Optimized Designs of Frameworks and Elements in Spatial Sampling for Crop Area Estimation

The experiments were conducted for monitoring crop area by spatial sampling methods in a winter wheat main production region with a size of 42Km×42Km in Hengshui City, Hebei Province in China to optimize designs of the frameworks and elements (sample size, sample-square dimension), based on Remote Sensing (RS) and Geographical Information System (GIS) techniques. 5 sample-square dimension levels (3000m×3000m, 2000m×2000m, 1000m×1000m, 500m×500m and 300m×300m) were selected and 3 sampling techniques (simple random sampling, systematic sampling and stratified sampling) were applied. The experimental results demonstrate that the sampling efficiency by stratified sampling was the maximal (the average relative error was 0.15%, the average sample size varied from 9 to 10); and that of the systematic sampling was inferior (the average relative error varies from 0.74%~2.06%, the average sample size is 229); the sampling efficiency by simple random sampling was the minimum (the average relative error is 2.04%, the average sample size is 229) among 3 sampling techniques. Sampling relative error decreased with sample-square dimension simultaneously. When the sample-square dimension was reduced to a certain extent (the size of sample is 500m×500m), the error was not declining any more. The sampling relative error was the minimum using the sample-square with a size of 500m×500m among 5 sample-square dimension levels.