Combination of feature selection and geographical stratification increases the soil total nitrogen estimation accuracy based on vis-NIR and pXRF spectral fusion.

• Soil TN estimation accuracy using vis-NIR spectra is higher than pXRF spectra. • Feature selection & geographical stratification improve soil TN estimation accuracy. • CARS is the optimal feature selection method for multi-sensor data fusion models. • SO-PLS fusion method increases model accuracy by fully using multiple sensor data. Fast and accurate monitoring of soil total nitrogen (TN) content is particularly important to optimize agricultural inputs (e.g. fertilizers) and inhibit N loss-induced pollution. Proximal soil sensing combined with multi-sensor fusion has been considered to be a promising alternative to traditional laboratory analysis because it can achieve fast, non-destructive, environmentally friendly, and low-cost monitoring. However, the accuracy of this technique depends on the heterogeneity of the dataset and the data fusion strategy. In this study, a total of 500 soil samples were collected from two locations with high degree of soil and environment heterogeneity in Xinjiang, China, and then visible-near-infrared spectroscopy (vis-NIR), portable X-ray fluorescence (pXRF) spectroscopy and soil TN measurement were conducted in the laboratory. Based on partial least squares regression algorithm, direct concatenation, outer-product matrix analysis, and sequentially orthogonalized partial least-square (SO-PLS) were applied for multi-sensor data fusion by using full spectra or spectral features. The results showed that the estimation accuracy using vis-NIR spectral data were higher than pXRF spectral data. Compared with single sensor data and full-spectrum data fusion, the feature selection combined with data fusion contributed to a higher soil TN estimation accuracy, and the competitive adaptive reweighted sampling combined with SO-PLS fusion and geographical stratification modeling strategy had the highest soil TN estimation accuracy, with a root mean square error (RMSE) of 0.1838 g kg−1, and a Lin's concordance correlation coefficient of 0.86. It was worth noting that geographical stratification was an effective modeling strategy to improve the TN estimation accuracy based on multi-sensor data fusion, and its RMSE was 0.10 % ∼ 11.70 % lower than that of global modeling. This study highlights the potential of feature selection combined with geographic stratification to increase the soil TN estimation accuracy based on multi-sensor fusion, especially in regions with high soil and environmental heterogeneity. [ABSTRACT FROM AUTHOR]