Research article
15 Feb 2017
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 Meij et al.
Related authors
AUTOMATIC APPLE TREE BLOSSOM ESTIMATION FROM UAV RGB IMAGERY
A. Tubau Comas, J. Valente, and L. Kooistra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 631–635, https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019, 2019
OPPORTUNITIES OF UAVS IN ORCHARD MANAGEMENT
C. Zhang, J. Valente, L. Kooistra, L. Guo, and W. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 673–680, https://doi.org/10.5194/isprs-archives-XLII-2-W13-673-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-673-2019, 2019
Soil fauna: key to new carbon models
Juliane Filser, Jack H. Faber, Alexei V. Tiunov, Lijbert Brussaard, Jan Frouz, Gerlinde De Deyn, Alexei V. Uvarov, Matty P. Berg, Patrick Lavelle, Michel Loreau, Diana H. Wall, Pascal Querner, Herman Eijsackers, and Juan José Jiménez
SOIL, 2, 565–582, https://doi.org/10.5194/soil-2-565-2016,https://doi.org/10.5194/soil-2-565-2016, 2016
Short summary
Mitigation of agricultural emissions in the tropics: comparing forest land-sparing options at the national level
S. Carter, M. Herold, M. C. Rufino, K. Neumann, L. Kooistra, and L. Verchot
Biogeosciences, 12, 4809–4825, https://doi.org/10.5194/bg-12-4809-2015,https://doi.org/10.5194/bg-12-4809-2015, 2015
Short summary
Related subject area
Microclimatic conditions and water content fluctuations experienced by epiphytic bryophytes in an Amazonian rain forest
Nina Löbs, David Walter, Cybelli G. G. Barbosa, Sebastian Brill, Rodrigo P. Alves, Gabriela R. Cerqueira, Marta de Oliveira Sá, Alessandro C. de Araújo, Leonardo R. de Oliveira, Florian Ditas, Daniel Moran-Zuloaga, Ana Paula Pires Florentino, Stefan Wolff, Ricardo H. M. Godoi, Jürgen Kesselmeier, Sylvia Mota de Oliveira, Meinrat O. Andreae, Christopher Pöhlker, and Bettina Weber
Biogeosciences, 17, 5399–5416, https://doi.org/10.5194/bg-17-5399-2020,https://doi.org/10.5194/bg-17-5399-2020, 2020
Short summary
Plant trait response of tundra shrubs to permafrost thaw and nutrient addition
Maitane Iturrate-Garcia, Monique M. P. D. Heijmans, J. Hans C. Cornelissen, Fritz H. Schweingruber, Pascal A. Niklaus, and Gabriela Schaepman-Strub
Biogeosciences, 17, 4981–4998, https://doi.org/10.5194/bg-17-4981-2020,https://doi.org/10.5194/bg-17-4981-2020, 2020
Short summary
Drought resistance increases from the individual to the ecosystem level in highly diverse Neotropical rainforest: a meta-analysis of leaf, tree and ecosystem responses to drought
Thomas Janssen, Katrin Fleischer, Sebastiaan Luyssaert, Kim Naudts, and Han Dolman
Biogeosciences, 17, 2621–2645, https://doi.org/10.5194/bg-17-2621-2020,https://doi.org/10.5194/bg-17-2621-2020, 2020
Short summary
Landsat NIR band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-451,https://doi.org/10.5194/bg-2019-451, 2019
Revised manuscript accepted for BG
Short summary
N : P stoichiometry and habitat effects on Mediterranean savanna seasonal root dynamics
Richard K. F. Nair, Kendalynn A. Morris, Martin Hertel, Yunpeng Luo, Gerardo Moreno, Markus Reichstein, Marion Schrumpf, and Mirco Migliavacca
Biogeosciences, 16, 1883–1901, https://doi.org/10.5194/bg-16-1883-2019,https://doi.org/10.5194/bg-16-1883-2019, 2019
Short summary
Rapid response of habitat structure and above-ground carbon storage to altered fire regimes in tropical savanna
