A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellit...

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Bibliographic Details
Main Authors: Oz Kira, Jiaming Wen, Jimei Han, Andrew J McDonald, Christopher B Barrett, Ariel Ortiz-Bobea, Yanyan Liu, Liangzhi You, Nathaniel D Mueller, Ying Sun
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Environmental Research Letters
Subjects:
solar-induced chlorophyll fluorescence (SIF)
crop yield
mechanistic light reactions
agricultural monitoring
satellite remote sensing
machine learning
Online Access:https://doi.org/10.1088/1748-9326/ad3142

Internet

https://doi.org/10.1088/1748-9326/ad3142

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