An adaptive uncertainty-guided sampling method for geospatial prediction and its application in digital soil mapping. Issue 2 (1st February 2023)

• Record Type: Journal Article
• Authors: Zhang, Lei; Zhu, A-Xing; Liu, Junzhi; Ma, Tianwu; Yang, Lin; Zhou, Chenghu
• Is Part Of: International journal of geographical information science. Volume 37:Issue 2(2023)
• Page Start: 476
• Page End: 498
• Publication Date: 2023-02-01
• Subjects: Spatial sampling -- prediction uncertainty -- adaptive approach -- spatial prediction -- digital soil mapping; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2125973
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: Sampling design can significantly reduce the uncertainty in geospatial predictions. In this paper, we developed an adaptive uncertainty-guided stepwise sampling (AUGSS) method to select sampling locations to supplement existing legacy sample points whose representation should be improved. The proposed method selects supplemental samples in a stepwise manner as guided by an objective function with two weighted sub-objectives. One reduces the area with high prediction uncertainty, and the other minimizes the overall prediction uncertainty for the entire area. The method takes an adaptive approach to adjust weights for the two sub-objectives and to tune an uncertainty threshold controlling whether a location can be reliably predicted during the sampling procedure. A case study on soil property prediction shows that AUGSS outperforms the stratified random sampling (SRS) and the non-adaptive uncertainty guided sampling method (UGSS) in terms of RMSE and Lin's concordance correlation coefficient with different sample sizes. This study shows that the AUGSS method offers a potential for effectively adding supplemental samples to existing samples which are insufficient for spatial prediction. The adaptive strategy guided by predicted uncertainty provides an efficient support to improve the spatial pattern of samples, which plays a key role in the result accuracy of geospatial predictive mapping.