Advancing Airborne Hyperspectral Data Processing and Applications for Sustainable Agriculture Using RTM-Based Machine Learning
Author:
Affiliation:
1. University of Illinois at Urbana,Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment,Champaign,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10283455.pdf?arnumber=10283455
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1. A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops
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3. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle
4. Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators
5. Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning
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