Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance

Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various...

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Bibliographic Details
Main Authors: Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Remote Sensing
Subjects:
chlorophyll estimation
hyperspectral reflectance
random forest regression
phenological stages
vegetation indices
cotton
Online Access:https://www.mdpi.com/2072-4292/17/15/2713
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