Welcome to Team PARAGON's solution for the 2024 NASA Space Apps Challenge! Our project provides a powerful and user-friendly platform for accessing real-time Landsat satellite data, specifically from Landsat 8 and 9, using an interactive interface that simplifies the process of retrieving, processing, and analyzing satellite imagery.
You can view our live web application here: http://teamparagon.earth/
- Video Presentation: Watch our presentation
- Canva Slides: View our slides
- Chalenge Details: 2024 NASA Space Apps Challenge - Landsat Reflectance Data: On the Fly and at Your Fingertips
Landsat missions have provided the longest continuous dataset of remotely sensed measurements of Earth’s land surface. Comparing ground-based spectral measurements with Landsat Surface Reflectance (SR) data collected at the same time can facilitate experiential learning, encourage scientific exploration with satellite data, foster interdisciplinary and spatial thinking skills, and empower individuals to become informed global citizens. But to compare ground-based measurements with Landsat data, you need to know when Landsat will be passing over a specific land area, and then be able to access the Landsat data collected at that place and time. This specialized and labor-intensive task has yet to be integrated into a single, cohesive application. Your challenge is to develop a web-based application that supports the comparison of ground-based observations with Landsat data by allowing users to define a target location, receive notifications when Landsat is to pass over that location, and then access and display the corresponding Landsat SR data.
Some key difficulties we solve include:
- The overwhelming volume of data generated by Landsat satellites, making data management difficult.
- Variations in image quality due to cloud cover, atmospheric conditions, and sensor calibration.
- Time-consuming data access and processing, often requiring specialized knowledge.
Our solution bridges these gaps by providing a streamlined, interactive platform to process, access, and visualize Landsat data along with ground-based measurements, easing the burden on users.
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Real-Time Satellite Tracking: Visualize the paths and positions of Landsat 8 and 9 satellites in real-time on an interactive map.
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Next Acquisition Date Calculation: Easily calculate the next acquisition date for a specific location for both Landsat 8 and Landsat 9 satellites.
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Landsat Reflectance Data: Access real Landsat data for specific geographical coordinates (latitude/longitude) and view it as an image overlay on the map.
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Interactive Map: Use Leaflet and ESRI Leaflet to interactively explore geographical data, track satellites, and view data overlays.
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Filter Controls: Use controls like cloud percentage filters, date range selection, and more to refine the Landsat data results.
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Email Reminder Feature: Set reminders to receive email notifications when the next satellite pass is approaching your location.
- React: Modern JavaScript library for building user interfaces.
- Leaflet: JavaScript library for interactive maps.
- ESRI Leaflet: Extends Leaflet with ArcGIS services and data.
- TypeScript: A typed superset of JavaScript for type safety.
- Tailwind CSS: A utility-first CSS framework for styling.
- Firebase: Used for authentication and email reminders.
- Python (Flask): Lightweight web framework for serving the backend.
- Skyfield: Library for satellite position calculations based on TLE data.
- USGS Machine-to-Machine (M2M) API: Used for searching and retrieving Landsat satellite data.
- NASA Celestrak TLE API: To retrieve up-to-date Two-Line Element (TLE) data for satellite tracking.
- USGS M2M API: Used for fetching Landsat satellite imagery based on user input.
- NASA Celestrak: Provides the Two-Line Element (TLE) data for tracking satellites.
- Landsat Track Metadata API: For fetching metadata on satellite paths.
- Firebase: Used for user authentication and email reminders.
- Track the real-time positions of Landsat 8 and Landsat 9 on an interactive map.
- Click on any location to see the next acquisition date for that location.
- The application uses satellite orbit data and acquisition cycles to calculate the next time a satellite will pass over a specific location.
- View reflectance data for any selected location.
- Use filter controls like cloud percentage and date range selection to refine results.
- Satellite images are shown as overlays on the map to analyze geographical changes.
- Users can enable notifications for specific locations to be reminded via email before the next satellite pass.
- Description: Retrieves real-time position data for Landsat 8 and 9 satellites.
- Response: JSON with details like latitude, longitude, altitude, and speed.
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Parameters:
path(Required): The satellite path for which to retrieve the next acquisition date.
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Response: The next acquisition date and time for both Landsat 8 and Landsat 9.
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Parameters:
start_date: Start date for fetching satellite images (default is 2024-09-01).end_date: End date for fetching satellite images (default is today's date).latitude: Latitude of the location of interest.longitude: Longitude of the location of interest.cloud_cover: Maximum cloud cover allowed (percentage).
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Response: Returns the Landsat image as a PNG file, processed for reflectance.
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Ground-Based Measurements Integration: We plan to streamline ground-based spectral measurements from soilspectroscopy.org into the web application, allowing for more detailed comparative analysis with Landsat data.
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Enhanced Analytics: In future iterations, we aim to include in-depth analytics, such as vegetation index and surface temperature analysis using Landsat data.
We welcome contributions from the community! Please follow the steps below to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Make your changes.
- Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature-branch). - Submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
- Satellite data and video clips used in this project are credited to the NASA Landsat mission and their official website.
For any questions, issues, or collaboration ideas, feel free to contact us through our GitHub page.
Visit our website: http://teamparagon.earth/