Skip to Main content Skip to Navigation
Conference papers

Brazil Soybean Area Estimation Based on Average Samples Change Rate of Two Years and Official Statistics of a Year Before

Abstract : Comprehensive, reliable and timely information of Brazil’s soybean area is necessary for China to make decisions on agricultural related problems. Spatial sampling method which combined remote sensing and sampling survey is widely used. Due to limitations of width and revisit cycle of medium resolution satellite, This study designed a typical investigation method about Brazil soybean area based on average samples change rate of two years and official statistics of a year before, typical samples were selected to survey, sampling frame was constructed on soybean planting state, the sampling unit was designed as 40 km × 40 km, the sampling proportion was 2 %, average samples change rate of two years were 2013 and 2014. Estimated area was compared with Brazil official harvested area in 2014 (published on 2015 April by Brazilian Institute of Geography and Statistics), the relative error is 2.37 %.
Document type :
Conference papers
Complete list of metadata

Cited literature [7 references]  Display  Hide  Download

https://hal.inria.fr/hal-01557802
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:49:45 PM
Last modification on : Thursday, July 6, 2017 - 3:54:12 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 3:24:30 AM

File

434296_1_En_36_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Kejian Shen, Weifang Li, Zhiyuan Pei, Fei Wang, Xiaoqian Zhang, et al.. Brazil Soybean Area Estimation Based on Average Samples Change Rate of Two Years and Official Statistics of a Year Before. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.366-374, ⟨1010.1007/978-3-319-48357-3_36⟩. ⟨hal-01557802⟩

Share

Metrics

Record views

95

Files downloads

154