Projects per year
Abstract
Quantifying aerosol compositions (e.g., type, loading) from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In this study, the first and second-order spectral derivatives of aerosol optical depth (AOD) with respect to wavelength are explored to determine the partitions of the major components of aerosols based on the spectral dependence of their particle optical size and complex refractive index. With theoretical simulations from the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, AOD spectral derivatives are characterized for collective models of aerosol types, such as mineral dust (DS) par-ticles, biomass-burning (BB) aerosols and anthropogenic pollutants (AP), as well as stretching out to the mixtures among them. Based on the intrinsic values from normalized spectral derivatives, referenced as the Normalized Derivative Aerosol Index (NDAI), a unique pattern is clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (f AOD) for major aerosol components can be extracted. The subtlety of this NDAI method is examined by using measurements of typical aerosol cases identified carefully by the ground-based Aerosol Robotic Network (AERONET) sun–sky spectroradiometer. The results may be highly practicable for quantifying f AOD among mixed-type aerosols by means of the normalized AOD spectral derivatives.
Original language | English |
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Article number | 1544 |
Journal | Remote Sensing |
Volume | 13 |
Issue number | 8 |
DOIs | |
State | Published - 2 Apr 2021 |
Keywords
- AOD spectral derivatives
- Aerosol partition
- Complex refractive index
- Fractions of total AOD
- Normalized derivative aerosol index
- Particle size
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Dive into the research topics of 'Spectral derivatives of optical depth for partitioning aerosol type and loading'. Together they form a unique fingerprint.Projects
- 2 Finished
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Development of 3-D Aerosol Distribution with Satellite Retrieval for the Application to Air Quality Monitoring and Model Forecast
Lin, T.-H. (PI)
1/08/19 → 31/07/20
Project: Research
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Development of Pm Monitoring and Forecasting System Based on Aerosol Properties Retrieved from Satellite and And in Situ Measurements
Lin, T.-H. (PI)
1/08/18 → 31/07/19
Project: Research