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dc.contributor.author
Song, Xiao Peng
dc.contributor.author
Potapov, Peter V.
dc.contributor.author
Krylov, Alexander
dc.contributor.author
King, Lee Ann
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Di Bella, Carlos Marcelo
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Hudson, Amy
dc.contributor.author
Khan, Ahmad
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Adusei, Bernard
dc.contributor.author
Stehman, Stephen V.
dc.contributor.author
Hansen, Matthew C.
dc.date.available
2019-03-28T22:13:37Z
dc.date.issued
2017-03
dc.identifier.citation
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
dc.identifier.issn
0034-4257
dc.identifier.uri
http://hdl.handle.net/11336/72783
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science Inc
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Agriculture
dc.subject
Classification
dc.subject
Cropland
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Decision Tree
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Image Time-Series
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Landsat
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Remote Sensing
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Sample
dc.subject.classification
Agricultura
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Agricultura, Silvicultura y Pesca
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CIENCIAS AGRÍCOLAS
dc.title
National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-03-26T14:08:44Z
dc.journal.volume
190
dc.journal.pagination
383-395
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Song, Xiao Peng. University of Maryland; Estados Unidos
dc.description.fil
Fil: Potapov, Peter V.. University of Maryland; Estados Unidos
dc.description.fil
Fil: Krylov, Alexander. University of Maryland; Estados Unidos
dc.description.fil
Fil: King, Lee Ann. University of Maryland; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Hudson, Amy. University of Maryland; Estados Unidos
dc.description.fil
Fil: Khan, Ahmad. University of Maryland; Estados Unidos
dc.description.fil
Fil: Adusei, Bernard. University of Maryland; Estados Unidos
dc.description.fil
Fil: Stehman, Stephen V.. State University of New York; Estados Unidos
dc.description.fil
Fil: Hansen, Matthew C.. University of Maryland; Estados Unidos
dc.journal.title
Remote Sensing of Environment
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.rse.2017.01.008
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0034425717300081
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