Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm cap...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 14; S. 3690
Hauptverfasser:	Akbari, Elahe, Boloorani, Ali Darvishi, Verrelst, Jochem, Pignatti, Stefano, Neysani Samany, Najmeh, Soufizadeh, Saeid, Hamzeh, Saeid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.07.2023
Schlagworte:	Accuracy Agricultural land Algorithms Artificial neural networks Biomass biophysical variables Comparative analysis Confidence intervals Corn Distribution Farms fractional vegetation cover Gaussian process Gaussian process regression Gene mapping Genetic algorithms Growing season Hydrology Identification and classification Iran irrigation management Kernels Leaf area Leaf area index Learning algorithms Leaves Machine learning machine learning regression algorithms Measurement Methods Neural networks normal distribution Optimization optimization algorithm Particle swarm optimization Phenology Photography prediction Regression Remote sensing Satellite imaging Seasons Silage Simulated annealing Statistical analysis summer Tabu search Uncertainty Variables Vegetation Vegetation cover Water management Iran
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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