Shaun R. Levick, Anna E. Richards, Garry D. Cook, Jon Schatz, Marcus Guderle, Richard J. Williams, Parash Subedi, Susan E. Trumbore, and Alan N. Andersen
Biogeosciences, 16, 1493–1503, https://doi.org/10.5194/bg-16-1493-2019,https://doi.org/10.5194/bg-16-1493-2019, 2019
Short summary
Dissolved organic matter characteristics of deciduous and coniferous forests with variable management: different at the source, aligned in the soil
Lisa Thieme, Daniel Graeber, Diana Hofmann, Sebastian Bischoff, Martin T. Schwarz, Bernhard Steffen, Ulf-Niklas Meyer, Martin Kaupenjohann, Wolfgang Wilcke, Beate Michalzik, and Jan Siemens
Biogeosciences, 16, 1411–1432, https://doi.org/10.5194/bg-16-1411-2019,https://doi.org/10.5194/bg-16-1411-2019, 2019
Short summary
Ecosystem responses to elevated CO2 using airborne remote sensing at Mammoth Mountain, California
Kerry Cawse-Nicholson, Joshua B. Fisher, Caroline A. Famiglietti, Amy Braverman, Florian M. Schwandner, Jennifer L. Lewicki, Philip A. Townsend, David S. Schimel, Ryan Pavlick, Kathryn J. Bormann, Antonio Ferraz, Emily L. Kang, Pulong Ma, Robert R. Bogue, Thomas Youmans, and David C. Pieri
Biogeosciences, 15, 7403–7418, https://doi.org/10.5194/bg-15-7403-2018,https://doi.org/10.5194/bg-15-7403-2018, 2018
Short summary
Ideas and perspectives: Tree–atmosphere interaction responds to water-related stem variations
Tim van Emmerik, Susan Steele-Dunne, Pierre Gentine, Rafael S. Oliveira, Paulo Bittencourt, Fernanda Barros, and Nick van de Giesen
Biogeosciences, 15, 6439–6449, https://doi.org/10.5194/bg-15-6439-2018,https://doi.org/10.5194/bg-15-6439-2018, 2018
Short summary
Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave
Milan Flach, Sebastian Sippel, Fabian Gans, Ana Bastos, Alexander Brenning, Markus Reichstein, and Miguel D. Mahecha
Biogeosciences, 15, 6067–6085, https://doi.org/10.5194/bg-15-6067-2018,https://doi.org/10.5194/bg-15-6067-2018, 2018
Short summary
Long-term dynamics of monoterpene synthase activities, monoterpene storage pools and emissions in boreal Scots pine
Anni Vanhatalo, Andrea Ghirardo, Eija Juurola, Jörg-Peter Schnitzler, Ina Zimmer, Heidi Hellén, Hannele Hakola, and Jaana Bäck
Biogeosciences, 15, 5047–5060, https://doi.org/10.5194/bg-15-5047-2018,https://doi.org/10.5194/bg-15-5047-2018, 2018
Short summary
The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia
Joshua P. Heyer, Mitchell J. Power, Robert D. Field, and Margreet J. E. van Marle
Biogeosciences, 15, 4317–4331, https://doi.org/10.5194/bg-15-4317-2018,https://doi.org/10.5194/bg-15-4317-2018, 2018
Short summary
Fungal loop transfer of nitrogen depends on biocrust constituents and nitrogen form
Zachary T. Aanderud, Trevor B. Smart, Nan Wu, Alexander S. Taylor, Yuanming Zhang, and Jayne Belnap
Biogeosciences, 15, 3831–3840, https://doi.org/10.5194/bg-15-3831-2018,https://doi.org/10.5194/bg-15-3831-2018, 2018
Short summary
Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning
Tommaso Jucker, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, Craig Brelsford, David F. R. P. Burslem, Nicolas J. Deere, Robert M. Ewers, Jakub Kvasnica, Simon L. Lewis, Yadvinder Malhi, Sol Milne, Reuben Nilus, Marion Pfeifer, Oliver L. Phillips, Lan Qie, Nathan Renneboog, Glen Reynolds, Terhi Riutta, Matthew J. Struebig, Martin Svátek, Edgar C. Turner, and David A. Coomes
