{"refrec":{"BRefID":345462,"RR":"<b>Helsen, K.; Bassi, L.; Feilhauer, H.; Kattenborn, T.; Matsushima, H.; Van Cleemput, E.; Somers, B.; Honnay, O.</b> (2021). Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance. <i>Ecol. Indic. 130</i>: 108111. <a href=\"https://dx.doi.org/10.1016/j.ecolind.2021.108111\" target=\"_blank\">https://dx.doi.org/10.1016/j.ecolind.2021.108111</a>","BEntID":342097,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Ecol. Indic. 130</i>: 108111. <a href=\"https://dx.doi.org/10.1016/j.ecolind.2021.108111\" target=\"_blank\">https://dx.doi.org/10.1016/j.ecolind.2021.108111</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Helsen, K.; Bassi, L.; Feilhauer, H.; Kattenborn, T.; Matsushima, H.; Van Cleemput, E.; Somers, B.; Honnay, O.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Helsen, K. <i>et al.</i>","Englishabstract":"Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (<i>Hieracium umbellatum</i> and <i>Jacobaea vulgaris</i>) and two shrubs (<i>Rosa rugosa</i> and <i>Rubus caesius</i>), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R<SUP>2</SUP> > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R<SUP>2</SUP> < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R<SUP>2</SUP> < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R<SUP>2</SUP> < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R<SUP>2</SUP> > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-19 01:32:13.376898","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Intraspecific trait variation; Leaf dry matter content; Leaf mass per area; Leaf water content; Equivalent water thickness; Partial least squares regression (PLSR); PROSPECT","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2021-09-28","DateCreate":"2021-09-28","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000693457200003","VABBcode":null,"OpenAcc":1,"DOI":"10.1016/j.ecolind.2021.108111"},"refs":null,"anarec":{"AnaID":345462,"PubliDate":2021,"Pagination":"108111","XtraPublOfAnaID":null,"ISBN":null,"Volume":"130","Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":45477,"SerRR":"Ecological Indicators. 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