An integrated approach of field, weather, and satellite data for monitoring maize phenology.

ABSTRACT: Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013-2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation-identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, ge

SUBMITTER: Nieto L 

PROVIDER: S-EPMC8333045 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature