FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2957520121> ?p ?o ?g. }

• W2957520121 endingPage "307" @default.
• W2957520121 startingPage "297" @default.
• W2957520121 abstract "The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change, food security and soil degradation. Reflectance spectroscopy has proven to be a promising technique for SOC quantification in the laboratory and increasingly from air- and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales with regular updates. When applied on larger scales, soil prediction accuracy decreases due to the inhomogeneity of samples. In this paper, we examined if spectral clustering of the LUCAS EU-wide topsoil database is successful without using other covariates than the spectral database and can improve SOC model performance compared to a reference model that was calibrated on the whole database without clustering. Different clustering methodologies were tested, including a k-means clustering based on principal component analysis or based on spectral feature variables, combined with partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach which builds a different multivariate model for each sample to be predicted. Furthermore, in order to allow for subsequent application to hyperspectral remote sensing data, atmospheric water wavelengths were removed from the analyses. The local PLSR approach achieved best results and was additionally applied to LUCAS spectra resampled to the upcoming hyperspectral EnMAP sensor which led to good results: R2 = 0.66, RMSEP = 5.78 g kg−1 and RPIQ = 1.93. The k-means clustering approach showed slightly better results than the reference model. Overall, our results showed similar performances for SOC prediction models compared to other approaches using PLSR with a larger spectral range and other soil parameters as covariates. This study shows that (i) it is possible to transfer the local PLSR approach onto a wavelengths reduced spectral library and to predict estimations of SOC at low-cost with reasonable accuracy based on large scale soil databases; and (ii) that the local regression approach is a valuable tool for SOC prediction models based solely on spectral data without the use of other soil covariates." @default.
• W2957520121 created "2019-07-23" @default.
• W2957520121 creator A5033345623 @default.
• W2957520121 creator A5050373839 @default.
• W2957520121 creator A5071624635 @default.
• W2957520121 creator A5074016805 @default.
• W2957520121 date "2019-11-01" @default.
• W2957520121 modified "2023-09-26" @default.
• W2957520121 title "A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database" @default.
• W2957520121 cites W1592341820 @default.
• W2957520121 cites W1772504446 @default.
• W2957520121 cites W1973273412 @default.
• W2957520121 cites W1986938560 @default.
• W2957520121 cites W1992800046 @default.
• W2957520121 cites W1993720070 @default.
• W2957520121 cites W1993971389 @default.
• W2957520121 cites W2010212234 @default.
• W2957520121 cites W2013665199 @default.
• W2957520121 cites W2016376046 @default.
• W2957520121 cites W2017422910 @default.
• W2957520121 cites W2020337281 @default.
• W2957520121 cites W2043359577 @default.
• W2957520121 cites W2048941377 @default.
• W2957520121 cites W2054603652 @default.
• W2957520121 cites W2066626803 @default.
• W2957520121 cites W2068377555 @default.
• W2957520121 cites W2072505961 @default.
• W2957520121 cites W2073503722 @default.
• W2957520121 cites W2077076389 @default.
• W2957520121 cites W2083041300 @default.
• W2957520121 cites W2101507827 @default.
• W2957520121 cites W2103766443 @default.
• W2957520121 cites W2109606373 @default.
• W2957520121 cites W2130496888 @default.
• W2957520121 cites W2144189317 @default.
• W2957520121 cites W2148393497 @default.
• W2957520121 cites W2158551840 @default.
• W2957520121 cites W2168770424 @default.
• W2957520121 cites W2196579671 @default.
• W2957520121 cites W2292439029 @default.
• W2957520121 cites W2329778734 @default.
• W2957520121 cites W2484762992 @default.
• W2957520121 cites W2768533279 @default.
• W2957520121 cites W2907522931 @default.
• W2957520121 doi "https://doi.org/10.1016/j.geoderma.2019.07.010" @default.
• W2957520121 hasPublicationYear "2019" @default.
• W2957520121 type Work @default.
• W2957520121 sameAs 2957520121 @default.
• W2957520121 citedByCount "35" @default.
• W2957520121 countsByYear W29575201212020 @default.
• W2957520121 countsByYear W29575201212021 @default.
• W2957520121 countsByYear W29575201212022 @default.
• W2957520121 countsByYear W29575201212023 @default.
• W2957520121 crossrefType "journal-article" @default.
• W2957520121 hasAuthorship W2957520121A5033345623 @default.
• W2957520121 hasAuthorship W2957520121A5050373839 @default.
• W2957520121 hasAuthorship W2957520121A5071624635 @default.
• W2957520121 hasAuthorship W2957520121A5074016805 @default.
• W2957520121 hasBestOaLocation W29575201212 @default.
• W2957520121 hasConcept C119857082 @default.
• W2957520121 hasConcept C124101348 @default.
• W2957520121 hasConcept C154945302 @default.
• W2957520121 hasConcept C159078339 @default.
• W2957520121 hasConcept C159390177 @default.
• W2957520121 hasConcept C159750122 @default.
• W2957520121 hasConcept C166957645 @default.
• W2957520121 hasConcept C182124840 @default.
• W2957520121 hasConcept C20529654 @default.
• W2957520121 hasConcept C205649164 @default.
• W2957520121 hasConcept C22354355 @default.
• W2957520121 hasConcept C27438332 @default.
• W2957520121 hasConcept C2779343474 @default.
• W2957520121 hasConcept C39432304 @default.
• W2957520121 hasConcept C39464130 @default.
• W2957520121 hasConcept C41008148 @default.
• W2957520121 hasConcept C62649853 @default.
• W2957520121 hasConcept C73555534 @default.
• W2957520121 hasConcept C77088390 @default.
• W2957520121 hasConceptScore W2957520121C119857082 @default.
• W2957520121 hasConceptScore W2957520121C124101348 @default.
• W2957520121 hasConceptScore W2957520121C154945302 @default.
• W2957520121 hasConceptScore W2957520121C159078339 @default.
• W2957520121 hasConceptScore W2957520121C159390177 @default.
• W2957520121 hasConceptScore W2957520121C159750122 @default.
• W2957520121 hasConceptScore W2957520121C166957645 @default.
• W2957520121 hasConceptScore W2957520121C182124840 @default.
• W2957520121 hasConceptScore W2957520121C20529654 @default.
• W2957520121 hasConceptScore W2957520121C205649164 @default.
• W2957520121 hasConceptScore W2957520121C22354355 @default.
• W2957520121 hasConceptScore W2957520121C27438332 @default.
• W2957520121 hasConceptScore W2957520121C2779343474 @default.
• W2957520121 hasConceptScore W2957520121C39432304 @default.
• W2957520121 hasConceptScore W2957520121C39464130 @default.
• W2957520121 hasConceptScore W2957520121C41008148 @default.
• W2957520121 hasConceptScore W2957520121C62649853 @default.
• W2957520121 hasConceptScore W2957520121C73555534 @default.
• W2957520121 hasConceptScore W2957520121C77088390 @default.
• W2957520121 hasFunder F4320323803 @default.

• next