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Title:

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Document type:
Zeitschriftenaufsatz
Author(s):
Yang, Haibo; Li, Fei; Wang, Wei; Yu, Kang
Abstract:
Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral...     »
Journal title:
Remote Sensing
Year:
2021
Journal volume:
13
Journal issue:
12
Pages contribution:
2339
Fulltext / DOI:
doi:10.3390/rs13122339
Publisher:
MDPI AG
E-ISSN:
2072-4292
Date of publication:
15.06.2021
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