Abstract
Sampling design plays a critical role in farm-level digital soil mapping (DSM). In many cases, a soil mapping model may not have been decided upon at the sample design stage. Design-based sampling may be more appropriate than model-based sampling because it is independent of subsequent soil mapping models. However, existing sampling methods optimize the sample size and locations in geographical space or feature space without considering the impacts of environmental similarity in local geographical space. In this paper, a novel sampling design method based on local environmental similarity was developed. Image segmentation was introduced into the sampling design by partitioning agricultural soil into subregions with good spatial continuity, within-region homogeneity, and between-region heterogeneity to determine the optimal sample size and locations. First, the environmental similarity between adjacent soils was calculated. Second, the merging process was iteratively conducted, and a series of segmentations was generated. Finally, the optimal sample size and locations were determined based on the optimal segmentation results. To validate the proposed method, it was compared with stratified random sampling, k-means sampling, and spatially balanced sampling methods. Two mapping models, ordinary kriging and sandwich estimation, were employed to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus, and available potassium. These comparative experiments showed that the proposed method had better potential to generate farm-level muti-soil property mapping results with good accuracy than the competing sampling methods. In conclusion, consideration of local environmental similarity and the use of image segmentation for soil sampling were helpful in determining the optimal sample size and key sample locations.
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Acknowledgements
This work was supported by the Joint Project of Science and Technology Research and Development Plan in Henan Province (project number: 222103810012), Key Scientific Research Projects of Colleges and Universities in Henan Province (project number: 22A170021), and Science and Technology Research and Development Projects in Henan Province (project number: 232102111100). The authors would like to thank the reviewers and editors for valuable advice and comments.
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Wang, Y., Qi, Q., Wang, J. et al. The potential of image segmentation applied to sampling design for improving farm-level multi-soil property mapping accuracy. Precision Agric 24, 2350–2373 (2023). https://doi.org/10.1007/s11119-023-10043-7
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DOI: https://doi.org/10.1007/s11119-023-10043-7