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Soil total nitrogen content and pH value estimation method considering spatial heterogeneity: Based on GNNW-XGBoost

Hao Liang¹, Yue Song², Zhen Dai³

¹College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300 China; Institute of Modern Agriculture and Health Care Industry, Wencheng 325300 China; College of Engineering, China Agricultural University, Beijing 100083 China; Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou 310058,China.

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|January 18, 2025

Summary

A new Geographically Neural Network Weighted-eXtreme Gradient Boosting (GNNW-XGBoost) model accurately estimates soil nitrogen and pH. This method improves predictions by accounting for spatial variations, crucial for sustainable agriculture and environmental monitoring.

Keywords:

GNNW-XGBoost model Soil pH and total N Spatial nonstationarity Vis-NIR data

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Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

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Published on: May 10, 2020

Area of Science:

Soil Science
Environmental Science
Data Science

Background:

Soil nitrogen and pH are critical for soil fertility, plant growth, and microbial activity.
Accurate estimation of soil properties is vital for sustainable agriculture and ecosystem health.
Existing models often fail to capture spatial variations in soil property-spectral relationships.

Purpose of the Study:

To develop and validate a novel model for estimating soil total nitrogen content and pH value.
To address the limitation of spatial non-stationarity in soil property prediction models.
To improve the accuracy of soil property estimation using spectral data.

Main Methods:

A hybrid model combining Geographically Neural Network Weighted Regression (GNNWR) and Extreme Gradient Boosting (XGBoost) was developed.
The model, termed GNNW-XGBoost, utilizes neural networks to enhance prediction accuracy.
Data from Eurostat (2009) on soil nutrients and visible near-infrared spectra from 23 EU member states were used.

Main Results:

The GNNW-XGBoost model demonstrated superior predictive accuracy over standalone XGBoost and GNNWR models.
Highest coefficients of determination (R²) achieved were 0.84 for total nitrogen and 0.80 for pH.
The model significantly reduced root mean square error (RMSE) for both total nitrogen and pH predictions.

Conclusions:

The GNNW-XGBoost model offers a robust and accurate method for predicting soil total nitrogen and pH.
This approach effectively captures spatial heterogeneity in spectral-soil property relationships.
The study provides valuable insights for environmental monitoring, resource management, and sustainable agriculture.