Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors

Biogeosciences

Volumn 14, Issue 3, 2017, Pages 733-749

  Van Der Meij, Bob ^a   Kooistra, Lammert ^b   Suomalainen, Juha ^b   Barel, Janna M ^b   De Deyn, Gerlinde B ^b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COMMUNITY DYNAMICS; CROP PLANT; ENVIRONMENTAL FACTOR; EXPERIMENTAL STUDY; FIELD METHOD; GRASS; GROWING SEASON; PLANT COMMUNITY; REMOTE SENSING; SENSOR; SOIL-VEGETATION INTERACTION; UNMANNED VEHICLE;

AVENA SATIVA; BRASSICACEAE; FABACEAE; LOLIUM PERENNE; POACEAE; RAPHANUS SATIVUS; VICIA SATIVA;

EID: 85013006954     PISSN: 17264170     EISSN: 17264189     Source Type: Journal    
DOI: 10.5194/bg-14-733-2017     Document Type: Article

References (92)

1. Aasen, H., Gnyp, M. L., Miao, Y., Bareth, G. : Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with Hypercor, Photogramm. Eng. Rem. S., 80, 51-61, 2014.
2. Abdi, H. : Partial least squares regression and projection on latent structure regression (PLS Regression), Wiley Interdisciplinary Reviews: Computational Statistics 2. 1, 97-106, 2010.
3. Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., 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.
4. Barel, J. M., Kuyper, T. W., de Boer, W., Douma, J. C., 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.
5. Bever, J. D., Dickie, I. A., Facelli, E., Facelli, J. M., Klironomos, J., Moora, M., Rillig, M. C., Stock, W. D., Tibbett, M., Zobel, M. : Rooting theories of plant community ecology in microbial interactions, Trends Ecol. Evol., 25, 468-478, 2010.
6. 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.
7. Brinkman, E. P., van der Putten, W. H., Bakker, E., Verhoeven, K. J. F. : Plant-soil feedback: experimental approaches, statistical analysis and ecological interpretations, J. Ecol., 98, 1063-1073, 2010.
8. 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.
9. Campbell, J. B. andWynne, R. H. : Introduction to Remote Sensing, The Guildford Press, New York, 2002.
10. Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., Suomalainen, J. : Estimating Plant Traits of Grasslands from UAVAcquired Hyperspectral Images: A Comparison of Statistical Approaches, ISPRS Int. J. Geo-Inf., 4, 2792-2820, 2015.
11. Chapman, S. C., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M. F., Holland, E., Zheng, B., Ling, T. J., Jimenez-Berni, J. : Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping, Agronomy, 4, 279-301, 2014.
12. Chen, P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., Li, B. : New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat, Remote Sens. Environ., 114, 1987-1997, 2010.
13. Cho, M. A., Skidmore, A., Corsi, F., vanWieren, S. E., 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.
14. Christenson, B., Schapaugh Jr., W. T., An, N., Price, K. P., Fritz, A. K. : Characterizing Changes in Soybean Spectral Response Curves with Breeding Advancements, Crop Science, 54, 1585-1597, 2013.
15. Chuvieco, E. : Fundamentals of Satellite Remote Sensing, New York: CRC Press, 2011.
16. Ciganda, V., Gitelson, A., Schepers, J. : Non-destructive determination of maize leaf and canopy chlorophyll content, J. Plant Physiol., 166, 157-167, 2009.
17. 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.
18. Colomina, I. and Molina, P. : Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm., 92, 79-97, 2014.
19. 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., Poorter, H. : A handbook of protocols for standardized and easy measurement of plant functional traits worldwide, Aust. J. Bot., 51, 335-380, 2003.
20. Cortois, R., Schröder-Georgi, T., Weigelt, A., van der Putten, W. H., De Deyn, G. B. : Plant-soil feedbacks: role of plant functional group and plant traits, J. Ecol., 104, 1608-1617, 2016.
21. De Bello, F., Lavorel, S., Diáz, 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., Harrison, P. A. : Towards an assessment of multiple ecosystem processes and services via functional traits, Biodivers. Conserv., 19, 2873-2893, 2010.
22. Dias, T., Dukes, A., Antunes, P. M. : Accounting for soil biotic effects on soil health and crop productivity in the design of crop rotations, J. Sci. Food Agr., 95, 447-454, 2014.
23. Diáz, 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., Gorné, L. D. : The global spectrum of plant form and function, Nature, 529, 167-171, 2016.
24. Faye, E., Rebaudo, F., Yanez-Cajo, D., Cauvy-Fraunie, S., 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.
25. Fiorani, F. and Schurr, U. : Future Scenarios for Plant Phenotyping, Annu. Rev. Plant Biol., 64, 267-291, 2013.
26. Franklin, S. E. : Remote Sensing for Sustainable Forest Management, CRC Press, New York, 2001.
27. 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., 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.
28. Gitelson, A. A. : Nondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, Anthocyanin) Contents: Evaluating a Semianalytical Three-Band Model, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., Huete, A., CRC Press, New York, 141-166, 2012.