Biogeosciences, 15, 3811–3830, https://doi.org/10.5194/bg-15-3811-2018,https://doi.org/10.5194/bg-15-3811-2018, 2018
Short summary
Thermal acclimation of leaf photosynthetic traits in an evergreen woodland, consistent with the coordination hypothesis
Henrique Fürstenau Togashi, Iain Colin Prentice, Owen K. Atkin, Craig Macfarlane, Suzanne M. Prober, Keith J. Bloomfield, and Bradley John Evans
Biogeosciences, 15, 3461–3474, https://doi.org/10.5194/bg-15-3461-2018,https://doi.org/10.5194/bg-15-3461-2018, 2018
Short summary
Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes
Victoria Meyer, Sassan Saatchi, David B. Clark, Michael Keller, Grégoire Vincent, António Ferraz, Fernando Espírito-Santo, Marcus V. N. d'Oliveira, Dahlia Kaki, and Jérôme Chave
Biogeosciences, 15, 3377–3390, https://doi.org/10.5194/bg-15-3377-2018,https://doi.org/10.5194/bg-15-3377-2018, 2018
Short summary
Fire intensity impacts on post-fire temperate coniferous forest net primary productivity
Aaron M. Sparks, Crystal A. Kolden, Alistair M. S. Smith, Luigi Boschetti, Daniel M. Johnson, and Mark A. Cochrane
Biogeosciences, 15, 1173–1183, https://doi.org/10.5194/bg-15-1173-2018,https://doi.org/10.5194/bg-15-1173-2018, 2018
Short summary
Bryophyte-dominated biological soil crusts mitigate soil erosion in an early successional Chinese subtropical forest
Steffen Seitz, Martin Nebel, Philipp Goebes, Kathrin Käppeler, Karsten Schmidt, Xuezheng Shi, Zhengshan Song, Carla L. Webber, Bettina Weber, and Thomas Scholten
Biogeosciences, 14, 5775–5788, https://doi.org/10.5194/bg-14-5775-2017,https://doi.org/10.5194/bg-14-5775-2017, 2017
Short summary
Parallel functional and stoichiometric trait shifts in South American and African forest communities with elevation
Marijn Bauters, Hans Verbeeck, Miro Demol, Stijn Bruneel, Cys Taveirne, Dries Van der Heyden, Landry Cizungu, and Pascal Boeckx
Biogeosciences, 14, 5313–5321, https://doi.org/10.5194/bg-14-5313-2017,https://doi.org/10.5194/bg-14-5313-2017, 2017
Short summary
Ecophysiological modeling of photosynthesis and carbon allocation to the tree stem in the boreal forest
Fabio Gennaretti, Guillermo Gea-Izquierdo, Etienne Boucher, Frank Berninger, Dominique Arseneault, and Joel Guiot
Biogeosciences, 14, 4851–4866, https://doi.org/10.5194/bg-14-4851-2017,https://doi.org/10.5194/bg-14-4851-2017, 2017
Short summary
Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators
Andreas Krause, Thomas A. M. Pugh, Anita D. Bayer, Jonathan C. Doelman, Florian Humpenöder, Peter Anthoni, Stefan Olin, Benjamin L. Bodirsky, Alexander Popp, Elke Stehfest, and Almut Arneth
Biogeosciences, 14, 4829–4850, https://doi.org/10.5194/bg-14-4829-2017,https://doi.org/10.5194/bg-14-4829-2017, 2017
Short summary
Detecting impacts of extreme events with ecological in situ monitoring networks
Miguel D. Mahecha, Fabian Gans, Sebastian Sippel, Jonathan F. Donges, Thomas Kaminski, Stefan Metzger, Mirco Migliavacca, Dario Papale, Anja Rammig, and Jakob Zscheischler
Biogeosciences, 14, 4255–4277, https://doi.org/10.5194/bg-14-4255-2017,https://doi.org/10.5194/bg-14-4255-2017, 2017
Short summary
Initial shifts in nitrogen impact on ecosystem carbon fluxes in an alpine meadow: patterns and causes
Bing Song, Jian Sun, Qingping Zhou, Ning Zong, Linghao Li, and Shuli Niu
