FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1598575158> ?p ?o ?g. }

• W1598575158 endingPage "9370" @default.
• W1598575158 startingPage "9347" @default.
• W1598575158 abstract "Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together." @default.
• W1598575158 created "2016-06-24" @default.
• W1598575158 creator A5003044369 @default.
• W1598575158 creator A5031882613 @default.
• W1598575158 creator A5039052506 @default.
• W1598575158 creator A5079448372 @default.
• W1598575158 creator A5085480844 @default.
• W1598575158 creator A5087731602 @default.
• W1598575158 date "2015-07-22" @default.
• W1598575158 modified "2023-10-16" @default.
• W1598575158 title "An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning" @default.
• W1598575158 cites W1744313060 @default.
• W1598575158 cites W1970664916 @default.
• W1598575158 cites W1972539809 @default.
• W1598575158 cites W1973333099 @default.
• W1598575158 cites W1977046327 @default.
• W1598575158 cites W1977691607 @default.
• W1598575158 cites W1978160572 @default.
• W1598575158 cites W1989302661 @default.
• W1598575158 cites W1990255627 @default.
• W1598575158 cites W1991276594 @default.
• W1598575158 cites W2000485836 @default.
• W1598575158 cites W2005034723 @default.
• W1598575158 cites W2007342648 @default.
• W1598575158 cites W2009517474 @default.
• W1598575158 cites W2013061102 @default.
• W1598575158 cites W2019965926 @default.
• W1598575158 cites W2025401351 @default.
• W1598575158 cites W2028141358 @default.
• W1598575158 cites W2028294158 @default.
• W1598575158 cites W2043673805 @default.
• W1598575158 cites W2049205358 @default.
• W1598575158 cites W2051128904 @default.
• W1598575158 cites W2059632352 @default.
• W1598575158 cites W2073100112 @default.
• W1598575158 cites W2077336029 @default.
• W1598575158 cites W2078840559 @default.
• W1598575158 cites W2079630883 @default.
• W1598575158 cites W2081887174 @default.
• W1598575158 cites W2101695001 @default.
• W1598575158 cites W2107325836 @default.
• W1598575158 cites W2121025745 @default.
• W1598575158 cites W2125107816 @default.
• W1598575158 cites W2127632654 @default.
• W1598575158 cites W2131126673 @default.
• W1598575158 cites W2132328937 @default.
• W1598575158 cites W2133353144 @default.
• W1598575158 cites W2133751300 @default.
• W1598575158 cites W2136876992 @default.
• W1598575158 cites W2136904244 @default.
• W1598575158 cites W2152164823 @default.
• W1598575158 cites W2157582727 @default.
• W1598575158 cites W2158863190 @default.
• W1598575158 cites W2160068471 @default.
• W1598575158 cites W2162421262 @default.
• W1598575158 cites W2166312616 @default.
• W1598575158 cites W2167894638 @default.
• W1598575158 cites W2168082801 @default.
• W1598575158 cites W2169614522 @default.
• W1598575158 cites W2911964244 @default.
• W1598575158 cites W3098421274 @default.
• W1598575158 cites W568010113 @default.
• W1598575158 cites W633320881 @default.
• W1598575158 doi "https://doi.org/10.3390/rs70709347" @default.
• W1598575158 hasPublicationYear "2015" @default.
• W1598575158 type Work @default.
• W1598575158 sameAs 1598575158 @default.
• W1598575158 citedByCount "58" @default.
• W1598575158 countsByYear W15985751582016 @default.
• W1598575158 countsByYear W15985751582017 @default.
• W1598575158 countsByYear W15985751582018 @default.
• W1598575158 countsByYear W15985751582019 @default.
• W1598575158 countsByYear W15985751582020 @default.
• W1598575158 countsByYear W15985751582021 @default.
• W1598575158 countsByYear W15985751582022 @default.
• W1598575158 countsByYear W15985751582023 @default.
• W1598575158 crossrefType "journal-article" @default.
• W1598575158 hasAuthorship W1598575158A5003044369 @default.
• W1598575158 hasAuthorship W1598575158A5031882613 @default.
• W1598575158 hasAuthorship W1598575158A5039052506 @default.
• W1598575158 hasAuthorship W1598575158A5079448372 @default.
• W1598575158 hasAuthorship W1598575158A5085480844 @default.
• W1598575158 hasAuthorship W1598575158A5087731602 @default.
• W1598575158 hasBestOaLocation W15985751581 @default.
• W1598575158 hasConcept C11413529 @default.
• W1598575158 hasConcept C119857082 @default.
• W1598575158 hasConcept C121332964 @default.
• W1598575158 hasConcept C127313418 @default.
• W1598575158 hasConcept C154945302 @default.
• W1598575158 hasConcept C199360897 @default.
• W1598575158 hasConcept C199390426 @default.
• W1598575158 hasConcept C2777655017 @default.
• W1598575158 hasConcept C41008148 @default.
• W1598575158 hasConcept C62520636 @default.
• W1598575158 hasConcept C62649853 @default.
• W1598575158 hasConcept C74902906 @default.
• W1598575158 hasConceptScore W1598575158C11413529 @default.
• W1598575158 hasConceptScore W1598575158C119857082 @default.

• next