A Homologous Preprocessing–Robust Fusion Framework for Stable Retrieval of Soil Total Nitrogen and Organic Matter from Hyperspectral Spectra

Accurate estimation of soil total nitrogen (TN) and soil organic matter (SOM) is important for sustainable soil fertility assessment and precision nutrient management. Visible–near-infrared hyperspectral sensing provides a rapid and non-destructive solution, but its inversion accuracy is strongly affected by spectral preprocessing, especially under small-sample conditions. To reduce dependence on a manually selected preprocessing operator, this study proposes a homologous preprocessing representation fusion framework based on greedy concatenation (HPRF–GC). The framework constructs multiple homologous spectral views from the same raw spectrum, selects informative views through cross-validation-guided greedy forward selection, and concatenates the selected views before random forest or support vector regression. A self-built in situ hyperspectral dataset was collected from two representative black calcareous Mollisol farms in Heilongjiang Province, China, including 200 composite samples measured with a GaiaField Pro V10 imager at 5 m height under midday illumination using white reference calibration. On this dataset, HPRF–GC reduced RMSE by 3.61% for TN–RF, 9.94% for TN–SVR, 0.87% for SOM–RF, and 7.15% for SOM–SVR compared with the strongest single-preprocessing baseline, while introducing only a modest training-time overhead. On the public LUCAS 2015 dataset, HPRF–GC achieved competitive TN prediction performance, with an R2 of 0.890 and an RMSE of 1.191 under RF. These results indicate that HPRF–GC provides a lightweight, interpretable and reproducible strategy for reducing preprocessing selection sensitivity in small-sample soil hyperspectral inversion.