Biogeosciences, 14, 3947–3956, https://doi.org/10.5194/bg-14-3947-2017,https://doi.org/10.5194/bg-14-3947-2017, 2017
Net ecosystem carbon exchange of a dry temperate eucalypt forest
Nina Hinko-Najera, Peter Isaac, Jason Beringer, Eva van Gorsel, Cacilia Ewenz, Ian McHugh, Jean-François Exbrayat, Stephen J. Livesley, and Stefan K. Arndt
Biogeosciences, 14, 3781–3800, https://doi.org/10.5194/bg-14-3781-2017,https://doi.org/10.5194/bg-14-3781-2017, 2017
Short summary
Growth responses of trees and understory plants to nitrogen fertilization in a subtropical forest in China
Di Tian, Peng Li, Wenjing Fang, Jun Xu, Yongkai Luo, Zhengbing Yan, Biao Zhu, Jingjing Wang, Xiaoniu Xu, and Jingyun Fang
Biogeosciences, 14, 3461–3469, https://doi.org/10.5194/bg-14-3461-2017,https://doi.org/10.5194/bg-14-3461-2017, 2017
Short summary
Cited articles
Aasen, H., Gnyp, M. L., Miao, Y., and Bareth, G.: Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with Hypercor, Photogramm. Eng. Rem. S., 80, 51–61, 2014.
Abdi, H.: Partial least squares regression and projection on latent structure regression (PLS Regression), Wiley Interdisciplinary Reviews: Computational Statistics 2.1, 97–106, 2010.
Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., and Fereres, E.: Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors, International Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 1–6, 2009.
Barel, J. M., Kuyper, T. W., de Boer, W., Douma, J. C., and De Deyn, G. B.: Legacy effects of winter cover crop mixtures on crop yield determined are driven by cover crop plant biomass and nitrogen concentration, under review, 2017.
Bever, J. D., Dickie, I. A., Facelli, E., Facelli, J. M., Klironomos, J., Moora, M., Rillig, M. C., Stock, W. D., Tibbett, M., and Zobel, M.: Rooting theories of plant community ecology in microbial interactions, Trends Ecol. Evol., 25, 468–478, 2010.
Blackburn, G. A.: Quantifying Chlorophylls and Carotenoids at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches, Remote Sens. Environ., 66, 273–285, 1998.
Brinkman, E. P., van der Putten, W. H., Bakker, E., and Verhoeven, K. J. F.: Plant-soil feedback: experimental approaches, statistical analysis and ecological interpretations, J. Ecol., 98, 1063–1073, 2010.
Broge, N. H. and Leblanc, E.: Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sens. Environ., 76, 156–172, 2000.
Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., and Suomalainen, J.: Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches, ISPRS Int. J. Geo-Inf., 4, 2792–2820, 2015.
Chapman, S. C., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M. F., Holland, E., Zheng, B., Ling, T. J., and Jimenez-Berni, J.: Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping, Agronomy, 4, 279–301, 2014.
Chen, P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., and Li, B.: New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat, Remote Sens. Environ., 114, 1987–1997, 2010.
Cho, M. A., Skidmore, A., Corsi, F., van Wieren, S. E., and Sobhan, I.: Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression, International Journal of Applied Earth Observations and Geoinformation, 9, 414–424, 2007.
Christenson, B., Schapaugh Jr., W. T., An, N., Price, K. P., and Fritz, A. K.: Characterizing Changes in Soybean Spectral Response Curves with Breeding Advancements, Crop Science, 54, 1585–1597, 2013.
