Abstract

AbstractQuestionsEffective and successful restoration of species‐rich semi‐natural grasslands requires knowledge of the soil nutrient status, including soil phosphorus availability. Plants are solid indicators of soil nutrient status, because their growth reflects nutrient availability integrated over a certain period. The use of reflectance spectroscopy to estimate vegetation nutrient content has the potential to offer a fast and efficient approach to provide information about soil nutrient availability for plants. Here, we investigated the effect and relative importance of vegetation phosphorus content compared with vegetation nitrogen content and species identity in explaining variation in vegetation reflectance.LocationPot experiment mimicking Western European grassland communities on a restoration trajectory.MethodsWe combined a pot experiment with a broad range of mesotrophic grassland species growing along a soil phosphorus gradient with a multivariate modelling approach. We measured vegetation spectra and vegetation phosphorus and nitrogen content in monocultures and mixtures.ResultsAlthough vegetation biochemistry explained a considerable part of the variation in vegetation reflectance, we found no pronounced absorption features for vegetation nitrogen and phosphorus content across the reflectance spectrum. The relatively large effect of species and community identity suggest that other drivers, for example vegetation architecture, overruled the effect of vegetation biochemistry on the reflectance spectrum. Our findings indicate that species detection and indirect case‐specific estimation of nutrients is possible, especially in structurally less‐complex canopies such as monospecific grass swards.ConclusionsDisentangling the specific drivers of variation in spectral reflectance is challenging. Many studies confound the effect of species identity on the vegetation reflectance spectrum with the effect of vegetation biochemistry. Here, we showed the importance of explicitly taking species identity into account. Gaining insight into light–vegetation interactions and the in‐depth integration of ecological theory in remote sensing are the way forward.

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