Multi-Modal Data Fusion for Satellite Based Analysis Ready Surface Reflectance

Optical satellite imagery used for monitoring the Earth’s dynamic surface is frequently limited by persistent cloud cover. This creates significant data gaps and inconsistencies in daily surface reflectance products. This project aims to advance how we combine optical data with different data modalities that can see through clouds, like active radar and passive microwave data. By leveraging these multi-modal datasets together, we can fill in the cloudy regions and make Planet’s “Planet Fusion” product — which provides a daily, complete picture of the Earth’s surface — even more accurate and reliable. For Planet, this means providing customers with better quality, uninterrupted data, helping to expand their services, particularly in areas often covered by clouds. Ultimately, this work will improve the quality of information provided by Planet Fusion for monitoring land surface changes irrespective of cloud cover conditions.

Faculty Supervisor:

Richard Kelly

Student:

Partner:

Planet Labs Geomatics Corp

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program: