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 % similar to 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.
