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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/115419
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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 |
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2020-10 |
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