Chuvieco, E.: Fundamentals of Satellite Remote Sensing, New York: CRC Press, 2011.
Clevers, J. G. P. W. and Kooistra, L.: Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 574–583, 2012.
Colomina, I. and Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm., 92, 79–97, 2014.
Cornelissen, J. H. C., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich, D. E., Reich, P. B., ter Steege, H., Morgan, H. D., van der Heijden, M. G. A., Pausas, J. G., and Poorter, H.: A handbook of protocols for standardized and easy measurement of plant functional traits worldwide, Aust. J. Bot., 51, 335–380, 2003.
Cortois, R., Schröder-Georgi, T., Weigelt, A., van der Putten, W. H., and De Deyn, G. B.: Plant-soil feedbacks: role of plant functional group and plant traits, J. Ecol., 104, 1608–1617, 2016.
De Bello, F., Lavorel, S., Díaz, S., Harrington, R., Cornelissen, J. H. C., Bardgett, R. D., Berg, M. P., Cipriotti, P., Feld, C. K., Hering, D., Da Silva, P. M., Potts, S. G., Sandin, L., Sousa, J. P., Storkey, J., Wardle, D. A., and Harrison, P. A.: Towards an assessment of multiple ecosystem processes and services via functional traits, Biodivers. Conserv., 19, 2873–2893, 2010.
Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., Garnier, E., Bönisch, G., Westoby, M., Poorter, H., Reich, P. B., Moles, A. T., Dickie, J., Gillison, A. N., Zanne, A. E., Chave, J., Wright, S. J., Sheremet'ev, S. N., Jactel, H., Baraloto, C., Cerabolini, B., Pierce, S., Shipley, B., Kirkup, D., Casanoves, F., Joswig, J. S., Günther, A., Falczuk, V., Rüger, N., Mahecha, M. D., and Gorné, L. D.: The global spectrum of plant form and function, Nature, 529, 167–171, 2016.
Faye, E., Rebaudo, F., Yanez-Cajo, D., Cauvy-Fraunie, S., and Dangles, O.: A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics, Methods in Ecology and Evolution, 7, 437–446, 2016.
Franklin, S. E.: Remote Sensing for Sustainable Forest Management, CRC Press, New York, 2001.
Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Dolezal, J., Eriksson, O., Fortunel, C., Freitas, H., Golodets, C., Grigulis, K., Jouany, C., Kazakou, E., Kigel, J., Kleyer, M., Lehsten, V., Leps, J., Meier, T., Pakeman, R., Papadimitriou, M., Papanastasis, V., Quested, H., Quétier, F., Robson, T. M., Roumet, C., Rusch, G., Skarpe, C., Sternberg, M., Theau, J. P., Thébault, A., Vile, D., and Zarovali, M. P.: A standardized methodology to assess the effects of land use change on plant traits, communities and ecosystem functioning in grasslands, Ann. Bot., 99, 967–985, 2007.
Gitelson, A. A.: Nondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, and Anthocyanin) Contents: Evaluating a Semianalytical Three-Band Model, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., and Huete, A., CRC Press, New York, 141–166, 2012.
Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., and Leavitt, B.: Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophys. Res. Lett., 30, 52–55, 2003.
Goswami, A., Gamon, J. A., Vargas, S., and Tweedie, C. E.: Relationship of NDVI, biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska, PeerJ PrePrints, 3, e1127, https://doi.org/10.7287/peerj.preprints.913v1, 2015.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L.: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sens. Environ., 81, 416–426, 2002.
Haghighattalab, A., González Pérez, L., Mondal S., Singh, D., Schinstock, D., Rutkoski, J., Ortiz-Monasterio, I., Singh, R. P., Goodin, D., and Poland, J.: Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries, Plant Methods, 12, 35–50, 2016.
Hansen, P. M. and Schjoerring, J. K.: Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression, Remote Sens. Environ., 86, 542–553, 2003.
