Detecting the response of plant functional traits to nutrient status in grassland by spectral reflectance measurements
Final Report Abstract
The project has been an integral part of activities on further developing a scientific basis for RS on grassland together with the University of Cologne, University of Karlsruhe, and colleagues from the Centre for Remote Sensing of the Land Surface in Bonn. Results indicate that hyperspectral RS can deliver valuable information on PFT composition of grassland as affected by nutrient supply. This is especially important because nutrient supply is one of the most important management tools in grassland agriculture. Further, as trait composition of crops strongly interacts with nutrients and soil conditions, RS will allow estimates of grassland ecosystem properties, functions and services. The integration of RS into the surveying and assessment for its ecosystem services on grassland is yet not fully explored, but the central role is well reviewed. Any contribution of RS on monitoring agricultural land surfaces should be based on a functional concept, rather than on empirical and statistical relationships only. In this sense this project contributed to explaining the link between RS and ecology as follows: Experiments 1, 2 and 3 provided a sound basis for estimating functional composition of grassland by RS. The plots in the RGE can be seen as communities of optical types in the sense of Ustin and Gamon (2010), which typically interact with solar radiation that is partly reflected and then sensed by the spectrometer. Different growth strategies of plants as induced by fertilizer application lead to different PFT composition and to different optical properties of plants in plots along the fertilizer gradient. Thus, the interrelation between physiological and physical processes and properties is highly relevant. Experiment 4 confirmed the value of REIP to estimate the N status of grassland as found earlier for wheat (Mistele and Schmidthalter, 2008). The mathematical correlation between REIP and NNI is based on a functional relationship between light absorption by chlorophyll, reflectance in chlorophyll absorption bands, as well as between leaf area and NIR reflectance. It further relies on the CNC which is also well explained by N uptake and partitioning. As for PFT detection in the RGE, the same interrelation holds between physiology of plants with physics of radiation absorption and reflectance. This project has been accompanied by a series of activities that focused on the role of PFT in Functional Ecology (see publications and theses listed below), thus providing a better understanding and the required skills to link RS with agronomy and biology of grass crops. Insofar this project has been highly interdisciplinary. The Rengen Grassland Experiment (RGE) is one of the oldest long-term experiments on Earth. Its scientific output is well documented since many years. The international consortium currently collaborating on the RGE believes that such long-term experiments require strong support and better integration into a national network of such sites.
Publications
- (2012). Plant functional traits and nutrient gradients on grassland. Grass and Forage Science
Schellberg, J., Da S. Pontes, L.S.
(See online at https://doi.org/10.1111/j.1365-2494.2012.00867.x) - (2014). Frontiers and perspectives on research strategies in grassland technology. Crop and Pasture Science 65, 508-623
Schellberg, J., Verbruggen, E.
(See online at https://doi.org/10.1071/CP13429) - (2015). Grass strategies and grassland community responses to environmental drivers: a review. Agronomy for Sustainable Development 35, 1297-1318
Da S. Pontes, L.S., Maire, V., Schellberg, J., Louault, F.
(See online at https://doi.org/10.1007/s13593-015-0314-1) - (2015). Improved estimation of nitrogen uptake in grasslands using the nitrogen dilution curve. Agronomy for Sustainable Development 35, 1561-1570
Reyes, J., Schellberg, J., Siebert, S., Elsaesser, M., Adam, J., Ewert, F.
(See online at https://doi.org/10.1007/s13593-015-0321-2) - (2017). Distinguishing intensity levels of grassland fertilization using vegetation indices. Remote Sensing 9, 81
Hollberg, J.H., Schellberg, J.
(See online at https://doi.org/10.3390/rs9010081)
