Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index

Autores
Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Piccolo, Maria Cintia
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using the Temperature Vegetation Dryness Index (TVDI). Triangular scatters from land surface temperature (LST) and enhanced vegetation index (EVI) space from MODIS (Moderate Resolution Imaging Spectroradiometer) were utilized to obtain TVDI and to estimate soil moisture availability. Then soybean and wheat crops yield was estimated on four agro-climatic zones of Argentine Pampas. TVDI showed a strong correlation with soil moisture measurements, with R2 values ranged from 0.61 to 0.83 and also it was in agreement with spatial pattern of soil moisture. Moreover, results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas. Depending on the agro-climatic zone, R2 values ranged from 0.68 to 0.79 for soybean crop and 0.76 to 0.81 for wheat. The RMSE values were 366 and 380 kg ha-1 for soybean and they varied between 300 and 550 kg ha-1 in the case of wheat crop. When expressed as percentages of actual yield, the RMSE values ranged from 12 to 13% for soybean and 14 to 22% for wheat. The bias values indicated that the obtained models underestimated soybean and wheat yield. Accurate crop grain yield forecast using the developed regression models was achieved one to three months before harvest. In many cases the results were better than others obtained using only a vegetation index, showing the aptitude of surface temperature and vegetation index combination to reflect the crop water condition. Finally, the analysis of a wide range of soil moisture availability allowed us to develop a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale.
Fil: Holzman, Mauro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Rivas, Raúl Eduardo. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Piccolo, Maria Cintia. Universidad Nacional del Sur. Departamento de Geografía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Soil Moisture
Modis
Optical Thermal
Crop Yield Forecasting
Remote Sensing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/12503
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/12503
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation indexHolzman, Mauro EzequielRivas, Raúl EduardoPiccolo, Maria CintiaSoil MoistureModisOptical ThermalCrop Yield ForecastingRemote Sensinghttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using the Temperature Vegetation Dryness Index (TVDI). Triangular scatters from land surface temperature (LST) and enhanced vegetation index (EVI) space from MODIS (Moderate Resolution Imaging Spectroradiometer) were utilized to obtain TVDI and to estimate soil moisture availability. Then soybean and wheat crops yield was estimated on four agro-climatic zones of Argentine Pampas. TVDI showed a strong correlation with soil moisture measurements, with R2 values ranged from 0.61 to 0.83 and also it was in agreement with spatial pattern of soil moisture. Moreover, results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas. Depending on the agro-climatic zone, R2 values ranged from 0.68 to 0.79 for soybean crop and 0.76 to 0.81 for wheat. The RMSE values were 366 and 380 kg ha-1 for soybean and they varied between 300 and 550 kg ha-1 in the case of wheat crop. When expressed as percentages of actual yield, the RMSE values ranged from 12 to 13% for soybean and 14 to 22% for wheat. The bias values indicated that the obtained models underestimated soybean and wheat yield. Accurate crop grain yield forecast using the developed regression models was achieved one to three months before harvest. In many cases the results were better than others obtained using only a vegetation index, showing the aptitude of surface temperature and vegetation index combination to reflect the crop water condition. Finally, the analysis of a wide range of soil moisture availability allowed us to develop a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale.Fil: Holzman, Mauro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; ArgentinaFil: Rivas, Raúl Eduardo. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; ArgentinaFil: Piccolo, Maria Cintia. Universidad Nacional del Sur. Departamento de Geografía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12503Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Piccolo, Maria Cintia; Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index; Elsevier Science; Itc Journal; 28; 5-2014; 181-1920303-2434enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0303243413001748info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.jag.2013.12.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:02:40Zoai:ri.conicet.gov.ar:11336/12503instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:02:41.078CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
title Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
spellingShingle Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
Holzman, Mauro Ezequiel
