Publications

Wang, D; Chen, ZX; Zhou, QB; Liu, J (2016). Comparison of spatial sampling schemes for crop sown acreage estimation in large-scale inventories. 2016 FIFTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS), 317-322.

Abstract
Timely and accurate estimating crop sown acreage is one of key technologies of crop yield monitoring by remote sensing, it has become an important subject in national agricultural condition monitoring field. In view of the problems occurred in the current sampling survey technology system in China, such as the sample size is not scientifically formulated; the rationality of sample layout is deficient due to that the spatial correlation and variability of the sampling units have not been quantitatively estimated; the application suitability of the spatial sampling methods has not been evaluated, owing to the lack of comparisons of various sampling schemes efficiencies; the spatial correlation between sampling units has been not considered in the process of population extrapolation and error analysis, consequently, which leads to that the sampling errors are overestimated or underestimated. The study's aims are to make a comparison on the efficiencies of different spatial sampling schemes at a large-scale area by combining "3S" technology (Remote Sensing, Geographic Information Systems and Global Positioning systems), Geostatistics theory and traditional sampling methods, in order to further improve the current efficiency of spatial sampling survey for crop acreage estimation. Shandong Province, China is chosen as the study area and winter wheat sown area as the study object. The basic geographic information data (1: 250000, province and county boundaries), land use data (1: 250000) in 2010, the spatial distribution data of winter wheat (derived by MODIS image, the spatial resolution is 250m) in 2010 and the winter wheat planting regionalization data of the study area are used in this paper. 13 spatial sampling schemes (simple random sampling, spatial random sampling, traditional systematic sampling, spatial systematic sampling, traditional stratified sampling and spatial stratified sampling et al) are designed to draw samples, then extrapolate population values and estimate the sampling error. The sample size is calculated based on the population mean and variance estimated by MODIS Image. The sample observations (that is the winter wheat sown area in a sampled unit) are obtained by TM or SPOT images. The relative error, coefficient of variation (CV) and sample size are selected as the evaluation indicator of sampling efficiency. The experimental results demonstrate that, the efficiency of spatial stratified sampling scheme (the stratification criterion is the ratio that the winter wheat area accounts for that of a sampling unit, and sampling interval is the spatial correlation threshold of sampling units) is the maximum among 13 sampling schemes, the corresponding relative error, CV and sample size are 0.76%, 2.42% and 32, respectively. Sorted in descending order, the rest of sampling schemes are traditional stratified sampling, traditional systematic sampling, spatial systematic sampling, spatial random sampling and simple random sampling. In this way, this research can provide a theoretical basis for improving the efficiency of spatial sampling survey for crop sown acreage estimation.

DOI:

ISSN:
2334-3168