Principle Component Analysis on multiple functions
Variations of PCA can still be applicable.

If the data is not linear, use nonlinear PCA.

It is not an issue there a multiple "y"s for every "x". PCA is unsupervised, there is no notion of targets. In PCA, there are only dimensions. 

Typically, the dimensions are standardized so each dimension can be weighted independent of scale.

One caveat - PCA is domain agnostic. Given that you have physical land data, you can use domain-specific methods such as hierarchical spatial indexing.