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Spectral data-driven and machine learning-based modeling of soil total nitrogen content.

Zhenyu Dong1, Ni Wang1, Jiancang Xie1

  • 1Institute of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|June 28, 2025
PubMed
Summary

Rapid soil total nitrogen (TN) monitoring is crucial for agriculture. This study developed a hyperspectral model for accurate TN quantification in arid soils, aiding precision agriculture.

Keywords:
Agro-pastoral transitional zoneFeature wavelength selectionMachine learningSoil TNVis-NIR spectroscopy

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Remote Sensing

Background:

  • Soil total nitrogen (TN) is vital for agricultural fertility and environmental health.
  • Rapid TN monitoring is essential for precision agriculture, especially in fragile zones like Northwest China.

Purpose of the Study:

  • To develop a rapid and accurate method for quantifying soil total nitrogen (TN) using hyperspectral technology.
  • To establish a robust prediction model for TN in arid agro-pastoral soils.

Main Methods:

  • Collected 116 soil samples from Jingbian County, China.
  • Applied spectral transformations, Correlation Analysis (CA), and Competitive Adaptive Reweighted Sampling (CARS) for feature extraction.
  • Utilized Partial Least Squares Regression (PLSR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) for model development.

Main Results:

  • Soil TN content ranged from 0.003 to 0.781 g kg⁻¹, with a mean of 0.266 g kg⁻¹.
  • Soil spectral reflectance showed a negative correlation with TN content.
  • The Log1/R-CARS-GBDT model achieved high accuracy (R² of 0.92 and 0.89 for calibration and validation).

Conclusions:

  • Hyperspectral analysis combined with advanced modeling techniques enables accurate TN quantification in arid soils.
  • The developed framework supports precision agriculture and data-driven land management in ecologically fragile regions.