Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming
Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season....
| Main Authors: | , , , , , |
|---|---|
| Format: | Online |
| Language: | spa |
| Published: |
Facultad de Ciencias Agropecuarias
2021
|
| Subjects: | |
| Online Access: | https://revistas.unc.edu.ar/index.php/agris/article/view/25148 |
| _version_ | 1811172635326808064 |
|---|---|
| author | Ovando, Gustavo de la Casa, Antonio Díaz, Guillermo Díaz, Pablo Bressanini, Luciano Miranda, Cristian |
| author_facet | Ovando, Gustavo de la Casa, Antonio Díaz, Guillermo Díaz, Pablo Bressanini, Luciano Miranda, Cristian |
| author_sort | Ovando, Gustavo |
| collection | Portal de Revistas |
| description | Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season. The objective of this work was to evaluate the performance of different Sentinel-2A VIs to estimate soybean (Glycine max (L.) Merril) yield within the framework of PA. The study was carried out using a soybean yield map during the 2017/2018 season from a plot located to the south of Córdoba city, Argentina. Every two bands were taken from a Sentinel-2A image from 4/Feb/2018 and VI were calculated using differences, ratios and normalized differences. The absolute value of the Pearson linear correlation coefficient (|r|) between the different IVs and soybean yield was computed. The highest value of |r| (0.726) corresponded to the difference between bands 8 (NIR) and 12 (SWIR), allowing to reproduce with sufficient precision the spatial variability of yields in the plot. |
| format | Online |
| id | oai:ojs.revistas.unc.edu.ar:article-25148 |
| institution | Universidad Nacional de Cordoba |
| language | spa |
| publishDate | 2021 |
| publisher | Facultad de Ciencias Agropecuarias |
| record_format | ojs |
| spelling | oai:ojs.revistas.unc.edu.ar:article-251482022-03-22T16:37:50Z Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming Desempeño de diferentes índices de vegetación de Sentinel-2 para estimar el rendimiento de soja en agricultura de precisión Ovando, Gustavo de la Casa, Antonio Díaz, Guillermo Díaz, Pablo Bressanini, Luciano Miranda, Cristian Teledetección mapa de rendimiento coeficiente de correlaci´ón lineal de Pearson remote sensing yield map Pearson linear correlation coefficient Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season. The objective of this work was to evaluate the performance of different Sentinel-2A VIs to estimate soybean (Glycine max (L.) Merril) yield within the framework of PA. The study was carried out using a soybean yield map during the 2017/2018 season from a plot located to the south of Córdoba city, Argentina. Every two bands were taken from a Sentinel-2A image from 4/Feb/2018 and VI were calculated using differences, ratios and normalized differences. The absolute value of the Pearson linear correlation coefficient (|r|) between the different IVs and soybean yield was computed. The highest value of |r| (0.726) corresponded to the difference between bands 8 (NIR) and 12 (SWIR), allowing to reproduce with sufficient precision the spatial variability of yields in the plot. La agricultura de precisión (AP) apunta a identificar la variabilidad del cultivo y del suelo en un lote para mejorar el manejo y optimizar el empleo de insumos. Los mapas de rendimiento son herramientas relevantes para planificar la AP. Los índices de vegetación (IV) provenientes de la teledetección permiten monitorear la variación espacio-temporal de los cultivos durante la estación de crecimiento. El objetivo del presente trabajo fue evaluar la idoneidad de diferentes IV de Sentinel-2A para estimar el rendimiento de soja (Glycine max (L.) Merril) en el marco de la AP. El estudio se realizó empleando un mapa de rendimiento de soja (campaña 2017-2018) de un lote ubicado al sur de laciudad de Córdoba, Argentina. Se calcularon IV empleando la diferencia, el cociente y la diferencia normalizada, tomando de a dos bandas de una imagen de Sentinel-2A del 4/2/2018. Se computó el valor absoluto del coeficientede correlación lineal de Pearson (|r|) entre los distintos IV y el rendimiento de soja. El mayor valor de |r| (0,726) correspondió a la diferencia entre las bandas 8 (NIR) y 12 (SWIR), permitiendo reproducir con suficiente precisión yanticipación la variabilidad espacial de los rendimientos en el lote. Facultad de Ciencias Agropecuarias 2021-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html https://revistas.unc.edu.ar/index.php/agris/article/view/25148 10.31047/1668.298x.v38.n2.25148 AgriScientia; Vol. 38 No. 2 (2021); 1-12 AgriScientia; Vol. 38 Núm. 2 (2021); 1-12 1668-298X 10.31047/1668.298x.v38.n2 spa https://revistas.unc.edu.ar/index.php/agris/article/view/25148/36782 https://revistas.unc.edu.ar/index.php/agris/article/view/25148/37361 Derechos de autor 2021 Gustavo Ovando, Antonio de la Casa, Guillermo Díaz, Pablo Díaz, Luciano Bressanini, Cristian Miranda https://creativecommons.org/licenses/by-sa/4.0 |
| spellingShingle | Teledetección mapa de rendimiento coeficiente de correlaci´ón lineal de Pearson remote sensing yield map Pearson linear correlation coefficient Ovando, Gustavo de la Casa, Antonio Díaz, Guillermo Díaz, Pablo Bressanini, Luciano Miranda, Cristian Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title | Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title_alt | Desempeño de diferentes índices de vegetación de Sentinel-2 para estimar el rendimiento de soja en agricultura de precisión |
| title_full | Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title_fullStr | Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title_full_unstemmed | Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title_short | Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming |
| title_sort | performance of different sentinel 2a vegetation indices to estimate soybean yield in precision farming |
| topic | Teledetección mapa de rendimiento coeficiente de correlaci´ón lineal de Pearson remote sensing yield map Pearson linear correlation coefficient |
| topic_facet | Teledetección mapa de rendimiento coeficiente de correlaci´ón lineal de Pearson remote sensing yield map Pearson linear correlation coefficient |
| url | https://revistas.unc.edu.ar/index.php/agris/article/view/25148 |
| work_keys_str_mv | AT ovandogustavo performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT delacasaantonio performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT diazguillermo performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT diazpablo performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT bressaniniluciano performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT mirandacristian performanceofdifferentsentinel2avegetationindicestoestimatesoybeanyieldinprecisionfarming AT ovandogustavo desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision AT delacasaantonio desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision AT diazguillermo desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision AT diazpablo desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision AT bressaniniluciano desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision AT mirandacristian desempenodediferentesindicesdevegetaciondesentinel2paraestimarelrendimientodesojaenagriculturadeprecision |