An heuristic uncertainty directed field sampling design for digital soil mapping

Abstract

Legacy samples are a valuable data source for digital soil mapping. However, these sample sets are often small in size and ad hoc in spatial distribution. Constrained by the limited representativeness of such a sample set, the obtained soil maps are often incomplete in spatial coverage with "gaps" at the locations which cannot be well represented by these samples. The maps may also contain areas of high prediction uncertainty. In order to extend the predicted area and reduce prediction uncertainty, additional samples are needed. This paper presents a sampling design based on prediction uncertainty to select samples which will effectively complement the sparse and ad hoc samples, and maximize the spatial coverage of prediction and minimize prediction uncertainty. A case study in China shows that this sampling scheme was effective in achieving these goals. Compared with stratified random sampling scheme, when the number of additional samples is the same, the produced map using uncertainty directed samples has larger predicted area, and the accuracy of the produced map is higher than that of the maps using stratified random samples. The finding of this study suggests that prediction uncertainty is a useful indicator to aid field sample selection and to complement the legacy data. Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

“…One hundred and four sampling points in Heshan (with a density of 0.8 km 2 per observation) and 295 sampling points in Xuancheng (with a density of 25 km 2 per observation) were collected separately in (Yang et al, 2013;Zhang et al, 2016;Zhu et al, 2010) (Fig. 1).…”

“…Twenty one transect samples were collected which covered major environmental variations within the shortest distance from ridge top to valley bottom. In Xuancheng, soil samples were collected through five sampling strategies: systematic sampling, integrative hierarchical stepwise sampling, stratified random sampling, a heuristic uncertainty directed field sampling, and subjective sampling by experts' experience (Zhang et al, 2016). Fifty eight samples were collected with 10 km by 10 km grid arrangement based on a systematic sampling strategy.…”

“…Fifty one representative samples were collected using the integrative hierarchical stepwise sampling strategy in Yang et al (2013). Sixty three samples were collected using a heuristic uncertainty directed sampling method in Zhang et al (2016). 58 samples were collected using a stratified random sampling strategy using parent materials as strata (Zhang et al, 2016).…”

“…Sixty three samples were collected using a heuristic uncertainty directed sampling method in Zhang et al (2016). 58 samples were collected using a stratified random sampling strategy using parent materials as strata (Zhang et al, 2016). The subjective sampling was conducted in farm land by the experience of expert with number of 65.…”

Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain

“…Without following any conventional sampling designs (e.g. stratified random sampling, regular sampling), the collected samples do not provide a good spatial coverage of the study area (Webster and Oliver, 1990;Zhang et al, 2016). In spite of this, these existing samples are important resources for DSM.…”