[An Improved DDV Method to Retrieve AOT for HJ CCD Image in Typical Mountainous Areas]

Area of Science:

• Remote Sensing
• Atmospheric Science
• Environmental Monitoring

Context:

• HJ CCD images lack Mid-Nir bands, hindering traditional Aerosol Optical Thickness (AOT) retrieval.
• Mountainous terrain causes significant spatial-temporal AOT variations, complicating atmospheric correction.
• Existing methods struggle with the unique challenges of aerosol retrieval in complex mountainous environments.

Purpose:

• To develop and validate an improved Dark Dense Vegetation (DDV) method for accurate AOT retrieval from HJ CCD imagery in mountainous areas.
• To address the limitations of HJ CCD's spectral bands for AOT estimation.
• To enhance atmospheric correction accuracy for remote sensing applications in challenging terrains.

Summary:

• A red band histogram threshold method was employed to identify Dark Dense Vegetation (DDV) pixels in mountainous HJ CCD imagery.
• Aerosol Optical Thickness (AOT) was retrieved using a lookup table generated by the 6S radiative transfer model and interpolated across the image.
• The improved DDV method demonstrated high consistency with MODIS aerosol products, achieving a fitting curve of y = 0.8286x - 0.01 and R² of 0.9843.

Impact:

• The enhanced DDV method effectively retrieves AOT, satisfying precision requirements for atmospheric and terrain radiation correction of HJ CCD images in mountainous regions.
• This approach overcomes spectral information limitations of HJ CCD imagery for radiative transfer calculations.
• The study provides a foundation for automated processing and improved accuracy of HJ CCD-based remote sensing in complex mountainous terrains.