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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/72783
Ver los metadatos del registro completo
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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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1844613398082355200 |
score |
13.070432 |