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 phe...

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Autores principales:	Luciana Nieto, Raí Schwalbert, P. V. Vara Prasad, Bradley J. S. C. Olson, Ignacio A. Ciampitti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c7875ae560b54618815e0502ea516170
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spelling	oai:doaj.org-article:c7875ae560b54618815e0502ea5161702021-12-02T14:53:35ZAn integrated approach of field, weather, and satellite data for monitoring maize phenology10.1038/s41598-021-95253-72045-2322https://doaj.org/article/c7875ae560b54618815e0502ea5161702021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95253-7https://doaj.org/toc/2045-2322Abstract 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, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.Luciana NietoRaí SchwalbertP. V. Vara PrasadBradley J. S. C. OlsonIgnacio A. CiampittiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luciana Nieto Raí Schwalbert P. V. Vara Prasad Bradley J. S. C. Olson Ignacio A. Ciampitti An integrated approach of field, weather, and satellite data for monitoring maize phenology
description	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, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.
format	article
author	Luciana Nieto Raí Schwalbert P. V. Vara Prasad Bradley J. S. C. Olson Ignacio A. Ciampitti
author_facet	Luciana Nieto Raí Schwalbert P. V. Vara Prasad Bradley J. S. C. Olson Ignacio A. Ciampitti
author_sort	Luciana Nieto
title	An integrated approach of field, weather, and satellite data for monitoring maize phenology
title_short	An integrated approach of field, weather, and satellite data for monitoring maize phenology
title_full	An integrated approach of field, weather, and satellite data for monitoring maize phenology
title_fullStr	An integrated approach of field, weather, and satellite data for monitoring maize phenology
title_full_unstemmed	An integrated approach of field, weather, and satellite data for monitoring maize phenology
title_sort	integrated approach of field, weather, and satellite data for monitoring maize phenology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c7875ae560b54618815e0502ea516170
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An integrated approach of field, weather, and satellite data for monitoring maize phenology

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