DOI: 10.5061/dryad.75k1d
title: Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

description: 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.
description: van der Meij et
 al_Biogeosciences2017_dataThe experimental set-up,
 treatments, data collection and data analyses are thoroughly described in
 the Biogeoscience manuscript ‘Remote sensing of plant trait responses to
 field-based plant-soil feedback using UAV-based optical sensors’
 doi:10.5194/bg-2016-452. Therefore we refer to the manuscript for detailed
 information an here we provide a brief summary to enable readers to follow
 what the data entail. The data were collected from a 2-year field
 experiment with plant rotations in a full factorial design. The plant
 treatments we focused on are legacy effects of the plant treatments
 (listed below) to the following oat crop. In this oat crop we quantified
 several plant traits both in situ and via remote sensing by use of UAV and
 hyperspectral and EGB sensors. The experiment was set-up in five
 randomized field blocks. We used part of the in situ collected data to
 parameterize the hyperspectral data based models and we validated these
 models with the other half of the field plots. Plant treatments Fa= fallow
 Lp= Lolium perenne Rs= Raphanus sativus Tr= Trifolium repens Vs= Vicia
 sativa Lp+Tr= 50:50 species mixture (relative to the monoculture seed
 densities) of the species Lp and Tr Rs+Vs= 50:50 species mixture (relative
 to the monoculture seed densities) of the species Rs and
 Vs