Data used for assessing model prediction of plant traits under spatial dependency using hyperspectral data. Accuracy of spatial and non-spatial models such as machine learning when using hyperspectral data as predictors
Question: Do spatial gradients of plant strategies correspond to patterns of plant traits obtained f...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
Data used for assessing model prediction of plant traits under spatial dependency using hyperspectra...
Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ pl...
The data is a collection of generated leaf area index (LAI) with increasing levels of spatial depend...
Data from remote sensing with finer spectral and spatial resolution are increasingly available. Whil...
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as...
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as...
While Hyperspectral Imaging (HSI) has been successfully applied for remote monitoring of vegetation,...
The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging...
The machine learning method, random forest (RF), is applied in order to derive biophysical and struc...
Applying a calibration model onto hyperspectral (HS) images is of great interest because it produces...
In Proceedings of the VII Congress of the European Society for Agronomy, Cordoba, Spain, 15-18th Jul...
Hyperspectral (HS) data of vegetation provide a wealth of detailed information to identify plant nut...
Question: Do spatial gradients of plant strategies correspond to patterns of plant traits obtained f...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
Data used for assessing model prediction of plant traits under spatial dependency using hyperspectra...
Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ pl...
The data is a collection of generated leaf area index (LAI) with increasing levels of spatial depend...
Data from remote sensing with finer spectral and spatial resolution are increasingly available. Whil...
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as...
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as...
While Hyperspectral Imaging (HSI) has been successfully applied for remote monitoring of vegetation,...
The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging...
The machine learning method, random forest (RF), is applied in order to derive biophysical and struc...
Applying a calibration model onto hyperspectral (HS) images is of great interest because it produces...
In Proceedings of the VII Congress of the European Society for Agronomy, Cordoba, Spain, 15-18th Jul...
Hyperspectral (HS) data of vegetation provide a wealth of detailed information to identify plant nut...
Question: Do spatial gradients of plant strategies correspond to patterns of plant traits obtained f...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains ...
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