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f085e39 d32d28f f085e39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # 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
---
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