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

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...

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Main Authors: Qinghan Dong, Jia Liu, Limin Wang, Zhongxin Chen, Javier Gallego
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/17/11/2638
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spelling doaj-ab65573533df4f1f9096f2674f0b82b42020-11-24T21:53:03ZengMDPI AGSensors1424-82202017-11-011711263810.3390/s17112638s17112638Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression EstimatorQinghan Dong0Jia Liu1Limin Wang2Zhongxin Chen3Javier Gallego4Department of Remote Sensing, Flemish Institute of Technological Research, 2400 Mol, BelgiumInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaJoint Research Centre, The European Commission, 21027 Ispra, ItalyImage 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.https://www.mdpi.com/1424-8220/17/11/2638crop arearemote sensing image classificationarea frame samplingstratificationregression estimator
collection DOAJ
language English
format Article
sources DOAJ
author Qinghan Dong
Jia Liu
Limin Wang
Zhongxin Chen
Javier Gallego
spellingShingle Qinghan Dong
Jia Liu
Limin Wang
Zhongxin Chen
Javier Gallego
Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
Sensors
crop area
remote sensing image classification
area frame sampling
stratification
regression estimator
author_facet Qinghan Dong
Jia Liu
Limin Wang
Zhongxin Chen
Javier Gallego
author_sort Qinghan Dong
title Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
title_short Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
title_full Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
title_fullStr Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
title_full_unstemmed Estimating Crop Area at County Level on the North China Plain with an Indirect Sampling of Segments and an Adapted Regression Estimator
title_sort estimating crop area at county level on the north china plain with an indirect sampling of segments and an adapted regression estimator
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-11-01
description 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.
topic crop area
remote sensing image classification
area frame sampling
stratification
regression estimator
url https://www.mdpi.com/1424-8220/17/11/2638
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