Sampling Design Optimization Based on Soil-Land Inference Model (SoLIM)



Soil is an essential source of ecosystem services such as food production and climate regulation. Soil information is of fundamental importance for decision making on adequate land use planning and management and environmental protection which is in fact the motivation behind soil surveys.  One of the majors concerns in soil science relies on the fact that the conventional methods of soil analysis are too expensive and time-consuming, and soil legacy databases are often not adequate for assessing and mapping the soil condition. In this sense, producing relevant soil information for improving the current soil legacy databases is one of the big goals of soil sensing and digital soil mapping. Classical sampling methods (e.g. simple random sampling, systematic sampling and stratified sampling) as well as the model-based sampling strategy require a large number of samples to account for the spatial variation of environmental variables. As sampling is constrained by financial resources, efficient sampling strategies are desirable. In this paper, we are focusing on sampling design optimization based on Soil-Land Inference Model (SoLIM) with respect to environmental covariates (soil and terrain attributes).


sampling strategy, soil mapping, terrain characteristics, Soil-Land Inference Model.

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