A multi-resolution approach to national-scale cultivated area estimation of soybean

Autores
King, LeeAnn; Adusei, Bernard; Stehman, Stephen V.; Potapov, Peter V.; Xiao-Peng, Song; Krylov, Alexander; Di Bella, Carlos Marcelo; Loveland, Thomas R.; Johnson, David M.; Hansen, Matthew C.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Satellite remote sensing data can provide timely, accurate, and objective information on cultivated area by crop type and, in turn, facilitate accurate estimates of crop production. Here, we present a generic multi-resolution approach to sample-based crop type area estimation at the national level using soybean as an example crop type. Historical MODIS (MODerate resolution Imaging Spectroradiometer) data were used to stratify growing regions into subsets of low, medium and high soybean cover. A stratified random sample of 20 km × 20 km sample blocks was selected and Landsat data for these sample blocks classified into soybean cover. The Landsat-derived soybean area was used to produce national estimates of soybean area. Current year MODIS-indicated soybean cover served as an auxiliary variable in a stratified regression estimator procedure. To evaluate the approach, we prototyped the method in the USA, where the 2013 USDA Cropland Data Layer (CDL) was used as a reference training data set for mapping soybean cover within each sample block. Three individual Landsat images were sufficient to accurately map soybean cover for all blocks, revealing that a rather sparse sample of phenological variation is needed to separate soybean from other cover types. In addition to stacks of images, we also evaluated standard radiometrically normalized Landsat inputs for mapping blocks individually (local-scale) and all at once (national-scale). All tested inputs resulted in area estimates comparable to the official USDA estimate of 30.86 Mha, with lower accuracy and higher standard error for national-scale mapping implementations. The stratified regression estimator incorporating current year MODIS-indicated soy reduced the standard error of the estimated soybean area by over 25% relative to the standard error of the stratified estimator. Finally, the method was ported to Argentina. A stratified random sample of blocks was characterized for soybean cultivated area using stacks of individual Landsat images for the 2013–2014 southern hemisphere growing season. A sub-sample of these blocks was visited on the ground to assess the accuracy of the Landsat-derived soy classification. The stratified regression estimator procedure performed similarly to the US application as it resulted in a reduction in standard error of about 25% relative to the stratified estimator not incorporating current year MODIS-indicated soybean. Our final estimated soybean area was 28% lower than that reported by the USDA, corresponding to a 20% field-based omission error related to underdeveloped fields. Lessons learned from this study can be ported to other regions of comparable field size and management intensity to assess soybean cultivated area. Results for the USA and Argentina may be viewed and downloaded at http://glad.geog.umd.edu/us-analysis and http://glad.geog.umd.edu/argentina-analysis, respectively.
Inst. de Clima y Agua
Fil: King, LeeAnn. 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: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Krylov, Alexander. 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
Fil: Loveland, Thomas R. United States Geological Survey; Estados Unidos
Fil: Johnson, David M. National Agricultural Statistics Service; Estados Unidos
Fil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fuente
Remote sensing of environment 195 : 13-29. (June 2017)
Materia
Soja
Imágenes por Satélites
Landsat
Soybeans
Satellite Imagery
MODIS
Area Cultivada
Argentina
Estados Unidos
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/1536

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oai_identifier_str oai:localhost:20.500.12123/1536
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network_name_str INTA Digital (INTA)
spelling A multi-resolution approach to national-scale cultivated area estimation of soybeanKing, LeeAnnAdusei, BernardStehman, Stephen V.Potapov, Peter V.Xiao-Peng, SongKrylov, AlexanderDi Bella, Carlos MarceloLoveland, Thomas R.Johnson, David M.Hansen, Matthew C.SojaImágenes por SatélitesLandsatSoybeansSatellite ImageryMODISArea CultivadaArgentinaEstados UnidosSatellite remote sensing data can provide timely, accurate, and objective information on cultivated area by crop type and, in turn, facilitate accurate estimates of crop production. Here, we present a generic multi-resolution approach to sample-based crop type area estimation at the national level using soybean as an example crop type. Historical MODIS (MODerate resolution Imaging Spectroradiometer) data were used to stratify growing