29. Gitelson, A. A., Vinã, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., Leavitt, B. : Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophys. Res. Lett., 30, 52-55, 2003.
30. Goswami, A., Gamon, J. A., Vargas, S., Tweedie, C. E. : Relationship of NDVI, biomass, Leaf Area Index (LAI) for six key plant species in Barrow, Alaska, PeerJ PrePrints, 3, e1127, doi:10. 7287/peerj. preprints. 913v1, 2015.
31. Govender, M., Chetty, K., Bulcock, H. : A review of hyperspectral remote sensing and its application in vegetation and water resource studies, Water SA, 33, 145-152, 2007.
32. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., 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.
33. Haghighattalab, A., González Pérez, L., Mondal S., Singh, D., Schinstock, D., Rutkoski, J., Ortiz-Monasterio, I., Singh, R. P., Goodin, D., Poland, J. : Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries, Plant Methods, 12, 35-50, 2016.
34. 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.
35. 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.
36. Hodge, A., Robinson, D., Fitter, A. : Are microorganisms more effective than plants at competing for nitrogen , Trends Plant Sci., 5, 304-308, 2000.
37. Homolová, L., Malenovský, Z., Clevers, J. G. P. W., Garciá-Santos, G., Schaepman, M. E. : Review of optical-based remote sensing for plant trait mapping, Ecological Complexity, 15, 1-16, 2013.
38. Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., 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.
39. Hruska, R., Mitchell, J., Anderson, M., Glenn, N. F. : Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle, Remote Sens., 4, 2736-2752, 2012.
40. Hunt Jr., E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., 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.
41. Jones, H. G. and Vaughan, R. A. : Remote Sensing of Vegetation: Principles, Techniques and Applications, Oxford University Press, UK, 2010.
42. 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., Patinõ, 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., Wirth, C. : TRY-a global database of plant traits, Glob. Change Biol., 17, 2905-2935, 2011.
43. Ke, P. J., Miki, T., Ding, T. S. : The soil microbial community predicts the importance of plant traits in plant-soil feedback, New Phytol., 206, 329-341, 2015.
44. Kooistra, L. and Clevers, J. G. P. W. : Estimating potato leaf chlorophyll content using ratio vegetation indices, Remote Sens. Lett., 7, 611-620, 2016.
45. Kooistra, L., Suomalainen, J., Iqbal, S., Franke, J., Wenting, Ph., Bartholomeus, H., Mücher, S., 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.
46. Kulmatiski, A. and Kardol, P. : Getting plant-soil feedbacks out of the greenhouse: experimental and conceptual approaches, in: Lüttge, U., Beyschlag, W., Murata, J., Progress in Botany, Springer Berlin Heidelberg, 449-472, 2008.
47. Kulmatiski, A., Beard, K. H., Stevens, J. R., Cobbold, S. M. : Plant-soil feedbacks: a meta-analytical review, Ecol. Lett., 11, 980-992, 2008.
48. Lamb, D. W., Steyn-Ross, M., Schaare, P., Hanna, M. M., Silvester, W., 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.
49. Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S., Baret, F. : Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots, Sensors, 8, 3557-3585, 2008.
50. Li, F., Miao, Y., 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.
51. Malhi, S. S., Johnston, A. M., Schoenau, J. J., Wang, Z. L., 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.
52. Maindonald, J. and Braun, W. J. : Data Analysis and Graphics Using R: An Example-Based Approach, Cambridge University Press, UK, 2010.
53. Michaelsen, J., Schimel, D. S., Friedl, M. A., Davis, F. W., Dubayah, R. C. : Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys, J. Veg. Sci., 5, 673-686, 1994.
54. Mulla, D. J. : Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, 101, 172-182, 2013.
55. Murphy, D. J. and Murray, B. G. : Encyclopedia of Applied Plant Sciences, Three-Volume Set, Academic Press, 2003.
56. 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.
57. Nebiker, S., Annen, A., Scherrer, M., 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.
58. Netto, A. T., Campostrini, E., de Gonçalves, O. J., Bressan-Smith, R. E. : Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves, Sci. Hortic., 104, 199-209, 2005.
59. 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.
60. Ortenberg, F. : Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, Truck Mounted; Integration of Hyperspectral Data with LIDAR, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., Huete, A., CRC Press, New York, 39-68, 2012.
61. Orwin, K. H., Buckland, S. M., Johnson, D., Turner, B. L., Smart, S., Oakley, S., Bardgett, R. D. : Linkages of plant traits to soil properties and the functioning of temperate grassland, J. Ecol., 98, 1074-1083, 2010.
