| # Data Collection, Preparation & Feature Extraction for AOD Estimation in the Middle East |
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| ## Table of Contents |
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| ### Data Collection and Preparation |
| 1. [Objective](#1-objective) |
| 2. [Data Collection](#2-data-collection) |
| 3. [Preprocessing of AOD Values](#3-preprocessing-of-aod-values) |
| 4. [Dataset Structure](#4-dataset-structure) |
| 5. [Final Combined Dataset](#5-final-combined-dataset) |
| 6. [Quality Control and Filtering](#6-quality-control-and-filtering) |
| 7. [Outcome](#7-outcome) |
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| ## 1. Objective |
| The objective of this work is to construct a dataset suitable for estimating Aerosol Optical Depth (AOD) over the Middle East using ground-based (AERONET) and satellite-based (Sentinel-2 A2) observations. |
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| ## 2. Data Collection |
| AOD measurements were obtained from AERONET sites across the Middle East. To ensure consistency and quality, only sites satisfying the following criteria were included: |
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| - Availability of **Level 2** AOD values. |
| - Records extending **after 2015**, since Sentinel-2 L2A products became broadly available post-2015. |
| - Valid measurements for **AOD at 500 nm** and **AOD at 675 nm** wavelengths. |
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| Corresponding Sentinel-2 images were downloaded using a **4 km × 4 km bounding box** centered at each AERONET site to ensure that the imagery spatially represents the site measurements. |
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| ## 3. Preprocessing of AOD Values |
| Raw AERONET CSV files varied in format, and most sites did not provide daily averages. Therefore, a preprocessing pipeline was applied as follows: |
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| 1. For each site, extract only the following fields: |
| - Date |
| - Site location (site name / identifier) |
| - Daily average of **AOD_500nm** |
| - Daily average of **AOD_675nm** |
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| 2. Compute the **daily average AOD at 550 nm** using the **Ångström interpolation** between the 500 nm and 675 nm wavelengths, since 550 nm is the standard reference wavelength for many AOD studies. The Ångström relation was applied per-day to produce a harmonized AOD_550nm value for each observation date. |
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| This procedure produces a single comparable daily AOD value per site-date and eliminates format inconsistency across AERONET CSV files. |
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| ## 4. Dataset Structure |
| For each AERONET site a directory was created with the naming convention: |
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| BM_{sitename} |
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| Each site directory contains: |
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| - train_images/ |
| - test_images/ |
| - BM_{sitename}_train_dataset.csv |
| - BM_{sitename}_test_dataset.csv |
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| Each CSV follows the column structure: |
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| File,location,aod,path,date,latitude,longitude |
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| Example row (site-level CSV): |
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| train_1.tif,Cairo_EMA_2,0.3821,/Train_images/train_1.tif,2021-08-26,30.080767,31.290067 |
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| **Notes:** |
| - `File` is the image filename (relative to the site folder). |
| - `location` is the site identifier used in this project. |
| - `aod` is the interpolated AOD at 550 nm. |
| - `path` is the image path relative to the dataset root or the site folder. |
| - `date` is the observation date (YYYY-MM-DD). |
| - `latitude`, `longitude` are the site coordinates. |
| - For each site 80% of the samples are for training, and 20% are for testing. |
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| ## 5. Final Combined Dataset |
| Two aggregate CSV files were created at the dataset root to allow regional-level training and evaluation: |
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| - BM_Middle_East_train_dataset.csv |
| - BM_Middle_East_test_dataset.csv |
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| These combined files use the same schema: |
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| File,location,aod,path,date,latitude,longitude |
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| Example aggregated-row: |
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| test_1.tif,Cairo_EMA_2,0.3574,/BM_Cairo_EMA_2/Test_images/test_1.tif,2019-07-23,30.080767,31.290067 |
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| **Note:** |
| - The final csv files made sure that 80% of each site is for training, and 20% of each site for testing. This way, we make sure that there is no site bias. |
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| ## 6. Quality Control and Filtering |
| - Only Level 2 AERONET records were used to ensure cloud-screened, quality-assured measurements. |
| - Sites with insufficient post-2015 coverage or missing either 500 nm or 675 nm records were excluded. |
| - Images were checked to ensure the site fell well within the 4 km × 4 km bounding box; anomalous geolocation mismatches were logged and removed. |
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| ## 7. Outcome |
| The result is a harmonized dataset linking AERONET-derived AOD (interpolated to 550 nm) and co-located Sentinel-2 imagery for 17 sites in the Middle East. The dataset supports both per-site model development and combined regional modeling for AOD estimation. |
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| license: mit |
| language: |
| - en |
| pretty_name: g |
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