Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning a...

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Bibliographic Details
Main Authors: Savittri Ratanopad Suwanlee, Dusadee Pinasu, Jaturong Som-ard, Enrico Borgogno-Mondino, Filippo Sarvia
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Remote Sensing
Subjects:
sugarcane
aboveground biomass
carbon stock
remote sensing
earth observation
time series
Online Access:https://www.mdpi.com/2072-4292/16/5/750

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