Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS

Remote sensing images are a good source of crop and soil information, which can be used to derive agronomic information for field management and yield prediction. Soybean (Glycine max (L.) Merrill) and corn (Zea mays L.) are the most important crops in Argentina taking into account the economic yiel...

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Autores principales: Bocco, Mónica, Ovando, Gustavo Gabriel, Sayago, Silvina, Willington, Enrique
Formato: article
Lenguaje:Inglés
Publicado: 2022
Materias:
Modelos matemáticos
Cultivos
Teledetección
Sensores remotos
Índice de vegetación
MODIS
Acceso en línea:http://hdl.handle.net/11086/24477
http://www.aet.org.es/?q=revista39-8
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R14111086-24477
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Modelos matemáticos
Cultivos
Teledetección
Sensores remotos
Índice de vegetación
MODIS
spellingShingle Modelos matemáticos
Cultivos
Teledetección
Sensores remotos
Índice de vegetación
MODIS
Bocco, Mónica
Ovando, Gustavo Gabriel
Sayago, Silvina
Willington, Enrique
Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
topic_facet Modelos matemáticos
Cultivos
Teledetección
Sensores remotos
Índice de vegetación
MODIS
description Remote sensing images are a good source of crop and soil information, which can be used to derive agronomic information for field management and yield prediction. Soybean (Glycine max (L.) Merrill) and corn (Zea mays L.) are the most important crops in Argentina taking into account the economic yield obtained by farmers and the sown area. In this work, simple mathematical models (linear, second order polynomial and exponential), with different vegetation indices (VI) derived of Moderateresolution Imaging Spectroradiometer (MODIS) images as inputs, were evaluated. The models were applied to estimate soybean and corn percent ground cover (fCover) over the growing season. The VI employed were the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), a modified SAVI (MSAVI), simple ratio (SR) and perpendicular vegetation index (PVI). The performances of the models (linear, polynomial and exponential) were very good and their results were equivalent. Although all models could successfully estimate fCover, results showed that, excepted with SR input, a linear model can predict ground coverage with R2 values greater than 0.86, when both crops are considered. When models are applied to soybean and corn separately, linear model with SAVI index has the best performance.
format article
author Bocco, Mónica
Ovando, Gustavo Gabriel
Sayago, Silvina
Willington, Enrique
author_facet Bocco, Mónica
Ovando, Gustavo Gabriel
Sayago, Silvina
Willington, Enrique
author_sort Bocco, Mónica
title Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
title_short Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
title_full Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
title_fullStr Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
title_full_unstemmed Simple models to estimate soybean and corn percent ground cover with vegetation indices from MODIS
title_sort simple models to estimate soybean and corn percent ground cover with vegetation indices from modis
publishDate 2022
url http://hdl.handle.net/11086/24477
http://www.aet.org.es/?q=revista39-8
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AT sayagosilvina simplemodelstoestimatesoybeanandcornpercentgroundcoverwithvegetationindicesfrommodis
AT willingtonenrique simplemodelstoestimatesoybeanandcornpercentgroundcoverwithvegetationindicesfrommodis
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