regions into subsets of low, medium and high soybean cover. A stratified random sample of 20 km × 20 km sample blocks was selected and Landsat data for these sample blocks classified into soybean cover. The Landsat-derived soybean area was used to produce national estimates of soybean area. Current year MODIS-indicated soybean cover served as an auxiliary variable in a stratified regression estimator procedure. To evaluate the approach, we prototyped the method in the USA, where the 2013 USDA Cropland Data Layer (CDL) was used as a reference training data set for mapping soybean cover within each sample block. Three individual Landsat images were sufficient to accurately map soybean cover for all blocks, revealing that a rather sparse sample of phenological variation is needed to separate soybean from other cover types. In addition to stacks of images, we also evaluated standard radiometrically normalized Landsat inputs for mapping blocks individually (local-scale) and all at once (national-scale). All tested inputs resulted in area estimates comparable to the official USDA estimate of 30.86 Mha, with lower accuracy and higher standard error for national-scale mapping implementations. The stratified regression estimator incorporating current year MODIS-indicated soy reduced the standard error of the estimated soybean area by over 25% relative to the standard error of the stratified estimator. Finally, the method was ported to Argentina. A stratified random sample of blocks was characterized for soybean cultivated area using stacks of individual Landsat images for the 2013–2014 southern hemisphere growing season. A sub-sample of these blocks was visited on the ground to assess the accuracy of the Landsat-derived soy classification. The stratified regression estimator procedure performed similarly to the US application as it resulted in a reduction in standard error of about 25% relative to the stratified estimator not incorporating current year MODIS-indicated soybean. Our final estimated soybean area was 28% lower than that reported by the USDA, corresponding to a 20% field-based omission error related to underdeveloped fields. Lessons learned from this study can be ported to other regions of comparable field size and management intensity to assess soybean cultivated area. Results for the USA and Argentina may be viewed and downloaded at http://glad.geog.umd.edu/us-analysis and http://glad.geog.umd.edu/argentina-analysis, respectively.Inst. de Clima y AguaFil: King, LeeAnn. 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: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Krylov, Alexander. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Loveland, Thomas R. United States Geological Survey; Estados UnidosFil: Johnson, David M. National Agricultural Statistics Service; Estados UnidosFil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; Estados Unidos2017-10-19T13:56:34Z2017-10-19T13:56:34Z2017-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/1536http://www.sciencedirect.com/science/article/pii/S00344257173015050034-4257 (Print)1879-0704 (Online)https://doi.org/10.1016/j.rse.2017.03.047Remote sensing of environment 195 : 13-29. (June 2017)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:44:12Zoai:localhost:20.500.12123/1536instacron: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 13:44:13.206INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv A multi-resolution approach to national-scale cultivated area estimation of soybean
title A multi-resolution approach to national-scale cultivated area estimation of soybean
spellingShingle A multi-resolution approach to national-scale cultivated area estimation of soybean
King, LeeAnn
Soja
Imágenes por Satélites
Landsat
Soybeans
Satellite Imagery
MODIS
Area Cultivada
Argentina
Estados Unidos
title_short A multi-resolution approach to national-scale cultivated area estimation of soybean
title_full A multi-resolution approach to national-scale cultivated area estimation of soybean
title_fullStr A multi-resolution approach to national-scale cultivated area estimation of soybean
title_full_unstemmed A multi-resolution approach to national-scale cultivated area estimation of soybean
title_sort A multi-resolution approach to national-scale cultivated area estimation of soybean
dc.creator.none.fl_str_mv King, LeeAnn
Adusei, Bernard
Stehman, Stephen V.
Potapov, Peter V.
Xiao-Peng, Song
Krylov, Alexander
Di Bella, Carlos Marcelo
Loveland, Thomas R.
Johnson, David M.
Hansen, Matthew C.
author King, LeeAnn
author_facet King, LeeAnn
Adusei, Bernard
Stehman, Stephen V.
Potapov, Peter V.
Xiao-Peng, Song
Krylov, Alexander
Di Bella, Carlos Marcelo
Loveland, Thomas R.
Johnson, David M.
Hansen, Matthew C.
author_role author
author2 Adusei, Bernard
Stehman, Stephen V.
Potapov, Peter V.
Xiao-Peng, Song
Krylov, Alexander
Di Bella, Carlos Marcelo
Loveland, Thomas R.
Johnson, David M.
