Applications of radiative transfer models to greenhouse vegetation
| Autor: | J.A. Dieleman, Gerrit Polder, Nastassia Rajh Vilfan, A. Elings |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Canopy
Pigment content Crop Physiology Leaf reflectance Fluspect Hyperspectral imaging Greenhouse Vegetation Horticulture Photosynthesis Atmospheric sciences PE&RC Tomato chemistry.chemical_compound chemistry Chlorophyll Hyperspectral imagery GTB Tuinbouw Technologie Radiative transfer Environmental science Gewasfysiologie Chlorophyll fluorescence |
| Zdroj: | Acta Horticulturae 1296 (2020) Acta Horticulturae, 1296, 357-362 |
| ISSN: | 0567-7572 |
| Popis: | In greenhouse horticulture, efficiency of climate control and plant protection can be improved by having an accurate impression of plant status, such as photosynthesis or chemical composition. Recent advances in remote sensing technologies have brought about a range of innovations in precision agriculture, with the potential for adaptation to greenhouses. Simple, traditionally used indices employ only one or two spectral bands, in which the contributions of various pigments and leaf or canopy structure can highly overlap. Consequently, such indices may be insufficient for applications. State-of-the-art models have been developed that can better interpret hyper- and multispectral leaf and canopy imagery by employing the biochemical and radiative transfer properties of vegetation. An example is the soil-canopy observation of photosynthesis and energy balance (SCOPE) model, which was developed specifically for crop canopies. Here we present one of the pillars of SCOPE, the leaf radiative transfer (RT) model Fluspect. Fluspect simulates leaf chlorophyll fluorescence, reflectance and transmittance spectra. The model can be inverted to obtain estimates of leaf chlorophylls, carotenoids, anthocyanins, xanthophyll epoxidation, water and dry matter content. Moreover, it can be linked to a model for leaf photosynthesis and when inverted, provide a method to estimate photosynthesis directly from leaf spectral information. We test the model against a tomato data set, with measured hyperspectral images, chlorophyll, sugar, acid, starch, dry matter content and nutrients. The first study of the data set, using partial least square regression, showed that hyperspectral images have a high correlation with important fruit and leaf compounds. We compared these results to Fluspect retrievals and conventional vegetation indices. In the paper, we discuss the potential added value of using RT models in greenhouse horticulture. |
| Databáze: | OpenAIRE |
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