Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China.

[Display omitted] • SOC prediction in the agro-pastoral transitional zone of northwest China was realised using VIS-NIRS and chemometrics. • Ten spectral transforms coupled with correlation analysis and the Boruta algorithm for feature wavelength selection. • SOC content prediction model based on Boruta-FD-RF was developed. • Integrating spectral preprocessing, feature selection and chemometrics strategies has good prediction accuracy and stability. Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China. [ABSTRACT FROM AUTHOR]