Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Biogeosciences, 14, 733-749, 2017
https://doi.org/10.5194/bg-14-733-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
15 Feb 2017
Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
Bob van der Meij1, Lammert Kooistra2, Juha Suomalainen2, Janna M. Barel3, and Gerlinde B. De Deyn3 1Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, the Netherlands
2Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
3Department of Soil Quality, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE  =  5.12 cm, R2  =  0.79), chlorophyll content (RMSE  =  0.11 g m−2, R2  =  0.80), N-content (RMSE  =  1.94 g m−2, R2  =  0.68), and fresh biomass (RMSE  =  0.72 kg m−2, R2  =  0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

Citation: van der Meij, B., Kooistra, L., Suomalainen, J., Barel, J. M., and De Deyn, G. B.: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors, Biogeosciences, 14, 733-749, https://doi.org/10.5194/bg-14-733-2017, 2017.
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Short summary
Plant–soil feedback (PSF) is an important mechanism to explain plant performance in natural and agricultural systems but is hard to quantify in field experiments. We used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way at high resolution using UAV-based optical sensors.
Plant–soil feedback (PSF) is an important mechanism to explain plant performance in natural and...
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