# Data Collection, Preparation & Feature Extraction for AOD Estimation in the Middle East

## Table of Contents

### 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:

- 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.

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:

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**

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.

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:

    BM_{sitename}

Each site directory contains:

- train_images/
- test_images/
- BM_{sitename}_train_dataset.csv
- BM_{sitename}_test_dataset.csv

Each CSV follows the column structure:

    File,location,aod,path,date,latitude,longitude

Example row (site-level CSV):

    train_1.tif,Cairo_EMA_2,0.3821,/Train_images/train_1.tif,2021-08-26,30.080767,31.290067

**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:

- BM_Middle_East_train_dataset.csv
- BM_Middle_East_test_dataset.csv

These combined files use the same schema:

    File,location,aod,path,date,latitude,longitude

Example aggregated-row:

    test_1.tif,Cairo_EMA_2,0.3574,/BM_Cairo_EMA_2/Test_images/test_1.tif,2019-07-23,30.080767,31.290067

**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
---