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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
31/01/2020 |
Actualizado : |
31/01/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
GASO, D.; BERGER, A.; CIGANDA, V. |
Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Computers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026 |
ISSN : |
0168-1699 |
DOI : |
10.1016/j.compag.2019.02.026 |
Idioma : |
Inglés |
Notas : |
Article history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019..
This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. |
Contenido : |
ABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. MenosABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to ... Presentar Todo |
Palabras claves : |
Crop growth model; Landsat; Leaf area index; Wheat; Yield. |
Asunto categoría : |
F01 Cultivo |
Marc : |
LEADER 02944naa a2200241 a 4500 001 1060735 005 2020-01-31 008 2019 bl uuuu u00u1 u #d 022 $a0168-1699 024 7 $a10.1016/j.compag.2019.02.026$2DOI 100 1 $aGASO, D. 245 $aPredicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images.$h[electronic resource] 260 $c2019 500 $aArticle history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019.. This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. 520 $aABSTRACT. Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. 653 $aCrop growth model 653 $aLandsat 653 $aLeaf area index 653 $aWheat 653 $aYield 700 1 $aBERGER, A. 700 1 $aCIGANDA, V. 773 $tComputers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Tacuarembó. |
Fecha actual : |
21/02/2014 |
Actualizado : |
27/05/2021 |
Tipo de producción científica : |
Documentos |
Autor : |
SORIA, J.; PISANO, J.; CABRERA, D.; CASTILLO, A.; GARCIA, C.; CARRAU, F.; LEONI, C.; PEREIRA, G. |
Afiliación : |
JORGE RAUL SORIA BARAIBAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JULIO CESAR PISANO CARBAJAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS DANILO CABRERA BOLOGNA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALICIA MARIA CASTILLO SALLE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CLAUDIO CESAR GARCIA GALLARRETA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO CARRAU CORRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA LEONI VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO CESAR PEREIRA BORNIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Comportamiento de arándanos (blueberry) tipo Rabbiteye y Southern Highbush en INIA Salto Grande (31ºS, 57ºW, 50 m altitud) e INIA Tacuarembó (31º42S, 55º40W, 100 m altitud), Uruguay. |
Fecha de publicación : |
2002 |
Fuente / Imprenta : |
In: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Avances en experimentación de frutales alternativos: arándanos y otros berries. (Reunión técnica). Las Brujas, Canelones (Uruguay): INIA, 2002. |
Páginas : |
p. 11-21 |
Serie : |
(INIA Serie Actividades de Difusión; 286) |
Idioma : |
Español |
Notas : |
INIA Las Brujas |
Thesagro : |
ARANDANO; BAYAS; ENSAYOS DE VARIEDADES. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/324/1/111219210807161453.pdf;ad286#page=12
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Marc : |
LEADER 01035naa a2200265 a 4500 001 1007536 005 2021-05-27 008 2002 bl uuuu u00u1 u #d 100 1 $aSORIA, J. 245 $aComportamiento de arándanos (blueberry) tipo Rabbiteye y Southern Highbush en INIA Salto Grande (31ºS, 57ºW, 50 m altitud) e INIA Tacuarembó (31º42S, 55º40W, 100 m altitud), Uruguay. 260 $c2002 300 $ap. 11-21 490 $a(INIA Serie Actividades de Difusión; 286) 500 $aINIA Las Brujas 650 $aARANDANO 650 $aBAYAS 650 $aENSAYOS DE VARIEDADES 700 1 $aPISANO, J. 700 1 $aCABRERA, D. 700 1 $aCASTILLO, A. 700 1 $aGARCIA, C. 700 1 $aCARRAU, F. 700 1 $aLEONI, C. 700 1 $aPEREIRA, G. 773 $tIn: INIA (Instituto Nacional de Investigación Agropecuaria); Programa Nacional Producción Frutícola. Avances en experimentación de frutales alternativos: arándanos y otros berries. (Reunión técnica). Las Brujas, Canelones (Uruguay): INIA, 2002.
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