Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops

[Display omitted] With an upcoming unprecedented stream of imaging spectroscopy data, there is a rising need for tools and software applications exploiting the spectral possibilities to extract relevant information on an operational basis. In this study, we investigate the potential of a scientific...

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Vydáno v:	ISPRS journal of photogrammetry and remote sensing Ročník 173; s. 278 - 296
Hlavní autoři:	Danner, Martin, Berger, Katja, Wocher, Matthias, Mauser, Wolfram, Hank, Tobias
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.03.2021
Témata:	computer software corn crops data collection grasslands Grid search hybrids Hyperspectral remote sensing leaf angle leaf area index leaf chlorophyll content Machine learning neural networks normal distribution photogrammetry radiative transfer Radiative transfer model Reflectance modelling satellites SPARC spatial data specific leaf weight spectroscopy support vector machines ANN Radiative transfer model SPARC MLPR (r)RMSE Grid search GPR Cm RTM NDVI SVR RBF SPARC03 LIDF ALIA Hyperspectral remote sensing ML RFR Tanh MSI Cab LUT EnMAP SNAP Reflectance modelling PCA LAI Machine learning ReLU RQ
ISSN:	0924-2716, 1872-8235
On-line přístup:	Získat plný text
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