National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey

Autores
Song, Xiao Peng; Potapov, Peter V.; Krylov, Alexander; King, Lee Ann; Di Bella, Carlos Marcelo; Hudson, Amy; Khan, Ahmad; Adusei, Bernard; Stehman, Stephen V.; Hansen, Matthew C.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be applied to other regions and potentially other crops in an operational mode.
Fil: Song, Xiao Peng. University of Maryland; Estados Unidos
Fil: Potapov, Peter V.. University of Maryland; Estados Unidos
Fil: Krylov, Alexander. University of Maryland; Estados Unidos
Fil: King, Lee Ann. University of Maryland; Estados Unidos
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hudson, Amy. University of Maryland; Estados Unidos
Fil: Khan, Ahmad. University of Maryland; Estados Unidos
Fil: Adusei, Bernard. University of Maryland; Estados Unidos
Fil: Stehman, Stephen V.. State University of New York; Estados Unidos
Fil: Hansen, Matthew C.. University of Maryland; Estados Unidos
Materia
Agriculture
Classification
Cropland
Decision Tree
Image Time-Series
Landsat
Remote Sensing
Sample
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:11336/72783

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network_name_str CONICET Digital (CONICET)
spelling National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field surveySong, Xiao PengPotapov, Peter V.Krylov, AlexanderKing, Lee AnnDi Bella, Carlos MarceloHudson, AmyKhan, AhmadAdusei, BernardStehman, Stephen V.Hansen, Matthew C.AgricultureClassificationCroplandDecision TreeImage Time-SeriesLandsatRemote SensingSamplehttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be applied to other regions and potentially other crops in an operational mode.Fil: Song, Xiao Peng. University of Maryland; Estados UnidosFil: Potapov, Peter V.. University of Maryland; Estados UnidosFil: Krylov, Alexander. University of Maryland; Estados UnidosFil: King, Lee Ann. University of Maryland; Estados UnidosFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hudson, Amy. University of Maryland; Estados UnidosFil: Khan, Ahmad. University of Maryland; Estados UnidosFil: Adusei, Bernard. University of Maryland; Estados UnidosFil: Stehman, Stephen V.. State University of New York; Estados UnidosFil: Hansen, Matthew C.. University of Maryland; Estados UnidosElsevier Science Inc2017-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/72783Song, Xiao Peng; Potapov, Peter V.; Krylov, Alexander; King, Lee Ann; Di Bella, Carlos Marcelo; et al.; National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey; Elsevier Science Inc; Remote Sensing of Environment; 190; 3-2017; 383-3950034-4257CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2017.01.008info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0034425717300081info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:25Zoai:ri.conicet.gov.ar:11336/72783instacron: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 09:44:25.801CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
title National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
spellingShingle National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
Song, Xiao Peng
Agriculture
Classification
Cropland
Decision Tree
Image Time-Series
Landsat
Remote Sensing
Sample
title_short National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
title_full National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
title_fullStr National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
title_full_unstemmed National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
title_sort National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
dc.creator.none.fl_str_mv Song, Xiao Peng
Potapov, Peter V.
Krylov, Alexander
King, Lee Ann
Di Bella, Carlos Marcelo
Hudson, Amy
Khan, Ahmad
Adusei, Bernard
Stehman, Stephen V.
Hansen, Matthew C.
author Song, Xiao Peng
author_facet Song, Xiao Peng
Potapov, Peter V.
Krylov, Alexander
King, Lee Ann
Di Bella, Carlos Marcelo
Hudson, Amy
Khan, Ahmad
Adusei, Bernard
Stehman, Stephen V.
Hansen, Matthew C.
author_role author
author2 Potapov, Peter V.
Krylov, Alexander
King, Lee Ann
Di Bella, Carlos Marcelo
Hudson, Amy
Khan, Ahmad
Adusei, Bernard
Stehman, Stephen V.
Hansen, Matthew C.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Agriculture
Classification
Cropland
Decision Tree
Image Time-Series
Landsat
Remote Sensing
Sample
topic Agriculture
Classification
Cropland
Decision Tree
Image Time-Series
Landsat
Remote Sensing
Sample
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be applied to other regions and potentially other crops in an operational mode.
Fil: Song, Xiao Peng. University of Maryland; Estados Unidos
Fil: Potapov, Peter V.. University of Maryland; Estados Unidos
Fil: Krylov, Alexander. University of Maryland; Estados Unidos
Fil: King, Lee Ann. University of Maryland; Estados Unidos
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hudson, Amy. University of Maryland; Estados Unidos
Fil: Khan, Ahmad. University of Maryland; Estados Unidos
Fil: Adusei, Bernard. University of Maryland; Estados Unidos
Fil: Stehman, Stephen V.. State University of New York; Estados Unidos
Fil: Hansen, Matthew C.. University of Maryland; Estados Unidos
description Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be applied to other regions and potentially other crops in an operational mode.
publishDate 2017
dc.date.none.fl_str_mv 2017-03
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/11336/72783
Song, Xiao Peng; Potapov, Peter V.; Krylov, Alexander; King, Lee Ann; Di Bella, Carlos Marcelo; et al.; National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey; Elsevier Science Inc; Remote Sensing of Environment; 190; 3-2017; 383-395
0034-4257
CONICET Digital
CONICET
url http://hdl.handle.net/11336/72783
identifier_str_mv Song, Xiao Peng; Potapov, Peter V.; Krylov, Alexander; King, Lee Ann; Di Bella, Carlos Marcelo; et al.; National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey; Elsevier Science Inc; Remote Sensing of Environment; 190; 3-2017; 383-395
0034-4257
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2017.01.008
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0034425717300081
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
dc.publisher.none.fl_str_mv Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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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