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Application of Linearization and Approximation01:29

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Meter-Scale UAV Hyperspectral Remote Sensing for Pollution Source Attribution: Dynamic Reference Spectrum

Tiliang Zou1, Chengzhi Xing2, Wei Tan2

  • 1School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China.

Environmental Science & Technology
|October 30, 2025
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Summary
This summary is machine-generated.

This study introduces a novel framework for Unmanned Aerial Vehicle (UAV)-based hyperspectral sensing to accurately monitor nitrogen dioxide (NO2) and nitrous acid (HONO) pollution with meter-scale resolution.

Keywords:
UAV meter-scale remote sensinghyperspectralreference spectrasource attributionsurface albedo

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Area of Science:

  • Atmospheric remote sensing
  • Environmental monitoring
  • Spectroscopy

Background:

  • Differential Optical Absorption Spectroscopy (DOAS) faces retrieval accuracy limitations in low-altitude UAV remote sensing under dynamic conditions.
  • Aerosol-surface coupling and surface heterogeneity introduce spectral deviations and biases in NO2 retrieval.

Purpose of the Study:

  • To develop a systematic framework combining UAV-based hyperspectral sensing with dynamic reference spectrum optimization.
  • To improve the quantitative monitoring accuracy of NO2 and HONO using meter-scale resolution remote sensing.
  • To address retrieval limitations in DOAS for dynamic observation conditions.

Main Methods:

  • Implementation of quadrotor-based hyperspectral remote sensing for NO2 and HONO monitoring.
  • Dynamic reference spectrum optimization using a weighted multicriteria scoring model.
  • Integration of DeepLabV3+ semantic segmentation with hyperspectral imaging for surface albedo correction.

Main Results:

  • Achieved meter-scale resolution quantitative monitoring of NO2 and HONO.
  • Reduced NO2 retrieval errors by 18-24% by resolving aerosol-surface coupling-induced spectral deviations.
  • Reduced biases in NO2 Vertical Column Density (VCD) retrievals from 28% to 12% through dynamic surface albedo correction.

Conclusions:

  • The developed methodology provides theoretical and technological foundations for advanced atmospheric monitoring.
  • Enables the transition from 'end-pipe treatment' to 'process control' in pollution management.
  • Offers novel tools for microemission source identification and high-resolution pollution inventory development.