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 directly the crop acreage. However this type of assessment often leads to a biased estimation because commission and omission errors generally do not compensate each other. Regression estimators combine remote sensing information with more accurate ground data on a field sample. They can result in more accurate and cost-effective assessment of crop acreage. In this pilot study to produce the 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 from which non-agricultural areas are excluded from ground survey. 202 ground points in the agriculture stratum were surveyed to compute crop statistics. 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 for an operational application at county level in the region.

DONG Qinghan;  LIU Jia;  WANG Limin;  CHEN Zhongxin;  GALLEGO PINILLA Francisco; 

2018-01-15

MDPI AG

JRC109342

1424-8220,   

http://www.mdpi.com/1424-8220/17/11/2638,    https://publications.jrc.ec.europa.eu/repository/handle/JRC109342,   

10.3390/s17112638,