摘要
Hyperspectral reflectance contains valuable information about leaf functional traits,which can indicate a plant's physiological status.Therefore,using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments.We evaluated the use of 2 nondestructive methods(i.e.,leaf and proximal/canopy)measuring hyperspectral reflectance in the 350-to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic.Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view.The proximal canopy measurements were collected under natural solar light,using the same spectroradiometer with fiber optical cable to collect data on individual seedlings'hemispherical conical reflectance factor.The latter method was highly susceptible to changes in incoming radiation.Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range.Moreover,using random forest and support vector machine learning algorithms,the proximal data obtained from the top of the seedlings offered up to 83%accuracy in predicting 3 different Scots pine populations.We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
基金
funded mainly by the Ministry of Education,Youth and Sports of the Czech Republic,scheme INTER-EXCELLENCE,INTER-ACTION,grant number LTA-USA19113,titled“Genetic variability of hyper-spectral reflectance in Scots pine ecotypes for selection of drought-resistant individuals”.This project was coordinated with U.S.partners:P.Campbell and J.Brawner
funding from the European Union's Horizon Europe Research and Innovation Programme under grant agreement no:101081774-OptFORESTS
supported by NASA,LCLUC Program NNH17ZDA001N-LCLUC,grant no:80NSS-C18K0337,titled“Prototyping MuSLI canopy chlorophyl content for assessment of vegetation function and productivity”
supported by the Academy of Finland Flagship on Photonics Research and Innovation(PREIN,320166)
