Abstract:Sampling design has long been a key issue of concerns in the study of Soil Geography. How to take samples efficiently is an important problem that investigators or researchers are tackling. As more and more synergic environmental data become easily available, they can also be used to assist designing of sampling so as to improve sampling efficiency. For that end, a multi-grade representative sampling method is developed. Using this method, investigators can catch patterns of soil spatial variability at different scales through designing different representative grades based on the relationship between soil and its environmental covariates. This method has been testified as an effective sampling method in a case study of watershed scale soil mapping. However, the application of this method is very limited, especially for mapping regional soil with more complicated pedogenesis. It is also unknown how it is when comparing with classic sampling methods, such as stratified random sampling. In this paper the multi-grade representative sampling method is compared with the stratified random sampling method in regional scaled soil property mapping, in the following two aspects: 1) designing of sampling sites, and result and accuracy of mapping; and 2) variation of accuracy of mapping with increased number of sampling sites. The case study was laid out in Xuancheng of Anhui Province, China, covering an area of 5900 km2.Seven environmental variables were selected, i.e. parent material (rock), slope gradient, profile curvature, contour curvature, topographic wetness index, annual average precipitation, and annual average temperature. The data of parent rock was used to stratify the study area. For representative sampling, FCM was employed to cluster the environmental variables of each stratum for designing representative sampling sites. Consequently a total of 59 sampling sites featuring three representative grades were defined. For random sampling, the number of sampling sites in each stratum was proportional to its area and also a total of 59 sampling sites were marked out. The two sets of sampling sites were sorted separately into three groups, each consisting of 46, 58 and 59 sites. For representative sampling, the first group of sampling sites was the highest in representativeness grade. The second group was the first group adding the sampling sites with the second highest representative grade. And the third group was the total sample set. For random sampling, the first group of sampling sites was 46 sampling sites picked out randomly from the set. The second group was the first group adding 12 sampling sites randomly drawn from the remaining (59-46) samples. And the third group was the total sample set.Soil surface layer sand content maps were plotted using two different soil mapping methods, i.e. multivariate linear regression and similarity-based mapping. The resultant soil maps were evaluated with independent validation sites and RMSE as evaluation index. Results show: 1) no matter which mapping method was used, in the case of the same sampling size, the representative sampling method was lower than the random sampling method in RMSE and even the former with fewer sampling sites (46) was lower than the latter with more sampling sites (59); 2)with increased number of sampling sites that were lower in representativeness, RMSE displayed a basically declining trend, while in random sampling method, increased number of sampling sites would aggravate the fluctuation of RMSE. It is, therefore, held that the multi-grade representative sampling method is more efficient and stable than the stratified random sampling.