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

Abstract

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 multi-RF 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. The proposed FARM approach was then validated under different cropping conditions at three study sites in China and two sites in other countries. The results showed that the FARM framework proved to be more effective than other methods at all five study sites, achieving average overall accuracies (OA) ranging from 89.67%−97.00%. In addition, when transferring the training samples from 2021 to other years (2020/2022), the OAs of site A, site B and site C were high during the heading period, with accuracies of 97.57%, 84.28% and 89.27%, respectively. These results demonstrated that, first, the FARM framework exhibits high efficiency and accuracy in different study areas without the need for extensive fieldwork to collect training samples. Second, the method has good performance in the early-season rice mapping of the new year and can be used to perform timely and accurate rice identification and monitoring tasks. The method shows great potential in obtaining large-area automatic rice mapping results in a timely and accurate manner. The latest release of FARM can be viewed at https://feature-selected.users.earthengine.app/view/farm, and the code is available on GitHub at the link https://github.com/gactyxc/FARM.git.