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
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Zusammenfassung: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 capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15143690