Soil Sampling DesignReferences
Brus, D.J. 2022. Spatial sampling with R. 1st edition. Boca Raton, Florida, Chapman; Hall/CRC. (also available at https://dickbrus.github.io/SpatialSamplingwithR/).
Goodbody, T.R., Coops, N.C. & Queinnec, M. 2023. Structurally guided sampling. (also available at https://cran.r-project.org/package=sgsR).
Goodbody, T.R.H., Coops, N.C., Queinnec, M., White, J.C., Tompalski, P., Hudak, A.T., Auty, D., Valbuena, R., LeBoeuf, A., Sinclair, I., McCartney, G., Prieur, J.-F. & Woods, M.E. 2023. sgsR: A structurally guided sampling toolbox for LiDAR-based forest inventories.
Malone, B.P., Minansy, B. & Brungard, C. 2019. Some methods to improve the utility of conditioned latin hypercube sampling. PeerJ, 7: e6451. https://doi.org/10.7717/peerj.6451
Minasny, B. & McBratney, A. 2006. A conditioned latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32: 1378–1388. https://doi.org/10.1016/j.cageo.2005.12.009
Roudier, P., Brugnard, C., Beaudette, D., Louis, B., Daust, K. & Clifford, D. 2011. Clhs: A r package for conditioned latin hypercube sampling. (also available at https://cran.r-project.org/web/packages/clhs/).