Optimizing Vegetation Cover Classification Accuracy by Assessing the Impact of Atmospheric Corrections on Spectral Reflectivity Using Remote Sensing Data

Abdulrahman B.  Ali; mohammed A. Mohammed; Ayat F.  Abed; Ban A. Alrazaq; Bilal A.  Ahmed

Article Details

Optimizing Vegetation Cover Classification Accuracy by Assessing the Impact of Atmospheric Corrections on Spectral Reflectivity Using Remote Sensing Data

Authors:

Abdulrahman B. Ali | mohammed A. Mohammed | Ayat F. Abed | Ban A. Alrazaq | Bilal A. Ahmed

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.59 Issue 1A, pp.235-254

Document Type:

Article

Publication Date:

1/2026

Document Language:

English

Document ID (Org No.):

2001836949

pISSN:

2663-8754

Keywords:

Abstract:

 Satellites contain special sensors that capture the spectral fingerprints of materials on the ground in the form of different spectral bands. These recorded data are affected by the atmosphere through clouds, water vapor particles, and aerosols. This can cause distortions in vegetation classification, a critical issue for classifying species with similar spectral fingerprints. Atmospheric corrections aim to improve spectral resolution and thus classification after removing atmospheric interference. This study focuses on the modifications made by solar radiation correction using atmospheric corrections for Bottom of Atmosphere reflectance on vegetation classification using the Landsat 9 dataset for the Nineveh Plains region. To improve the results, the accuracy of the surface reflectance limit calibration and atmospheric correction was calculated, along with the use of Vegetation Condition Indices. A series of radiometric and geometric procedures were performed to ensure the accuracy of vegetation classification, including the dark object subtraction method, surface reflectance, and atmospheric correction using iCOR algorithm. The accuracy of vegetation classification was evaluated before and after the corrections. The study found that vegetation classification accuracy improved significantly after the atmospheric correction. The overall accuracy and kappa statistics showed significant increases, where the overall accuracy increased from 72% (kappa = 0.64) in winter and 76% (kappa = 0.70) in summer, to 91% (kappa = 0.88) and 90% (kappa = 0.86) in winter and summer, respectively.

Article Details

Optimizing Vegetation Cover Classification Accuracy by Assessing the Impact of Atmospheric Corrections on Spectral Reflectivity Using Remote Sensing Data

Authors:

Abdulrahman B. Ali | mohammed A. Mohammed | Ayat F. Abed | Ban A. Alrazaq | Bilal A. Ahmed

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.59 Issue 1A, pp.235-254

Document Type:

Article

Publication Date:

1/2026

Document Language:

English

Document ID (Org No.):

2001836949

pISSN:

2663-8754

Signature reflectivity

Classification accuracy

Atmospheric corrections

Landsat-9, Vegetation condition index

Issues

Article Details

Optimizing Vegetation Cover Classification Accuracy by Assessing the Impact of Atmospheric Corrections on Spectral Reflectivity Using Remote Sensing Data

Authors: Abdulrahman B. Ali | mohammed A. Mohammed | Ayat F. Abed | Ban A. Alrazaq | Bilal A. Ahmed

Publisher: Iraqi Geologists Union (Union of Iraqi Geologists)

Journal: The Iraqi Geological Journal Vol.59 Issue 1A, pp.235-254

Document Type: Article

Publication Date: 1/2026

Document Language: English

Document ID (Org No.): 2001836949

pISSN: 2663-8754

Signature reflectivity

Classification accuracy

Atmospheric corrections

Landsat-9, Vegetation condition index

Issues

Authors:

Abdulrahman B. Ali | mohammed A. Mohammed | Ayat F. Abed | Ban A. Alrazaq | Bilal A. Ahmed

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.59 Issue 1A, pp.235-254

Document Type:

Article

Publication Date:

1/2026

Document Language:

English

Document ID (Org No.):

2001836949

pISSN:

2663-8754