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Sensors 2017, 17(11), 2638; doi:10.3390/s17112638

Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator

1
Department of Remote Sensing, Flemish Institute of Technological Research, 2400 Mol, Belgium
2
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Joint Research Centre, The European Commission, 21027 Ispra, Italy
*
Author to whom correspondence should be addressed.
Received: 1 September 2017 / Revised: 27 October 2017 / Accepted: 4 November 2017 / Published: 16 November 2017
(This article belongs to the Special Issue Sensors in Agriculture)
View Full-Text   |   Download PDF [4070 KB, uploaded 16 November 2017]   |  

Abstract

Image classifications, including sub-pixel analysis, are often used to estimate crop acreage directly. However, this type of assessment often leads to a biased estimation, because commission and omission errors generally do not compensate for each other. Regression estimators combine remote sensing information with more accurate ground data on a field sample, and can result in more accurate and cost-effective assessments of crop acreage. In this pilot study, which aims to produce crop statistics in Guoyang County, the area frame sampling approach is adapted to a strip-like cropping pattern on the North China Plain. Remote sensing information is also used to perform a stratification in which non-agricultural areas are excluded from the ground survey. In order to compute crop statistics, 202 ground points in the agriculture stratum were surveyed. Image classification was included as an auxiliary variable in the subsequent analysis to obtain a regression estimator. The results of this pilot study showed that the integration of remote sensing information as an auxiliary variable can improve the accuracy of estimation by reducing the variance of the estimates, as well as the cost effectiveness of an operational application at the county level in the region. View Full-Text
Keywords: crop area; remote sensing image classification; area frame sampling; stratification; regression estimator crop area; remote sensing image classification; area frame sampling; stratification; regression estimator
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dong, Q.; Liu, J.; Wang, L.; Chen, Z.; Gallego, J. Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator. Sensors 2017, 17, 2638.

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