FARM: A fully automated rice mapping framework combining Sentinel-1 SAR and Sentinel-2 multi-temporal imagery

Rice farming exemplifies intensive agriculture, demanding significant inputs to achieve optimal yields. Thus, accurate and precise mapping of rice cultivation is vital for effective agricultural management and food security. However, such studies have been limited by the challenges of obtaining optical cloud-free data and dealing with radar data’s speckle noise. Identifying crops using a single data source poses many difficulties. Additionally, acquiring sufficient representative training samples that accurately reflect diverse phenological patterns is challenging for large-scale monitoring and rice cultivation classification. To address these challenges, this study proposed a fully automated rice-mapping framework (FARM) that combines the strengths of time-series synthetic aperture radar (SAR) and optical satellite imagery for large-scale rice mapping without manual sample collection. First, an object-based, fully automatic training sample generation strategy is introduced. The phenology constraint rule, based on time-series SAR satellite images and specific rice-flooding features, is used to extract rice sample objects. Second, the extracted rice sample objects, adhering to phenological rules, serve as training samples for paddy rice extraction by integrating multiple random forest (RF) classifiers, referred to as the multiRF method, where each RF is individually built using images acquired during each phenological phase of the growing season. Furthermore, the study explored the availability of the method in early-season rice identification by transferring the training samples acquired by the FARM to a new year. This dataset is the final classification maps of the proposed FARM framework.