Spatial Autocorrelation of Winter Wheat in Sampling Units and its Effect on Sampling Efficiency

Field: Earth and Planetary Sciences (Physical Sciences)

An effective way to obtain large areas of crop area information is the space sampling method constructed by combining remote sensing data with traditional sampling methods. However, the traditional sampling method requires that the sampling units should satisfy the principle of mutual independence, and does not take into account that regional crops are spatially attributed to spatial autocorrelation due to natural conditions, socioeconomic factors, and other factors. In the past, it has not been reported whether the spatial autocorrelation has influence on the sampling efficiency of agricultural crops and how it affects the extent of impact, thus limiting the further improvement of the spatial sampling efficiency of crop acreage. In response to this problem, this study selected Fengtai County of Anhui Province as a research area. Through the combination of remote sensing data, spatial analysis and traditional sampling methods, three spatial sampling schemes (simple, systematic, and stratified sampling) were designed, 10 sampling unit scale levels, and the spatial autocorrelation of winter wheat area in different sampling units is quantitatively evaluated using the global spatial autocorrelation index (Moran's I); based on different sampling unit scales, three kinds of spatial sampling schemes are used to conduct sample selection, overall extrapolation and error estimation; the overall relative error (r) of the sampling extrapolation, the coefficient of variation (CV) of the total value estimate and the sample size (n) were selected as the evaluation index of the sampling efficiency to quantitatively evaluate the efficiency of the three spatial sampling schemes. The research results show that the winter wheat planting area spatial autocorrelation gradually decreases with the increase of sampling unit size in the sampling unit, but it still shows a strong spatial autocorrelation, The variation range of Z-Score is indicating that the winter wheat in the study area shows significant aggregation characteristics at different sampling unit sizes in the study area. Using 3 sampling methods to extrapolate the total population of cells at different sampling unit sizes, compared with the same sampling, the relative sampling error in the winter wheat area estimation first increases and then decreases with the increase of sampling unit size (decreased spatial autocorrelation), 1) For each method, the sampling error average under the four sampling fractions is conducted, and the average sampling error reaches the lowest point at 2000m × 2000m, the variation coefficient increases with the size of the sampling unit. When the sampling unit size is controlled within 2000 m×2000 m, the average variation coefficient basically controlled within 15%. 2) Relative sampling error of the crop area estimation, variation coefficient, and average relative sampling error and average variation coefficient are all presented as stratified sampling was the smallest, followed by systematic sampling, and simple random sampling was the largest, where the mean variation coefficient of average sampling relative to stratified sampling has the smallest change in average variation coefficient. 3) When the sampling method is determined, the relative sampling error of the estimated crop area decreases as the sampling ratio increases. When the fraction reaches 5%, the sampling error amplitude is basically stable within 7%, the variation coefficient is stable within 5%, and within the 2000 m×2000 m sampling unit scale, continuing to increase sampling ratio has little effect on sampling error and variation coefficient. 4) Through the above three kinds of evaluation indicators, a quantitative sampling extrapolation efficiency evaluation model, spatial autocorrelation and sampling schemes are obtained. This study can provide a reference for rationally designing spatial sampling efficiency of crops in the presence of spatial autocorrelation.