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Soil texture is important for many environmental processes. In this paper, westudy the classification of soil texture based on hyperspectral data. Wedevelop and implement three 1-dimensional (1D) convolutional neural networks(CNN): the LucasCNN, the LucasResNet which contains an identity block asresidual network, and the LucasCoordConv with an additional coordinates layer.Furthermore, we modify two existing 1D CNN approaches for the presentedclassification task. The code of all five CNN approaches is available on GitHub(Riese, 2019). We evaluate the performance of the CNN approaches and comparethem to a random forest classifier. Thereby, we rely on the freely availableLUCAS topsoil dataset. The CNN approach with the least depth turns out to bethe best performing classifier. The LucasCoordConv achieves the bestperformance regarding the average accuracy. In future work, we can furtherenhance the introduced LucasCNN, LucasResNet and LucasCoordConv and includeadditional variables of the rich LUCAS dataset.

Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data