LAUSR

Abstract Digital soil mapping (DSM) is the most widely used method for producing spatial information of soil organic matter (SOM). Accuracy of the information is generally determined by modelling methods and sample sizes used for DSM. However, different studies present different importance of modelling method and sample size on accuracy of DSM, while they do not explore various combinations of modelling method and sample size. Based on the studies, it is supposed that there exists an optimal combination of modelling method and sample size for producing information of SOM accurately and economically. With SOM data of 1861 soil samples collected in Guangdong, China, the present study first assessed importance of modelling method and sample size and then examined if an optimal combination of modelling method and sample size existed for the area. Six modelling methods were explored, while 12 sample sizes were used, ranging from 100 to 1200 with an interval of 100. For each size, 10 repeated samples were randomly taken from a data of 1311 samples which were randomly selected from all the 1861 soil samples based on the probability distribution of the SOM data. The results showed that, for small sample sizes, the modelling methods have a greater impact on accuracy of DSM. However, for large sample sizes, e.g., more than 1000, the sample sizes have a much greater impact. Due to the varying importance of modelling method and sample size, there exists an optimal combination of modelling method and sample size for spatial prediction of SOM in the area, i.e., the combination of regression kriging and a sample size of 800. Thus, for economically producing detailed and accurate information on spatial distribution of SOM, it is recommended that a series of modelling methods and sample sizes are tried to identify an optimal combination of modelling method and sample size.