Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data

In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts of SOM hyperparameters on the regression performance and compare the results of the SOM framework with two established regressors on a measured dataset. The derived results reveal the potential of the SOM framework. Finally, we propose further research aspects for the SOM framework to analyze its capabilities and limitations. We have published our dataset in [1] to ensure the reproducibility of the results.