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

Sensors

Qinghan Dong¹  Limin Wang²  Zhongxin Chen²  Jia Liu²  Javier Gallego³ 

[1] Department of Remote Sensing, Flemish Institute of Technological Research, 2400 Mol, Belgium;Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;Joint Research Centre, The European Commission, 21027 Ispra, Italy;

关键词: crop area;    remote sensing image classification;    area frame sampling;    stratification;    regression estimator;   

DOI  :  10.3390/s17112638

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown