Related references
Note: Only part of the references are listed.Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps
Tianwu Ma et al.
GEODERMA (2020)
Sampling for digital soil mapping: A tutorial supported by R scripts
D. J. Brus
GEODERMA (2019)
Efficient sampling for geostatistical surveys
Alexandre M. J. -C. Wadoux et al.
EUROPEAN JOURNAL OF SOIL SCIENCE (2019)
Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon
Juan Contreras et al.
INTERNATIONAL JOURNAL OF CLIMATOLOGY (2019)
Sampling design optimization for soil mapping with random forest
Alexandre M. J-C. Wadoux et al.
GEODERMA (2019)
How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?
R. M. Lark et al.
GEODERMA (2018)
Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale
Marisa Beatriz Domenech et al.
GEODERMA (2017)
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C plus plus and R
Marvin N. Wright et al.
JOURNAL OF STATISTICAL SOFTWARE (2017)
Digital soil mapping across the globe
Dominique Arrouays et al.
GEODERMA REGIONAL (2017)
Prediction of Soil Properties at Farm Scale Using a Model-Based Soil Sampling Scheme and Random Forest
Mauricio Castro-Franco et al.
SOIL SCIENCE (2015)
A comparison of calibration sampling schemes at the field scale
K. Schmidt et al.
GEODERMA (2014)
Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling
Kevin L. Werbylo et al.
JOURNAL OF HYDROLOGY (2014)
A Comparison of Three Field Sampling Methods to Estimate Soil Carbon Content
Luke Worsham et al.
FOREST SCIENCE (2012)
Sampling for validation of digital soil maps
D. J. Brus et al.
EUROPEAN JOURNAL OF SOIL SCIENCE (2011)
Delineating the hazard zone of multiple soil pollutants by multivariate indicator kriging and conditioned Latin hypercube sampling
Hone-Jay Chu et al.
GEODERMA (2010)
Optimization of sample patterns for universal kriging of environmental variables
Dick J. Brus et al.
GEODERMA (2007)
Optimized sample schemes for geostatistical surveys
B. P. Marchant et al.
MATHEMATICAL GEOLOGY (2007)
A conditioned Latin hypercube method for sampling in the presence of ancillary information
Budiman Minasny et al.
COMPUTERS & GEOSCIENCES (2006)
The influence of variogram parameters on optimal sampling schemes for mapping by kriging
JW van Groenigen
GEODERMA (2000)