对比景观土壤性质普通克里格法和回归克里格法的比较研究

The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size, spatial structure, and auxiliary variables (terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes (agricultural vs. forested). When spatial structure could not be well captured by point-based observations (e. g., when the ratio of sample spacing over correlation range was > 0.5), or when a strong relationship existed between target soil properties and auxiliary variables (e. g., their R² was > 0.6), regression kriging (RK) was more accurate for interpolating soil properties in both landscapes studied. Otherwise, ordinary kriging (OK) was better. Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation, because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables. Because of a smaller ratio of elevation change over total study area (E/A = 1.2) and multiple parent materials in the agricultural land, OK was generally more accurate in that landscape. In contrast, a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment. The results from this study can be useful for selecting kriging for various soil properties and landscapes.

Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes

The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size, spatial structure, and auxiliary variables (terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes (agricultural vs. forested). When spatial structure could not be well captured by point-based observations (e.g., when the ratio of sample spacing over correlation range was > 0.5), or when a strong relationship existed between target soil properties and auxiliary variables (e.g., their R2 was > 0.6), regression kriging (RK) was more accurate for interpolating soil properties in both the landscapes studied. Otherwise, ordinary kriging (OK) was better. Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation, because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables. Because of a smaller ratio of elevation change over total study area (E/A = 1.2) and multiple parent materials in the agricultural land, OK was generally more accurate in that landscape. In contrast, a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment. The results from this study can be useful for selecting kriging for various soil properties and landscapes.