Decreased Latency in Landsat Derived Evapotranspiration Products Using Machine Learning on Google Earth Engine-Reference-Cited by-同舟云学术

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Decreased Latency in Landsat Derived Evapotranspiration Products Using Machine Learning on Google Earth Engine

Published:2024-07-07 Issue: Volume: Page:3054-3057
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Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Yang Yun¹,Anderson Martha²,Knipper Kyle³,Gao Feng²,Hain Christopher⁴,Duan Weina¹,Melton Forrest⁵,Morton Charles⁶,Volk John⁶,Wang Zhuosen⁷

Affiliation:

1. Mississippi State University,Starkville,MS

2. USDA-ARS,Hydrology and Remote Sensing Lab,Beltsville,MD

3. USDA-ARS,Sustainable Agricultural Water System Unit,Davis,CA

4. NASA Marshall Flight Center,Nashville,AL

5. California State University,Monterey,CA

6. Desert Research Institute,Reno,NV

7. University of Maryland,College Park,MD

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641995.pdf?arnumber=10641995

Reference19 articles.

1. Landsat-8: Science and product vision for terrestrial global change research

2. Opening the archive: How free data has enabled the science and monitoring promise of Landsat

3. Fifty years of Landsat science and impacts

4. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

5. Using High-Spatiotemporal Thermal Satellite ET Retrievals for Operational Water Use and Stress Monitoring in a California Vineyard

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