Optimization of spatial sampling schemes for maize acreage estimation

Di Wang; Qingbo Zhou; Zhongxin Chen; Jia Liu

doi:10.1117/1.JRS.9.097092

26 May 2015 Optimization of spatial sampling schemes for maize acreage estimation

Di Wang, Qingbo Zhou, Zhongxin Chen, Jia Liu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 097092 (May 2015). https://doi.org/10.1117/1.JRS.9.097092

Abstract

Sampling fraction, sample layout, and sampling unit scale are the three basic elements of a spatial sampling scheme. Optimizing these factors plays an important role in decreasing the sampling cost and improving the extrapolation accuracy of survey sampling. Spatial analysis, “3S” techniques, and traditional sampling methods are employed to optimize the three basic elements. Dehui County in Jilin Province, China was chosen as the study area, maize sown acreage as the study object, and square grids as the shape of the sampling units. The experimental results demonstrate that the spatial autocorrelation of sampling unit increases with its scale. When the scale is 500×500 m, there is almost no spatial autocorrelation among sampling units, so 500×500 m is selected as the optimal sampling unit scale. The spatial stratified heterogeneity of sampling units decreases with increasing scale. When the sampling unit scale is 500×500 m, it must be stratified to improve the sampling efficiency. The cultivated land area in one sampling unit can be selected as a stratification criterion due to the significant linear correlation relationship between it and the maize area in all sampling units. Stratified system isometric sampling and 1% are the optimal sample layout pattern and sampling fraction, respectively. This research provides a theoretical basis for improving the spatial sampling efficiency to estimate crop acreage.

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Di Wang, Qingbo Zhou, Zhongxin Chen, and Jia Liu "Optimization of spatial sampling schemes for maize acreage estimation," Journal of Applied Remote Sensing 9(1), 097092 (26 May 2015). https://doi.org/10.1117/1.JRS.9.097092

Published: 26 May 2015

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KEYWORDS

Error analysis

Statistical analysis

Agriculture

Remote sensing

Spatial analysis

Nickel

Spatial resolution

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