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

Autores
Xiao-Peng, Song; Potapov, Peter V.; Krylov, Alexander; King, LeeAnn; 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.
Inst. de Clima y Agua
Fil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Krylov, Alexander. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: King, LeeAnn. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Hudson, Amy. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Khan, Ahmad. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Adusei, Bernard. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Stehman, Stephen V. Department of Forest and Natural Resources Management; Estados Unidos
Fil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fuente
Remote sensing of environment 190 : 383-395. (2017)
Materia
Reconocimiento de Suelos
Imágenes por Satélites
Soja
Satellite Imagery
Soybeans
Soil Surveys
Estados Unidos
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/840

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oai_identifier_str oai:localhost:20.500.12123/840
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spelling National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field surveyXiao-Peng, SongPotapov, Peter V.Krylov, AlexanderKing, LeeAnnDi Bella, Carlos MarceloHudson, AmyKhan, AhmadAdusei, BernardStehman, Stephen V.Hansen, Matthew C.Reconocimiento de SuelosImágenes por SatélitesSojaSatellite ImagerySoybeansSoil SurveysEstados UnidosReliable 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.Inst. de Clima y AguaFil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Krylov, Alexander. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: King, LeeAnn. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Hudson, Amy. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Khan, Ahmad. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Adusei, Bernard. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Stehman, Stephen V. Department of Forest and Natural Resources Management; Estados UnidosFil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; Estados Unidos2017-07-28T18:26:26Z2017-07-28T18:26:26Z2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/840http://www.sciencedirect.com/science/article/pii/S0034425717300081?via%3Dihub0034-4257https://doi.org/10.1016/j.rse.2017.01.008Remote sensing of environment 190 : 383-395. (2017)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:44:08Zoai:localhost:20.500.12123/840instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:08.471INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Xiao-Peng, Song
Reconocimiento de Suelos
Imágenes por Satélites
Soja
Satellite Imagery
Soybeans
Soil Surveys
Estados Unidos
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 Xiao-Peng, Song
Potapov, Peter V.
Krylov, Alexander
King, LeeAnn
Di Bella, Carlos Marcelo
Hudson, Amy
Khan, Ahmad
Adusei, Bernard
Stehman, Stephen V.
Hansen, Matthew C.
author Xiao-Peng, Song
author_facet Xiao-Peng, Song
Potapov, Peter V.
Krylov, Alexander
King, LeeAnn
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, LeeAnn
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 Reconocimiento de Suelos
Imágenes por Satélites
Soja
Satellite Imagery
Soybeans
Soil Surveys
Estados Unidos
topic Reconocimiento de Suelos
Imágenes por Satélites
Soja
Satellite Imagery
Soybeans
Soil Surveys
Estados Unidos
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.
Inst. de Clima y Agua
Fil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Krylov, Alexander. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: King, LeeAnn. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Hudson, Amy. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Khan, Ahmad. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Adusei, Bernard. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Stehman, Stephen V. Department of Forest and Natural Resources Management; Estados Unidos
Fil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; 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 2017-07-28T18:26:26Z
2017-07-28T18:26:26Z
2017
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/20.500.12123/840
http://www.sciencedirect.com/science/article/pii/S0034425717300081?via%3Dihub
0034-4257
https://doi.org/10.1016/j.rse.2017.01.008
url http://hdl.handle.net/20.500.12123/840
http://www.sciencedirect.com/science/article/pii/S0034425717300081?via%3Dihub
https://doi.org/10.1016/j.rse.2017.01.008
identifier_str_mv 0034-4257
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Remote sensing of environment 190 : 383-395. (2017)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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