Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping?
Conditioned Latin Hypercube Sampling 0.9.0 说明说明书 Package‘clhs’October12,2022 Type Package Title Conditioned Latin Hypercube Sampling Version0.9.0 Date2021-10-14 Maintainer Pierre Roudier<***.nz> URL https://github.com/pierreroudier/clhs/ BugReports https://github.com/pierreroudier/clhs/...
This chapter discusses methods for soil sampling that allow calibration of proximal sensor readings to soil properties. Conditioned Latin hypercube sampling (cLHS) was recently proposed as a method for sampling based on covariates obtained from proximal soil sensors. The method provides full coverage of...
Conditioned Latin Hypercube sampling (Minasny and McBratney, 2006) is the most widely used algorithm. After the cLHS design have been firstly presented, it was developed a group of sampling methods based on different modifications of Latin Hypercube sampling (Clifford et al., 2014; Nketia et al...
Conditioned Latin hypercube sampling (cLHS) was recently proposed as a method for sampling based on covariates obtained from proximal soil sensors. The method provides full coverage of the range of each variable by maximally stratifying the marginal distribution. A modification of cLHS was made so ...
Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil mapping. cLHS samples are randomly selected in each stratum of environmental variables, thus the produced sample sets can vary significantly at different runs with the same ...
In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for ...
Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and ...
For conditioned Latin hypercube sampling (cLHS) the problem is: given N sites with ancillary variables (X), select x a sub-sample of size n(nN) in order that x forms a Latin hypercube, or the multivariate distribution of X is maximally stratified. This paper presents the cLHS method with...
Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. The sample sizes...