Satellite_S5P

---

A Python toolkit for retrieving, processing, and visualizing Sentinel-5P satellite data, with a focus on air pollutants like NO₂, CO, SO₂, O₃, and HCHO.

Features

• Automated data retrieval from Copernicus Open Access Hub
• Advanced data processing with interpolation and quality control
• High-quality visualization of pollutant concentration maps
• Time series analysis capabilities
• Built-in file retention management to prevent storage overflow

Prerequisites

Before using this toolkit, you need to complete the following steps:

1. Copernicus Account Setup:

   • Register for a free account at Copernicus Open Access Hub
   • Save your username and password for API access

2. Environment Configuration:

   • Create a .env file in the project root directory with your credentials:

     COPERNICUS_USERNAME=your_username
     COPERNICUS_PASSWORD=your_password
     

Installation

# Clone the repository
git clone https://github.com/Alex870521/Satellite_S5P.git

# Navigate to the directory
cd Satellite_S5P

# Install required packages
pip install -r requirements.txt

Usage

Fetching Satellite Data

from src.api.sentinel_api import S5PFetcher

# Initialize fetcher
fetcher = S5PFetcher(max_workers=3)

# Fetch products
products = await fetcher.fetch_data(
    file_class='OFFL',  # 'OFFL' for offline data, 'NRTI' for near-real-time
    file_type='NO2___',  # Available types: 'NO2___', 'CO____', 'SO2___', 'O3__PR', 'HCHO__'
    start_date='2025-02-20',
    end_date='2025-03-06',
    boundary=(120, 122, 22, 25),
    limit=None
)

# Download products
fetcher.parallel_download(products)

Example output:

╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│              Fetching sentinel-5p products (NO₂) from 2025-02-20 to 2025-03-06 ...               │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────── Found 7 Products ────────────────────────────────────────╮
│                                                                                                  │
│                                       Product Information                                        │
│┏━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓ │
│┃ No. ┃ Time                ┃ Name                                                  ┃      Size ┃ │
│┡━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │
││   1 │ 2025-02-25T04:54:18 │ S5P_OFFL_L2__NO2____20250225T043243...50226T204825.nc │ 589.88 MB │ │
││   2 │ 2025-02-24T05:13:14 │ S5P_OFFL_L2__NO2____20250224T045140...50225T210805.nc │ 591.67 MB │ │
││   3 │ 2025-02-24T03:31:44 │ S5P_OFFL_L2__NO2____20250224T031009...50226T055814.nc │ 593.46 MB │ │
││   4 │ 2025-02-23T03:50:40 │ S5P_OFFL_L2__NO2____20250223T032906...50224T235016.nc │ 593.48 MB │ │
││   5 │ 2025-02-22T04:09:37 │ S5P_OFFL_L2__NO2____20250222T034802...50223T200228.nc │ 590.79 MB │ │
││   6 │ 2025-02-21T04:28:32 │ S5P_OFFL_L2__NO2____20250221T040659...50222T202822.nc │ 589.21 MB │ │
││   7 │ 2025-02-20T04:47:30 │ S5P_OFFL_L2__NO2____20250220T042555...50221T204529.nc │ 564.02 MB │ │
│└─────┴─────────────────────┴───────────────────────────────────────────────────────┴───────────┘ │
│                                                                                                  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯

Processing Data

from src.processing.data_processor import S5Processor

# Initialize processor
processor = S5Processor(
    interpolation_method='rbf',
    resolution=(5.5, 3.5),  # Resolution in km
    mask_qc_value=0.5       # Quality control threshold
)

# Process data
processor.process_each_data(
    file_class='OFFL',
    file_type='NO2___',
    start_date='2025-02-20',
    end_date='2025-03-06',
)

Data Description

NO2 Data Processing

This toolkit processes nitrogen dioxide (NO2) and other pollutant data from satellite observations with these key features:

• Quality Control: Filters out low-quality measurements
• Spatial Interpolation: Converts sparse satellite readings to regular grid
• Temporal Aggregation: Daily, weekly, and monthly averages
• Visualization: Generates high-quality maps and time series plots
• Data Export: Creates NetCDF files compatible with other analysis tools

Contact

For bug reports and feature requests please visit GitHub Issues.