Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations. The Kriging method involves length-scale hyperparameters whose optimization is essential to obtain an accurate model and is typically performed using maximum likelihood estimation (MLE). However, for high-dimensional problems, the hyperparameter optimization is problematic due to the shape of the likelihood function, to the exponential growth of the search space with the dimension, and to over-fitting issues when there are too few observations. It often fails to provide correct hyperparameter values. This presentation presents some bibliography elements related to this problem and introduces a new method with a combination of Kriging sub-models with fixed length-scales. Some preliminary results are given.