Differentiation of soil conditions over low relief areas using feedback dynamic patterns.

In many areas, such as plains and gently undulating terrain, easy-to-measure soil-forming factors such as landform and vegetation do not co-vary with soil conditions across space to the level that they can be effectively used in digital soil mapping. A challenging problem is how to develop a new environmental variable that co-varies with soil spatial variation under these situations. This study examined the idea that change patterns (dynamic feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, can be used to differentiate soil types. To examine this idea, we selected two study areas with different climates: one in northeastern China and the other in northwestern China. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to capture land surface feedback. To measure feedback dynamics, we used spectral information divergence (SID). Results of an independent-samples t-test showed that there was a significant difference in SID values between pixel pairs of the same soil subgroup and those of different subgroups. This indicated that areas with different soil types (subgroup level) exhibited significantly different dynamic feedback patterns, and areas within the same soil type have similar dynamic feedback patterns. It was also found that the more similar the soil types, the more similar the feedback patterns. These findings could lead to the development of a new environmental covariate that could be used to improve the accuracy of soil snapping in low-relief areas.