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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/840
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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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1844619115781685249 |
score |
12.559606 |