Enabling maize mapping in double-cropping regions of Argentina and Brazil without ground reference data by leveraging multiple satellite platforms

Ryoungseob Kwon, Youngryel Ryu, Philippe Ciais, David Makowski, Changhyun Choi, Huaize Feng, Sungchan Jeong, Pengfei Tang, Kyung­-Do Lee

Large-scale maize mapping is challenging due to the lack of field-level datasets, which were essential for training machine learning models. The United States (US) provides abundant in-situ labels and national maize maps while Argentina and Brazil offered limited data and double-cropping further complicates mapping. In this study, we propose a survey-free method for maize mapping in Argentina and Brazil, with cropping intensity considered. We first pre-trained a maize classifier on US cornfields using Global Ecosystem Dynamics Investigation (GEDI) Relative Height (RH) metrics and Sentinel-2 imagery, then transferred this model to Argentina and Brazil to produce candidate maize labels at peak growth. We distinguished maize from other crops using phenology-based empirical thresholds. These high-confidence labels were then used to train a Random Forest model. Finally, we validated the maps with Google Street View (GSV) imagery and benchmark maps. Our framework produced 18,481 maize and 15,725 non-maize labels in Argentina and 2,125 first-season maize, 7,406 s-season maize and 7,643 non-maize in Brazil. Evaluation of our maize maps against GSV images showed satisfactory agreement, with accuracies around 90 %. The maps also aligned well with benchmark maps, exhibiting 91 % accuracy for Argentina and 88 % for Brazil. Our proposed frameworks can enhance maize mapping where field surveys are challenging.