Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in North Rhine-Westphalia, Germany

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 2016-January, Issue , 2016, Pages 1071-1077

  Willkomm, M ^a   Bolten, A ^a   Bareth, G ^a  

^a UNIVERSITY OF COLOGNE   (Germany)

Author keywords

Agriculture; CSM; DEM; Hyperspectral data; Plant height; Rice; UAV; Vegetation index

Indexed keywords

AGRICULTURE; CROPS; CULTIVATION; FOOD SUPPLY; NITROGEN; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

GROWTH CHARACTERISTIC; HYPERSPECTRAL DATA; HYPERSPECTRAL REMOTE SENSING; NITROGEN CONCENTRATIONS; NON-DESTRUCTIVE MONITORING; PLANT HEIGHT; RICE; VEGETATION INDEX;

REMOTE SENSING;

EID: 84987817185     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5194/isprsarchives-XLI-B1-1071-2016     Document Type: Conference Paper

References (23)

1. Aber, J.S., Marzloff, I., and Ries, J. B., 2010. Small-Format Aerial Photography. Principles, Techniques and Gesoscience Applications. Elsevier, Amsterdam.
2. Bendig, J., Willkomm, M., Tilly, N., Gnyp, M.L., Bennertz, S., Qiang, C., Miao, Y., Lenz-Wiedemann, V.I.S. & Bareth, G., 2013a. Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Rostock, Germany, vol. XL-1/W2, pp. 45-50.
3. Bendig, J., Bolten, A. & Bareth, G., 2013b. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability. PFG Photogrammetrie, Fernerkundung, Geoinformation, 2013(6), pp. 551-562.
4. Berni, J.A.J., Zarco-Tejada, P.J., Suárez, L., González-Dugo, V., & Fereres, E., 2009. Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(6).
5. Fageria, N.K., Slaton, N.A. & Baligar, V.C., 2003. Nutrient Management for Improving Lowland Rice Productivity and Sustainability. Advances in Agronomy, 80(1), pp. 63-152.
6. Fageria, N.K., 2007. Yield physiology of rice. Journal of Plant Nutrition, 30(4-6), pp. 843-879.
7. ∗/E (26 Jan. 2015).
8. Geipel, J., Link, J. & Claupein, W., 2014. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System. Remote Sensing, 6(11), pp. 10335-10355.
9. Gnyp, M. L., Yu, K., Aasen, H., Yao, Y., Huang, S., Miao, Y. & Bareth, G., 2013. Analysis of Crop Reflectance for Estimating Biomass in Rice Canopies at Different Phenological Stages. Photogrammetrie - Fernerkundung - Geoinformation, 2013(4), pp. 351-365.
10. Goetz, A.F.H., 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 113(1), pp. 5-16.
11. Guyot, G. & Baret, F., 1988. Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux. Spectral Signatures of Objects in Remote Sensing, 287(1), pp. 279-286.
12. Hansen, P.M. & Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), pp. 542-553.
13. Katsura, K., Okami, M., Mizunuma, H. & Kato, Y., 2010. Radiation use efficiency, N accumulation and biomass production of high-yielding rice in aerobic culture. Field Crops Research, 117(1), pp. 81-89.
14. Mulla, D.J., 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), pp. 358-371.
15. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. & Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126.
16. Rouse, J. W., and Haas, R. H., Schell, J. A. & Deering, D. W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351(1), pp. 307-317.
17. Snavely, K.N., 2008. Scene Reconstruction and Visualization from Internet Photo Collections. Dissertation, University of Washington, USA.
18. Stroppiana, D., Boschetti, M., Brivio, P.A. & Bocchi, S., 2009. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111(1-2), pp. 119-129.
19. Thenkabail, P.S., Enclona, E.A., Ashton, M.S. & van der Meer, B., 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91(3-4), pp. 354-376.
20. Tomasi, C. & Kanade, T., 1992. Shape and motion from image streams under orthography: a factorization method. International Journal of Computer Vision, 9(2), pp. 137-154.
21. Van der Wal, T., Abma, B., Viguria, A., Previnaire, E., Zarco-Tejada, P., Serruys, P., Van Valkengoed, E. & van der Voet, P. F., 2013. Unmanned aerial systems for crop monitoring services. Precision Agriculture, 13(1), pp. 169-175.
22. Watts, A.C., Ambrosia, V.G. & Hinkley, E.A., 2012. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sensing, 4(6), pp. 1671-1692.
23. Yoshida, S., 1981. Fundamentals of rice crop science. The International Rice Research Institute, Los Banos.