62. Peinetti, H. R., Menezes, R. S. C., 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, nitrogen dynamics, Oecologia, 127, 334-342, 2001.
63. Pinter Jr., P. J., Hathfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., Upchurch, D. R. : Remote Sensing for Crop Management, Photogramm. Eng. Rem. S., 69, 647-664, 2003.
64. Qi, J., Inoue, Y., Wiangwang, N. : Hyperspectral Remote Sensing in Global Change Studies, in: Hyperspectral Remote Sensing of Vegetation, edited by: Thenkabail, P. S., Lyon, J. G., Huete, A., CRC Press, New York, 70-89, 2012.
65. Rango, A., Laliberte, A., Herrick, J. E., Winters, C., Havstad, K., Steele, C., Browning, D. : Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, management, J. Appl. Remote Sens., 3, 1-15, 2009.
66. 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., Schurr, U. : Non-invasive approaches or phenotyping of enhanced performance traits in bean, Functional Plant Biology, 38, 968-983, 2011.
67. Reddy, T. A. : Applied Data Analysis and Modeling for Energy Engineers and Scientists, Springer, New York, 2011.
68. Scotford, I. M. and Miller, P. C. H. : Combination of Spectral Reflectance and Ultrasonic Sensing to monitor the Growth of Winter Wheat, Biosystems Engineering, 87, 27-38, 2004.
69. Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., 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.
70. Shippert, P. : Why use hyperspectral imagery , Photogramm. Eng. Rem. S., 70, 377-396, 2004.
71. Singh, A., Ganapathysubramanian, B., Singh, A. K., Sarkar, S: Machine Learning for High-Throughput Stress Phenotyping in Plants, Trends Plant Sci., 21, 110-124, 2016.
72. Souza, A. A., Galvaõ, L. S., Santos, J. R. : Relationships between Hyperion-derived vegetation indices, biophysical parameters, elevation data in a Brazilian savannah environment, Remote Sens. Lett., 1, 55-64, 2010.
73. 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.
74. Tian, Y. C., Yao, X., Yang, J., Cao, W. X., Hannaway, D. B., Zhu, Y. : Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with groundand space-based hyperspectral reflectance, Field Crop. Res., 120, 299-310, 2011.
75. Tilly, N., Hoffmeister, D., Aasen, H., Brands, J., 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.
76. Thenkabail, P. S., Smith, R. B., de Pauw, E. : Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics, Remote Sens. Environ., 71, 158-182, 2000.
77. Thenkabail, P. S., Lyon, J. G., 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., Huete, A., CRC Press, New York, 3-33, 2012.
78. Uddling, J., Gelang-Alfredsson, J., Piikki, K., Pleijel, H. : Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings, Photosynth. Res., 91, 37-46, 2007.
79. 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., Wardle, D. A. : Plant-soil feedbacks: the past, the present and future challenges, J. Ecol., 101, 265-276, 2013.
80. Vincini, M., Frazzi, E., 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.
81. von Bueren, S. K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M. P., Yule, I. J. : Deploying four optical UAV-based sensors over grassland: challenges and limitations, Biogeosciences, 12, 163-175, doi:10. 5194/bg-12-163-2015, 2015.
82. Wang, Z., Schaaf, C. B., Lewis, P., Knyazikhin, Y., Schull, M. A., Strahler, A. H., Yao, T., Myneni, R. B., Chopping, M. J., Blair, B. J. : Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data, Remote Sens. Environ., 115, 1595-1601, 2011.
83. Warner, T. A., Nellis, M. D., Foody, G. M. : The SAGE Handbook of Remote Sensing, SAGE Publications Ltd, London, UK, 2009.
84. Weiss, M., Troufleau, D., Baret, F., Chauki, H., Prévot, L., Olioso, A., Bruguier, N., Brisson, N. : Coupling canopy functioning and radiative transfer models for remote sensing data assimilation, Agr. Forest Meteorol., 108, 113-128, 2001.
85. Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H., Diemer, M. : The worldwide leaf economics spectrum, Nature, 428, 821-827, 2004.
86. Wu, C., Niu, Z., Tang, Q., Huang, W. : Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agr. Forest Meteorol., 148, 1230-1241, 2008.
87. Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J. : Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices, Agr. Forest Meteorol., 149, 1015-1021, 2009.
88. 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.
89. Yu, K., Gnyp, M. L., Gao, J., Miao, Y., Chen, X., 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.
90. Zhang, C. and Kovacs, J. M. : The application of small unmanned aerial systems for precision agriculture: a review, Precision Agriculture, 13, 693-712, 2012.
91. Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., Reed, B. C., Huete, A. : Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471-475, 2003.
92. Zhao, B., Ma, B., Hu, Y., Liu, J. : Characterization of nitrogen and water status in oat leaves using optical sensing approach, J. Sci. Food Agr., 95, 367-378, 2014.