Hardin, P. J. and Jensen, R. R.: Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities, GI Science & Remote Sensing, 48, 99–111, 2011.
Homolová, L., Malenovský, Z., Clevers, J. G. P. W., García-Santos, G., and Schaepman, M. E.: Review of optical-based remote sensing for plant trait mapping, Ecological Complexity, 15, 1–16, 2013.
Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., and Pesonen, L.: Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture, Remote Sens., 5, 5006–5039, 2013.
Hruska, R., Mitchell, J., Anderson, M., and Glenn, N. F.: Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle, Remote Sens., 4, 2736–2752, 2012.
Hunt Jr., E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., and Perry, E. M.: A visible band index for remote sensing leaf chlorophyll content at the canopy scale, International Journal of Applied Earth Observation and Geoinformation, 21, 103–112, 2013.
Kattge, J., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C., Blonder, B., Bond, W. J., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J. Q., Chapin, F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Duarte, L., Durka, W., Elser, J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernández-Méndez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R. V., Green, W. A., Gutierrez, A. G., Hickler, T., Higgins, S., Hodgson, J. G., Jalili, A., Jansen, S., Joly, C., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M. H., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J., Llusià, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B., Messier, J., Moles, A. T., Müller, S. C., Nadrowski, K., Naeem, S., Niinemets, Ü., Nöllert, S., Nüske, A., Ogaya, R., Oleksyn, J., Onipchenko, V. J., Onoda, Y., Ordoñez, J., Overbeck, G., Ozinga, W. A., Patiño, S., Paula, S., Pausas, J. G., Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Prinzing, A., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado-Negret, B., Sardans, J., Shiodera, S., Shipley, B., Siefert, A., Sosinski, E., Soussana, J.-F., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., White, S., Wright, S. J., Yguel, B., Zaehle, S., Zanne, A. E., and Wirth, C.: TRY – a global database of plant traits, Glob. Change Biol., 17, 2905–2935, 2011.
Kooistra, L., Suomalainen, J., Iqbal, S., Franke, J., Wenting, Ph., Bartholomeus, H., Mücher, S., and Becker, R.: Crop Monitoring Using a Light-Weight Hyperspectral Mapping System for Unmanned Aerial Vehicles: First Results for the 2013 Season, in: Proceedings of the Workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation, edited by: Bendig, J. and Bareth, G., Kölner Geographische Arbeiten, Cologne, 94, 51–58, 2014.
Kulmatiski, A. and Kardol, P.: Getting plant-soil feedbacks out of the greenhouse: experimental and conceptual approaches, in: Lüttge, U., Beyschlag, W., and Murata, J., Progress in Botany, Springer Berlin Heidelberg, 449–472, 2008.
Kulmatiski, A., Beard, K. H., Stevens, J. R., and Cobbold, S. M.: Plant-soil feedbacks: a meta-analytical review, Ecol. Lett., 11, 980–992, 2008.
Lamb, D. W., Steyn-Ross, M., Schaare, P., Hanna, M. M., Silvester, W., and Steyn-Ross, A.: Estimating leaf nitrogen concentration in ryegrass (
Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations, Int. J. Remote Sens., 23, 3619–3648, 2002.
Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S., and Baret, F.: Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots, Sensors, 8, 3557–3585, 2008.
Li, F., Miao, Y., and Chen, X.: Using Area-Based Spectral Indices to Estimate Aerial N Uptake of Maize, in: Proceedings of the Workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation, edited by: Bendig, J. and Bareth, G., Kölner Geographische Arbeiten, Cologne, 94, 59–65, 2014.
Malhi, S. S., Johnston, A. M., Schoenau, J. J., Wang, Z. L., and Vera, C. L.: Seasonal biomass accumulation and nutrient uptake of wheat, barley and oat on a Black Chernozem Soil in Saskatchewan, Can. J. Plant Sci., 86, 1005–1014, 2006.
Michaelsen, J., Schimel, D. S., Friedl, M. A., Davis, F. W., and Dubayah, R. C.: Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys, J. Veg. Sci., 5, 673–686, 1994.