Soil Moisture
Modis
Optical Thermal
Crop Yield Forecasting
Remote Sensing
title_short Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
title_full Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
title_fullStr Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
title_full_unstemmed Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
title_sort Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
dc.creator.none.fl_str_mv Holzman, Mauro Ezequiel
Rivas, Raúl Eduardo
Piccolo, Maria Cintia
author Holzman, Mauro Ezequiel
author_facet Holzman, Mauro Ezequiel
Rivas, Raúl Eduardo
Piccolo, Maria Cintia
author_role author
author2 Rivas, Raúl Eduardo
Piccolo, Maria Cintia
author2_role author
author
dc.subject.none.fl_str_mv Soil Moisture
Modis
Optical Thermal
Crop Yield Forecasting
Remote Sensing
topic Soil Moisture
Modis
Optical Thermal
Crop Yield Forecasting
Remote Sensing
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using the Temperature Vegetation Dryness Index (TVDI). Triangular scatters from land surface temperature (LST) and enhanced vegetation index (EVI) space from MODIS (Moderate Resolution Imaging Spectroradiometer) were utilized to obtain TVDI and to estimate soil moisture availability. Then soybean and wheat crops yield was estimated on four agro-climatic zones of Argentine Pampas. TVDI showed a strong correlation with soil moisture measurements, with R2 values ranged from 0.61 to 0.83 and also it was in agreement with spatial pattern of soil moisture. Moreover, results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas. Depending on the agro-climatic zone, R2 values ranged from 0.68 to 0.79 for soybean crop and 0.76 to 0.81 for wheat. The RMSE values were 366 and 380 kg ha-1 for soybean and they varied between 300 and 550 kg ha-1 in the case of wheat crop. When expressed as percentages of actual yield, the RMSE values ranged from 12 to 13% for soybean and 14 to 22% for wheat. The bias values indicated that the obtained models underestimated soybean and wheat yield. Accurate crop grain yield forecast using the developed regression models was achieved one to three months before harvest. In many cases the results were better than others obtained using only a vegetation index, showing the aptitude of surface temperature and vegetation index combination to reflect the crop water condition. Finally, the analysis of a wide range of soil moisture availability allowed us to develop a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale.
Fil: Holzman, Mauro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Rivas, Raúl Eduardo. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Azul; Argentina
Fil: Piccolo, Maria Cintia. Universidad Nacional del Sur. Departamento de Geografía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using the Temperature Vegetation Dryness Index (TVDI). Triangular scatters from land surface temperature (LST) and enhanced vegetation index (EVI) space from MODIS (Moderate Resolution Imaging Spectroradiometer) were utilized to obtain TVDI and to estimate soil moisture availability. Then soybean and wheat crops yield was estimated on four agro-climatic zones of Argentine Pampas. TVDI showed a strong correlation with soil moisture measurements, with R2 values ranged from 0.61 to 0.83 and also it was in agreement with spatial pattern of soil moisture. Moreover, results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas. Depending on the agro-climatic zone, R2 values ranged from 0.68 to 0.79 for soybean crop and 0.76 to 0.81 for wheat. The RMSE values were 366 and 380 kg ha-1 for soybean and they varied between 300 and 550 kg ha-1 in the case of wheat crop. When expressed as percentages of actual yield, the RMSE values ranged from 12 to 13% for soybean and 14 to 22% for wheat. The bias values indicated that the obtained models underestimated soybean and wheat yield. Accurate crop grain yield forecast using the developed regression models was achieved one to three months before harvest. In many cases the results were better than others obtained using only a vegetation index, showing the aptitude of surface temperature and vegetation index combination to reflect the crop water condition. Finally, the analysis of a wide range of soil moisture availability allowed us to develop a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/12503
Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Piccolo, Maria Cintia; Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index; Elsevier Science; Itc Journal; 28; 5-2014; 181-192
0303-2434
url http://hdl.handle.net/11336/12503
identifier_str_mv Holzman, Mauro Ezequiel; Rivas, Raúl Eduardo; Piccolo, Maria Cintia; Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index; Elsevier Science; Itc Journal; 28; 5-2014; 181-192
0303-2434
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0303243413001748
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.jag.2013.12.006
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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