Field sampling is an important way of collecting soil information for the modeling and evaluation steps during digital soil mapping (DSM). However, some predesigned samples may not be accessible in the field due to natural or anthropogenic reasons. Simply abandoning the inaccessible samples or casually selecting substitutes from other locations may affect the quality of the corresponding DSM. To address this issue, we propose a new method of dynamically recommending substitute locations for inaccessible samples, which was implemented in a prototype system on a smart phone platform. The proposed method takes into concern the original sampling strategy and recommends substitute sample locations based on a measure of suitability index. The suitability index is calculated to incorporate a substitutive degree as well as the sampling cost involved. The substitutive degree depicts to what extent a substitute location may replace the original sample in the context of soil mapping, while the sampling cost characterizes the travel expense to the substitute location following the overall fieldwork route arrangements. The proposed method currently supports four commonly used sampling strategies, i.e., simple random sampling, stratified random sampling, grid sampling, and purposive sampling based on environmental similarity. Two substitute sampling scenarios, instant sampling and subsequent sampling, are considered by the proposed method, to adapt to surveyors’ actual field sampling route arrangements when estimating the accessibility and sampling cost of potential substitute locations. Monte Carlo simulation experiments in a study area (about 5800 km2) located in Anhui province of China were conducted to use the proposed method to recommend substitute locations for two modeling sample sets designed based on purposive sampling strategy and stratified random sampling strategy respectively (59 points for each set) from other 224 previously obtained samples. Experimental results evaluated based on 57 independent evaluation samples showed that the proposed method was able to recommend substitute locations without affecting the performance of DSM, when less than 10% samples were replaced by substitute samples. A subsequent sampling scenario was revealed to incur lower sampling cost than an instant sampling scenario.