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Updated: Dec 22, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms.

Guangfei Wei1,2, Yu Li1, Zhitao Zhang1,2

  • 1College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.

Peerj
|May 8, 2020
PubMed
Summary

Unmanned aerial vehicles (UAVs) with multispectral sensors can accurately estimate soil salt content (SSC). The Variable Importance in Projection (VIP) combined with Random Forest (RF) model showed the highest accuracy for soil salinization monitoring.

Keywords:
Estimation modelsMachine learning algorithmsMultispectral sensorSoil salt contentUnmanned aerial vehicle (UAV)Variable selection methods

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Area of Science:

  • Remote Sensing
  • Soil Science
  • Agricultural Engineering

Background:

  • Soil salinization is a significant global issue impacting socioeconomic development and agricultural sustainability.
  • Traditional satellite-borne sensors have limitations in spatial and temporal resolution for effective soil salinization monitoring.
  • Unmanned aerial vehicles (UAVs) offer on-demand, high-resolution multispectral data acquisition for improved soil monitoring.

Purpose of the Study:

  • To quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery.
  • To explore advanced data mining techniques for extracting valuable information from multispectral data.
  • To compare the effectiveness of different spectral covariate selection methods and machine learning algorithms for SSC estimation.

Main Methods:

  • Collection of 60 soil samples for ground-truthing in Inner Mongolia, China.
  • Acquisition of UAV-borne multispectral data and construction of 22 spectral covariates (bands and indices).
  • Application of Gray Relational Analysis (GRA), Successive Projections Algorithm (SPA), and Variable Importance in Projection (VIP) for covariate selection.
  • Development of estimation models using Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), and Random Forest (RF) regression.
  • Model performance evaluation using coefficient of determination (R^2), Root Mean Squared Error (RMSE), and Ratio of Performance to Deviation (RPD).

Main Results:

  • Variable selection methods significantly improved SSC estimation accuracy, with VIP > GRA > SPA.
  • Machine learning algorithm performance followed the order: RF > SVR > BPNN.
  • The VIP-Random Forest (VIP-RF) model achieved the highest accuracy, with Rc^2 = 0.835, Rp^2 = 0.812, and RPD = 2.299.
  • All 12 developed SSC estimation models demonstrated quantitative estimation capability (RPD > 1.4).

Conclusions:

  • UAV-borne multispectral sensing is a feasible and effective technology for quantitative soil salinization monitoring.
  • The VIP method combined with RF regression provides a robust approach for SSC estimation from multispectral data.
  • This study offers valuable insights and a reference for future research on UAV-based soil salinization assessment.