Forecasting maize yield at field scale based on high-resolution satellite imagery

Autores
Schwalbert, Rai A.; Amado, Telmo J.C.; Nieto, Luciana; Varela, Sebastián; Corassa, Geomar M.; Horbe, Tiago A.N.; Rice, Charles W.; Peralta, Nahuel R.; Ciampitti, Ignacio A.
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.
Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Yield forecasting models
Maize
Satellite imagery
Yield maps
Model validation
Sentinel-2
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/115419

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spelling Forecasting maize yield at field scale based on high-resolution satellite imagerySchwalbert, Rai A.Amado, Telmo J.C.Nieto, LucianaVarela, SebastiánCorassa, Geomar M.Horbe, Tiago A.N.Rice, Charles W.Peralta, Nahuel R.Ciampitti, Ignacio A.Ciencias InformáticasYield forecasting modelsMaizeSatellite imageryYield mapsModel validationSentinel-2Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020Sociedad Argentina de Informática e Investigación Operativa2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/115419enginfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/reference/doi/10.1016/j.biosystemseng.2018.04.020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:26:50Zoai:sedici.unlp.edu.ar:10915/115419Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:26:50.847SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Forecasting maize yield at field scale based on high-resolution satellite imagery
title Forecasting maize yield at field scale based on high-resolution satellite imagery
spellingShingle Forecasting maize yield at field scale based on high-resolution satellite imagery
Schwalbert, Rai A.
Ciencias Informáticas
Yield forecasting models
Maize
Satellite imagery
Yield maps
Model validation
Sentinel-2
title_short Forecasting maize yield at field scale based on high-resolution satellite imagery
title_full Forecasting maize yield at field scale based on high-resolution satellite imagery
title_fullStr Forecasting maize yield at field scale based on high-resolution satellite imagery
title_full_unstemmed Forecasting maize yield at field scale based on high-resolution satellite imagery
title_sort Forecasting maize yield at field scale based on high-resolution satellite imagery
dc.creator.none.fl_str_mv Schwalbert, Rai A.
Amado, Telmo J.C.
Nieto, Luciana
Varela, Sebastián
Corassa, Geomar M.
Horbe, Tiago A.N.
Rice, Charles W.
Peralta, Nahuel R.
Ciampitti, Ignacio A.
author Schwalbert, Rai A.
author_facet Schwalbert, Rai A.
Amado, Telmo J.C.
Nieto, Luciana
Varela, Sebastián
Corassa, Geomar M.
Horbe, Tiago A.N.
Rice, Charles W.
Peralta, Nahuel R.
Ciampitti, Ignacio A.
author_role author
author2 Amado, Telmo J.C.
Nieto, Luciana
Varela, Sebastián
Corassa, Geomar M.
Horbe, Tiago A.N.
Rice, Charles W.
Peralta, Nahuel R.
Ciampitti, Ignacio A.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Yield forecasting models
Maize
Satellite imagery
Yield maps
Model validation
Sentinel-2
topic Ciencias Informáticas
Yield forecasting models
Maize
Satellite imagery
Yield maps
Model validation
Sentinel-2
dc.description.none.fl_txt_mv Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.
Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020
Sociedad Argentina de Informática e Investigación Operativa
description Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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