A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different auxiliary indicators


Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling. However, these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty. To address this lack of error information, this paper introduces a hybrid entropy indicator (HEI). Two conventional indicators, the acreage indicator (AI) and the fragmentation indicator (FI), were also evaluated to compare the results of the three indicators in a homogeneous agricultural area (Pinghu, PH) and a heterogeneous agricultural area (Zhuji, ZJ). The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation (CV) (as low as 1.59%) and also has the highest estimation accuracy with the lowest standard deviation of estimation. For both areas, the performances of HEI and AI are very similar, and better than FI. These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation, while FI is not recommended. Furthermore, the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.


Natural Resources and the Environment

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International Journal of Digital Earth


Taylor & Francis

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