Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

Abstract Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km...

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Bibliographic Details
Main Authors: Andrew L. Mullen, Jennifer D. Watts, Brendan M. Rogers, Mark L. Carroll, Clayton D. Elder, Jonas Noomah, Zachary Williams, Jordan A. Caraballo‐Vega, Allison Bredder, Eliza Rickenbaugh, Eric Levenson, Sarah W. Cooley, Jacqueline K. Y. Hung, Greg Fiske, Stefano Potter, Yili Yang, Charles E. Miller, Susan M. Natali, Thomas A. Douglas, Ethan D. Kyzivat
Format: Article
Language:English
Published: Wiley 2023-04-01
Series:Geophysical Research Letters
Subjects:
PlanetScope
water bodies
remote sensing
deep learning
seasonality
methane
Online Access:https://doi.org/10.1029/2022GL102327
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