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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
Remote Sensing
Jahr:
2021
Band / Volume:
13
Heft / Issue:
12
Seitenangaben Beitrag:
2339
Volltext / DOI:
doi:10.3390/rs13122339
Verlag / Institution:
MDPI AG
E-ISSN:
2072-4292
Publikationsdatum:
15.06.2021
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