sensing is important for guiding operators. The object of this study was the Sentinel-2A/B image data of the rice planting demonstration regions in southwestern Guangdong, China. We designed an algorithm for early rice area mapping based on feature opti-mization and random forest (RF). For modeling, we selected 35 common remote sensing features and applied out-of-bag (OOB) to construct 7 feature combinations. The results showed that the overall accuracy (OA) and Kappa coefficient of the RF with the best combination were 91.23% and 87.55%, respectively. Compared with support vector machine (SVM) and back propagation neural network (BPNN), the model result of RF was also the best among the three. Additionally, the maximum error of the rice area was less than 16% when the model was transferred to other regions in Guangdong. The feature optimization and RF-based algorithm proposed in this study can effectively map the early rice region. It can be applied to estimate rice area based on satellite remote sensing image data and reveal the ecological status of rice cultivation in southwestern Guangdong.