Hansen, Matthew C.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Soja
Imágenes por Satélites
Landsat
Soybeans
Satellite Imagery
MODIS
Area Cultivada
Argentina
Estados Unidos
topic Soja
Imágenes por Satélites
Landsat
Soybeans
Satellite Imagery
MODIS
Area Cultivada
Argentina
Estados Unidos
dc.description.none.fl_txt_mv Satellite remote sensing data can provide timely, accurate, and objective information on cultivated area by crop type and, in turn, facilitate accurate estimates of crop production. Here, we present a generic multi-resolution approach to sample-based crop type area estimation at the national level using soybean as an example crop type. Historical MODIS (MODerate resolution Imaging Spectroradiometer) data were used to stratify growing regions into subsets of low, medium and high soybean cover. A stratified random sample of 20 km × 20 km sample blocks was selected and Landsat data for these sample blocks classified into soybean cover. The Landsat-derived soybean area was used to produce national estimates of soybean area. Current year MODIS-indicated soybean cover served as an auxiliary variable in a stratified regression estimator procedure. To evaluate the approach, we prototyped the method in the USA, where the 2013 USDA Cropland Data Layer (CDL) was used as a reference training data set for mapping soybean cover within each sample block. Three individual Landsat images were sufficient to accurately map soybean cover for all blocks, revealing that a rather sparse sample of phenological variation is needed to separate soybean from other cover types. In addition to stacks of images, we also evaluated standard radiometrically normalized Landsat inputs for mapping blocks individually (local-scale) and all at once (national-scale). All tested inputs resulted in area estimates comparable to the official USDA estimate of 30.86 Mha, with lower accuracy and higher standard error for national-scale mapping implementations. The stratified regression estimator incorporating current year MODIS-indicated soy reduced the standard error of the estimated soybean area by over 25% relative to the standard error of the stratified estimator. Finally, the method was ported to Argentina. A stratified random sample of blocks was characterized for soybean cultivated area using stacks of individual Landsat images for the 2013–2014 southern hemisphere growing season. A sub-sample of these blocks was visited on the ground to assess the accuracy of the Landsat-derived soy classification. The stratified regression estimator procedure performed similarly to the US application as it resulted in a reduction in standard error of about 25% relative to the stratified estimator not incorporating current year MODIS-indicated soybean. Our final estimated soybean area was 28% lower than that reported by the USDA, corresponding to a 20% field-based omission error related to underdeveloped fields. Lessons learned from this study can be ported to other regions of comparable field size and management intensity to assess soybean cultivated area. Results for the USA and Argentina may be viewed and downloaded at http://glad.geog.umd.edu/us-analysis and http://glad.geog.umd.edu/argentina-analysis, respectively.
Inst. de Clima y Agua
Fil: King, LeeAnn. 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: Potapov, Peter V. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Xiao-Peng, Song. University of Maryland. Department of Geographical Sciences; Estados Unidos
Fil: Krylov, Alexander. 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
Fil: Loveland, Thomas R. United States Geological Survey; Estados Unidos
Fil: Johnson, David M. National Agricultural Statistics Service; Estados Unidos
Fil: Hansen, Matthew C. University of Maryland. Department of Geographical Sciences; Estados Unidos
description Satellite remote sensing data can provide timely, accurate, and objective information on cultivated area by crop type and, in turn, facilitate accurate estimates of crop production. Here, we present a generic multi-resolution approach to sample-based crop type area estimation at the national level using soybean as an example crop type. Historical MODIS (MODerate resolution Imaging Spectroradiometer) data were used to stratify growing regions into subsets of low, medium and high soybean cover. A stratified random sample of 20 km × 20 km sample blocks was selected and Landsat data for these sample blocks classified into soybean cover. The Landsat-derived soybean area was used to produce national estimates of soybean area. Current year MODIS-indicated soybean cover served as an auxiliary variable in a stratified regression estimator procedure. To evaluate the approach, we prototyped the method in the USA, where the 2013 USDA Cropland Data Layer (CDL) was used as a reference training data set for mapping soybean cover within each sample block. Three individual Landsat images were sufficient to accurately map soybean cover for all blocks, revealing that a rather sparse sample of phenological variation is needed to separate soybean from other cover types. In addition to stacks of images, we also evaluated standard radiometrically normalized Landsat inputs for mapping blocks individually (local-scale) and all at once (national-scale). All tested inputs resulted in area estimates comparable to the official USDA estimate of 30.86 Mha, with lower accuracy and higher standard error for national-scale mapping implementations. The stratified regression estimator incorporating current year MODIS-indicated soy reduced the standard error of the estimated soybean area by over 25% relative to the standard error of the stratified estimator. Finally, the method was ported to Argentina. A stratified random sample of blocks was characterized for soybean cultivated area using stacks of individual Landsat images for the 2013–2014 southern hemisphere growing season. A sub-sample of these blocks was visited on the ground to assess the accuracy of the Landsat-derived soy classification. The stratified regression estimator procedure performed similarly to the US application as it resulted in a reduction in standard error of about 25% relative to the stratified estimator not incorporating current year MODIS-indicated soybean. Our final estimated soybean area was 28% lower than that reported by the USDA, corresponding to a 20% field-based omission error related to underdeveloped fields. Lessons learned from this study can be ported to other regions of comparable field size and management intensity to assess soybean cultivated area. Results for the USA and Argentina may be viewed and downloaded at http://glad.geog.umd.edu/us-analysis and http://glad.geog.umd.edu/argentina-analysis, respectively.
publishDate 2017
dc.date.none.fl_str_mv 2017-10-19T13:56:34Z
2017-10-19T13:56:34Z
2017-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/1536
http://www.sciencedirect.com/science/article/pii/S0034425717301505
0034-4257 (Print)
1879-0704 (Online)
https://doi.org/10.1016/j.rse.2017.03.047
url http://hdl.handle.net/20.500.12123/1536
http://www.sciencedirect.com/science/article/pii/S0034425717301505
https://doi.org/10.1016/j.rse.2017.03.047
identifier_str_mv 0034-4257 (Print)
1879-0704 (Online)
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 195 : 13-29. (June 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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