Mulla, D. J.: Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, 101, 172–182, 2013.
Mutanga, O. and Skidmore, A. K.: Narrow band vegetation indices overcome the saturation problem in biomass estimation, Int. J. Remote Sens., 25, 3999–4014, 2004.
Nebiker, S., Annen, A., Scherrer, M., and Oesch, D.: A Light-Weight Multispectral sensor for Micro UAV – Opportunities for Very High Resolution Airborne Remote Sensing, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 1193–1199, 2008.
Netto, A. T., Campostrini, E., de Gonçalves, O. J., and Bressan-Smith, R. E.: Photosynthetic pigments, nitrogen, chlorophyll
a fluorescence and SPAD-502 readings in coffee leaves, Sci. Hortic., 104, 199–209, 2005.
Nguyen, H. T. and Lee, B.: Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression, Eur. J. Agron., 24, 349–356, 2006.
Ortenberg, F.: Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck Mounted; Integration of Hyperspectral Data with LIDAR, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., and Huete, A., CRC Press, New York, 39–68, 2012.
Orwin, K. H., Buckland, S. M., Johnson, D., Turner, B. L., Smart, S., Oakley, S., and Bardgett, R. D.: Linkages of plant traits to soil properties and the functioning of temperate grassland, J. Ecol., 98, 1074–1083, 2010.
Peinetti, H. R., Menezes, R. S. C., and Coughenour, M. B.: Changes induced by elk browsing in the aboveground biomass production and distribution of willow (Salix monticola Bebb): their relationships with plant water, carbon, and nitrogen dynamics, Oecologia, 127, 334–342, 2001.
Pinter Jr., P. J., Hathfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., and Upchurch, D. R.: Remote Sensing for Crop Management, Photogramm. Eng. Rem. S., 69, 647–664, 2003.
Qi, J., Inoue, Y., and Wiangwang, N.: Hyperspectral Remote Sensing in Global Change Studies, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., and Huete, A., CRC Press, New York, 70–89, 2012.
Rango, A., Laliberte, A., Herrick, J. E., Winters, C., Havstad, K., Steele, C., and Browning, D.: Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management, J. Appl. Remote Sens., 3, 1–15, 2009.
Rascher, U., Blossfeld, S., Fiorani, F., Jahnke, S., Jansen, M., Kuhn, A. J., Matsubara, S., Märtin, L. L. A., Merchant, A., Metzner, R., Müller-Linow, M., Nagel, K. A., Pieruschka, R., Pinto, F., Schreiber, C. M., Temperton, V. M., Thorpe, M. R., van Dusschoten, D., Volkenburgh, E. van, Windt, C. W., and Schurr, U.: Non-invasive approaches or phenotyping of enhanced performance traits in bean, Functional Plant Biology, 38, 968–983, 2011.
Reddy, T. A.: Applied Data Analysis and Modeling for Energy Engineers and Scientists, Springer, New York, 2011.
Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., and Hall, F. G.: Canopy Reflectance, Photosynthesis and Transpiration. III. A Reanalysis Using Improved leaf Models and a New Canopy Integration Scheme, Remote Sens. Environ., 42, 187–216, 1992.
Shippert, P.: Why use hyperspectral imagery?, Photogramm. Eng. Rem. S., 70, 377–396, 2004.
Singh, A., Ganapathysubramanian, B., Singh, A. K., and Sarkar, S: Machine Learning for High-Throughput Stress Phenotyping in Plants, Trends Plant Sci., 21, 110–124, 2016.
Souza, A. A., Galvão, L. S., and Santos, J. R.: Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment, Remote Sens. Lett., 1, 55–64, 2010.
Suomalainen, J., Anders, N., Iqbal, S., Roerink, G., Franke, J., Wenting, P., Hünniger, D., Bartholomeus, H., Becker, R. and Kooistra, L.: A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles, Remote Sens., 6, 11013–11030, 2014.
