# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 5 Use of Remote Sensing to Design the Sampling Strategy for DSM  
When RS products are available and relevant in a target area, and if we assume that the SCORPAN model for DSM is efficient, it seems logical to optimize the soil sampling design based on covariate space, and thus to use for designing the sampling, RS products alone or with other covariates. This may be especially useful if the number of soil measurements/observations is constrained. This has led some authors to propose that soil sampling is designed in terms of covariate space. One of the seminal articles in this technique is the constrained Latin Hypercube Sampling (cLHS) proposed by [PERSON] and [PERSON] [318]. Figure 8: Main cropping practices accessible via remote sensing and reviewed by [PERSON] et al. [309]. After [PERSON] et al., Remote Sensing and Cropping Practices: A Review. _Remote Sensing_**2018**, _10_, doi:10.3390/rs10010099. Bold cropping systems were reviewed. © [PERSON] et al. with permission of the authors. The cLHS sampling in the presence of covariates is designed to select a limited number of sites in an area. According to [PERSON] and [PERSON] [318], \"This kind of sampling should produce a reasonably efficient way of sampling soil and its environment so that the range of conditions are encountered, ensuring a good chance of fitting relationships if they exist\". The last words of this quote, i.e., \"if they exist\", are very important to consider. A strong assumption of cLHS is that the selected covariates represent the variability of the target variable. In other words, if a controlling variable is missing from the set of covariates, the sampling design has a high probability of missing part of the variability of the target property. The main advantage is to optimize the number of samples and thus the sampling effort and cost to capture the main combinations of the covariate feature space. One of its weaknesses is that it is not well suited to some spatial modeling techniques, such as geostatistics and kriging [131]. This is because cLHS, in its process of selecting sampling points, maximizes the feature difference between sampling points. The residuals of the predictions obtained after modeling using a cLHS sampling for calibration, can, however, be analyzed by geostatistics or other validation techniques, on the condition that an independent validation set is available (see an example in Figure 9, left). Note that in the figure extracted from [PERSON] and [PERSON] [318], they just use the stratified sampling design as an example compared to cLHS. Various adaptations of cLHS proposed by different researchers consider accessibility and cost, or better space filling (e.g., [319; 320; 321]). Space filling has the advantage of not missing situations that could be under-represented by cLHS. If, for example, a large area is characterized by similar combinations in the feature space, it might be under-sampled, even if it contains a soil variability that is not captured by the covariates. [PERSON] et al. [322] recently studied sampling design optimization for soil mapping with random forest, and concluded that cLHS sampling performed worse than other sampling designs. It should be noted that, in their study, the subsampling design was based on a preliminary selection of an existing set of sample sites that was already a mixed sampling design, i.e., subsampling a regular grid using a method close to cLHS, and with known results (LUCAS Soil, [323]). In other words, the spatial coverage of their learning full sample was already rather efficient, and some covariates had already been used to locate the samples in the grid. Note that the idea of exploring the cLHS method using covariates on a dense set of already sampled points was also tested by [PERSON] et al. [141], who concluded that it was efficient at highlighting situations that were under-sampled in the original soil legacy  
Figure 9: Example of the location of sample points in landscape using stratified random sampling (**left**) or using cLHS (**right**).