Principal Investigator

Brian Bue
Email: bbue@jpl.nasa.gov

Description

This project leverages nearly a decade of experience in developing automated plume detection systems for methane remote sensing campaigns. Using imaging spectrometer observations from airborne platforms like AVIRIS-NG and GAO, as well as spaceborne platforms such as EMIT and Carbon Mapper/Tanager, the team has demonstrated robust capabilities for detecting methane plumes across diverse geographical locations. The project employs advanced deep learning techniques, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformers, to capture complex geospatial structures in remote sensing data. Through rigorous statistical validation and in-depth analysis of spatiotemporally diverse retrieval products, the research aims to address key challenges in operational plume detection and monitoring for both airborne and spaceborne platforms.

Publications

In Preparation

Multi-Platform Methane Plume Detection via Model and Domain Adaptation
Vassiliki Mancoridis, Brian Bue, Jake H. Lee, Andrew Thorpe, Daniel Cusworth, Alana Ayasse, Philip G. Brodrick, and Riley Duren

Towards Operational Automated Greenhouse Gas Detection
Brian D Bue, Jake Lee, Andrew Thorpe, Phil Brodrick, Dan Cusworth, Alana Ayasse, and Riley Duren

2024

Challenges and Opportunities in Operational Automated Methane Plume Detection
Riley Duren, Brian Bue, Jake Lee, Andrew K Thorpe, Philip G Brodrick, Daniel Cusworth, and Robert O Green
AGU Fall 2024, A52C-07

Spectral Transformers for EMIT Methane Retrieval
Jake Lee, Brian Bue, Philip G Brodrick, Andrew K Thorpe, William Keely, Michael Kiper, and Jay Fahlen
AGU Fall 2024

2023

Deep Learning Saliency and Segmentation Methods for Robust Methane Plume Detection
Jake Lee, Brian D Bue, Andrew K Thorpe, Daniel Cusworth, Alana Ayasse, and Riley Duren
AGU Fall 2023 [abstract/poster]

Towards Operational Automated Methane Plume Monitoring
Brian Bue, Jake Lee, Jack Lightholder, Andrew K Thorpe, Daniel Cusworth, Alana Ayasse, and Riley Duren
AGU Fall 2023 [abstract]

Improving Deep Learning Methods for Robust Methane Plume Detection using Alternative Input Representations
Anagha Satish, Brian D Bue, Jake Lee, Andrew K Thorpe, Daniel Cusworth, Alana Ayasse, and Riley Duren
AGU Fall 2023 [abstract]

Leveraging Airborne Data to Enable Spaceborne Methane Plume Detection via Model and Data Driven Approaches
Vassiliki Mancoridis, Brian D Bue, Jake Lee, Andrew K Thorpe, Daniel Cusworth, Alana Ayasse, and Riley Duren
AGU Fall 2023 [abstract]

Algorithm advances for an operational imaging spectroscopy greenhouse gas detection system - lessons from EMIT after a year in orbit
Philip Brodrick, Jay Fahlen, Red Willow Willow Coleman, David R Thompson, Andrew K Thorpe, Clayton Elder, Claire Villanueva-Weeks, Amanda Lopez, Jake Lee, Brian D Bue, K. Dana Chadwick, and Robert O Green
AGU Fall 2023

2022

Robust Multi-Campaign Imaging Spectrometer Methane Plume Detection using Deep Learning
Jake Lee, Brian Bue, Micahel Garay, Andrew Thorpe, Riley Duren, Daniel Cusworth, and Alana Ayasse
AGU Fall 2022 [abstract/poster]

2021

Improving Imaging Spectrometer Methane Plume Detection with Large Eddy Simulations
Arjun Ashok Rao, Steffen Mauceri, Andrew Thorpe, Jake Lee, Siraput Jongaramrungruang, Riley Duren
AGU Fall 2021 [abstract/poster]

Methane Plume Detection weith Future Orbital Imaging Spectrometers
Jake Lee, Steffen Mauceri, Sharmita Dey, Arjun Ashok Rao, Ryan Alimo, Andrew Thorpe, Siraput Jongaramrungruang, Riley Duren
AGU Fall 2021 [abstract/poster]