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

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.