Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network - Tsakiridis, Nikolaos L.; Keramaris, Konstantinos D.; Theocharis, John B.; Zalidis, George C. (Geoderma, 2020)
•A novel 1-D CNN learning framework for soil spectral analysis is proposed.•Multiple spectral sources are combined to maximize the spectral information.•All soil properties are simultaneously predicted to utilize the cross-correlations.•A local adaptive error-correction mechanism enhances the prediction performance.•The framework produced interpretable and accurate results in the LUCAS SSL. The use of visible near-infrared and shortwave-infrared (VNIR-SWIR) diffuse reflectance spectroscopy for the estimation of soil properties is increasingly maturing with large-scale soil spectral libraries (SSLs) of laboratory spectra developed across the globe. Such an SSL is the publicly available LUCAS topsoil database with approximately 20,000 soil samples encompassing 23 countries of the European Union. A wide variety of machine learning tools have been applied to the LUCAS SSL to predict some of the soil samples’ physicochemical properties with different degrees of accuracy. In this paper, we developed and examined the use of a novel one-dimensional convolutional neural network (CNN) to simultaneously predict ten physicochemical properties of the LUCAS SSL. Leveraging on the use of multiple-input channels it uses as model inputs the absorbance spectra along with some pre-processed spectra developed using standard techniques. Moreover, it exploits the use of local spectral neighborhoods to perform an adaptive error-correction mechanism. This novel localized multi-channel 1-D CNN was applied to all the available physicochemical properties of the LUCAS SSL and was statistically compared with the current state-of-the-art where it was shown to statistically outperform its counterparts, as well as with other CNNs where it exhibited the best performance. In particular, for the mineral soil samples, the RMSE for the Clay content was 4.80% (R2 0.86), for soil organic carbon the RMSE was 10.96 g kg−1 (R2 0.86), while for total nitrogen the RMSE was 0.66 g kg−1 (R2 0.83).