Tian, Y. C., Yao, X., Yang, J., Cao, W. X., Hannaway, D. B., and Zhu, Y.: Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance, Field Crop. Res., 120, 299–310, 2011.
Tilly, N., Hoffmeister, D., Aasen, H., Brands, J., and Bareth, G.: Multi-Temporal Crop Surface Model derived from Terrestrial Laser Scanning for Accurate Plant Height Measurement and Biomass estimation of Barley, in: Proceedings of the Workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation, edited by: Bendig, J. and Bareth, G., Kölner Geographische Arbeiten, Cologne, 94, 83–91, 2014.
Thenkabail, P. S., Lyon, J. G., and Huete, A.: Advances in Hyperspectral Remote Sensing of Vegetation in Agricultural Croplans, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., and Huete, A., CRC Press, New York, 3–33, 2012.
Uddling, J., Gelang-Alfredsson, J., Piikki, K., and Pleijel, H.: Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings, Photosynth. Res., 91, 37–46, 2007.
van der Putten, W. H., Bardgett, R. D., Bever, J. D., Bezemer, T. M., Casper, B. B., Fukami, T., Kardol, P., Klioronomos, J. N., Kulmatiski, A., Schweitzer, J. A., Suding, K. N., van de Voorde, T. F. J., and Wardle, D. A.: Plant-soil feedbacks: the past, the present and future challenges, J. Ecol., 101, 265–276, 2013.
Vincini, M., Frazzi, E., and Alessio, P.: Comparison of narrow-band and broad-band vegetation indices for canopy chlorophyll density estimation in sugar beet, in: Precision agriculture '07, edited by: Stafford, J. V., Wageningen Academic Publishers, the Netherlands, 189–196, 2007.
von Bueren, S. K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M. P., and Yule, I. J.: Deploying four optical UAV-based sensors over grassland: challenges and limitations, Biogeosciences, 12, 163–175, https://doi.org/10.5194/bg-12-163-2015, 2015.
Wang, Z., Schaaf, C. B., Lewis, P., Knyazikhin, Y., Schull, M. A., Strahler, A. H., Yao, T., Myneni, R. B., Chopping, M. J., and Blair, B. J.: Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data, Remote Sens. Environ., 115, 1595–1601, 2011.
Weiss, M., Troufleau, D., Baret, F., Chauki, H., Prévot, L., Olioso, A., Bruguier, N., and Brisson, N.: Coupling canopy functioning and radiative transfer models for remote sensing data assimilation, Agr. Forest Meteorol., 108, 113–128, 2001.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H., and Diemer, M.: The worldwide leaf economics spectrum, Nature, 428, 821–827, 2004.
Wu, C., Niu, Z., Tang, Q., and Huang, W.: Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agr. Forest Meteorol., 148, 1230–1241, 2008.
Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., and Feng, J.: Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices, Agr. Forest Meteorol., 149, 1015-1021, 2009.
Yang, Y.-K. and Miller, L.-D.: Correlations of Rice Grain Yields to Radiometric Estimates of Canopy Biomass as a Function of Growth Stage, Korean Journal of Remote Sensing, 1, 63–87, 1985.
Yu, K., Gnyp, M. L., Gao, J., Miao, Y., Chen, X., and Bareth, G.: Using Partial Least Squares (PLS) to Estimate Canopy Nitrogen and Biomass of Paddy Rice in China's Sanjiang Plain, in: Proceedings of the Workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation, edited by: Bendig, J. and Bareth, G., Kölner Geographische Arbeiten, Cologne, 94, 99–103, 2014.
Zhang, C. and Kovacs, J. M.: The application of small unmanned aerial systems for precision agriculture: a review, Precision Agriculture, 13, 693–712, 2012.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., Reed, B. C., and Huete, A.: Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471–475, 2003.
Zhao, B., Ma, B., Hu, Y., and Liu, J.: Characterization of nitrogen and water status in oat leaves using optical sensing approach, J. Sci. Food Agr., 95, 367–378, 2014.