Location: Range Management ResearchTitle: Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations
|BEAUDETTE, DYLAN - Natural Resources Conservation Service (NRCS, USDA)|
|Herrick, Jeffrey - Jeff|
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/24/2020
Publication Date: 9/23/2020
Citation: Maynard, J.J., Salley, S.W., Beaudette, D., Herrick, J.E. 2020. Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations. Soil Science Society of America Journal. 2020. https://doi.org/10.1002/saj2.20119.
Interpretive Summary: Accurate information on the soil resource and its effects on land potential are urgently needed by land managers for making informed land-use decisions. Soils are inherently complex systems that exhibit a high degree of spatial variability, making the identification of a soil and its properties at a point-location extremely difficult. Recent developments in smartphone-based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non-soil scientists to sample, characterize, and classify soil profiles. This study evaluated an operational soil classification framework that supports soil identification by citizen scientists using limited, simple soil observations. This study showed that soil sampling by standard depth intervals and the use of a simple set of soil property values resulted in classification accuracies that were only slighlty less than with the use of more complicated sampling and more detailed soil properties. Results from this study support the utility of simple soil observations sampled at fixed depths by citizen scientists for the identification of soils at unknown locations.
Technical Abstract: Accurately identifying the soil map unit component at a specific point-location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone-based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non-soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth-wise variability (i.e., depth-support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert-Plus) representing different levels of detail and three types of depth-support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e.,Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert-Plus dataset (53–60%). This study also showed minimal effect from the type of depth-support used to represent depth-wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.