Comparing mapping approaches at subcatchment scale in northern Thailand with emphasis on the Maximum Likelihood approach

This study compares different soil mapping approaches in three different petrographic areas in order to test their suitability for regional mapping in northern Thailand. Sampling was based on transects or grid-based randomization. Maps were created based on expert knowledge (eye fitting) or using Classification Tree (CART algorithm) or the Maximum Likelihood approach. In addition, local knowledge-based-soil maps were created. Validation was performed using soil reference maps and independent sampling points. The mapping approaches based on transects and grid-based randomization showed a very high correspondence with the respective reference soil map and a very high degree of matching with independent sampling points. Both methods are best suited for sub-watershed scale. Mapping larger areas is difficult due to the inaccessibility of the mountainous regions. The soil maps based on Maximum Likelihood showed a high correlation with the respective reference soil maps and the individual sampling points. Maximum Likelihood maps and Classification Tree maps showed similar levels of accuracy. The Maximum Likelihood approach is applicable to upscaling procedures; therefore, a calibration area is required which represents the target area. Local knowledge-based-soil mapping is very cheap and fast, but is restricted to village areas where classification often varies even within a village. Despite this, local knowledge is very useful for soil reconnaissance surveys, as well as to acquire an overview of the major distribution of soils and their properties. Upscaling of local knowledge due to its inherent inconsistency is not realistic. the mapping approaches investigated only the Maximum Likelihood approach and the Classification Tree method are suitable for upscaling procedures. Both approaches require a calibration area which can only be mapped by transects or grid-based random sampling. These approaches are more efficient in the case of known ex-ante soil variability, so that the distance for transects, grid cell size, and amount of sampling points can be chosen in a proper way. It is useful to apply local knowledge in order to elevate ex-ante knowledge of soil information. In one pilot study local knowledge-based-soil sampling could reduce the amount of sampling points within a calibration area to less than the half used for transects and randomized mapping. The Maximum Likelihood approach applied afterwards provided an accuracy of at least 70% and hence is as accurate as the Classification Tree method. It seems to be most useful to combine the different approaches.