APPLICATION OF HYPERSPECTURAL IMAGES AND GROUND DATA FOR PRECISION FARMING
Abstract
Crops, like other plants, clearly react to various changes in both natural and anthropogenic factors (herbicides, pesticides, fertilizers, etc.), which affects the amount of phytomass, its fractional composition, and developmental and physiological state of the plant, and, accordingly, is reflected in the spectral image. Data on spectral characteristics of plants allow users to determine quickly and with a high degree of reliability various indicators of the state of agricultural crops and thus improve the efficiency of agrotechnical practices and the use of land resources and facilitate the implementation of the precision farming concept. Reflective properties of plants (and hence crops) carry a large amount of meaningful information about the species, stage of development, and morpho-physiological state, allowing determination of the interrelations between the spectrometric characteristics and temporal physiological parameters. The paper presents the results of monitoring of the state of winter wheat and corn in experimental fields in southern and central Russia in the spring and summer of 2016.
About the Authors
Y. Akhtman
Geodetic Engineering Laboratory of École Polytechnique Fédérale de Lausanne
(EPFL), Switzerland
Switzerland
Ph.D., is Research Associate at the Geodetic Engineering Laboratory of École Polytechnique Fédérale de Lausanne (EPFL)
Switzerland
Ph.D., is Research Associate at the Geodetic Engineering Laboratory of École Polytechnique Fédérale de Lausanne (EPFL)
E. Golubeva
M.V. Lomonosov Moscow State University
Russian Federation
Professor of the Environmental Science Department and Head of Education Programs “Ecology and Environmental Science” and “Landscape Planning and Design” at the Faculty of Geography, Lomonosov Moscow State University (1974 – M.Sc. in Biogeography, 1982 – Ph.D. in Biogeography, 1999 – D. Sc. in Biology)
Russian Federation
Professor of the Environmental Science Department and Head of Education Programs “Ecology and Environmental Science” and “Landscape Planning and Design” at the Faculty of Geography, Lomonosov Moscow State University (1974 – M.Sc. in Biogeography, 1982 – Ph.D. in Biogeography, 1999 – D. Sc. in Biology)
O. Tutubalina
M.V. Lomonosov Moscow State University
Russian Federation
Leading Researcher at the Laboratory of Aerospace Methods, Department of Cartography and Geoinformatics, Faculty of Geography
Russian Federation
Leading Researcher at the Laboratory of Aerospace Methods, Department of Cartography and Geoinformatics, Faculty of Geography
M. Zimin
M.V. Lomonosov Moscow State University
Russian Federation
Senior Researcher at the Laboratory of Aerospace Methods, Department of Cartography and Geoinformatics, Faculty of Geography, M.V. Lomonosov State University. He graduated from the Faculty of Geography in 2001 (M.Sc. Cartography and GIS) and then received Ph.D. in Cartography and GIS (2009
Russian Federation
Senior Researcher at the Laboratory of Aerospace Methods, Department of Cartography and Geoinformatics, Faculty of Geography, M.V. Lomonosov State University. He graduated from the Faculty of Geography in 2001 (M.Sc. Cartography and GIS) and then received Ph.D. in Cartography and GIS (2009
References
1. Bao Y., Xu K., Min J., Xu J. (2013) Estimating wheat shoot nitrogen content at vegetative stage from in situ hyperspectral measurements. Crop science, 5, pp. 2063–2071.
2. Blackburn G. (1998) Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing, 19(4), pp. 657-675
3. Borengasser M., Hungate W., Watkins R. (2007) Hyperspectral Remote Sensing: Principles and Applications. Boca Raton, Florida, USA: CRC Press Constantin D. (2017) Miniature hyperspectral systems. (Dirs: B. Merminod and Y. Akhtman) Thèse École polytechnique fédérale de Lausanne EPFL, n° 7647 (DOI:10.5075/epflthesis-7647)
4. Constantin D., Rehak M., Akhtman Y., Liebisch F. (2015) Detection of crop properties by means of hyperspectral remote sensing from a micro UAV. Bornimer Agrartechnische Berichte 88 (EPFL-CONF-218662), pp.129-137
5. Haboudane D., Miller J., Pattey E., Zarco-Tejada P., Strachan I. (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90 (3), 337-352
6. Liebisch F., Kirchgessner N., Schneider D., Walter A., Hund A. (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant methods 11 (1), 9, 2015. DOI: 10.1186/s13007-015-0048-8
7. Liebisch F., Rohrer B., Walter A. (2015). Evaluation of literature indices for determination of crop biophysical properties for ground and airborne hyperspectral methods. In: 9th EARSeL
8. SIG Imaging Spectroscopy workshop, Luxembourg. 14 – 16 April 2015. Poster. http://www. seon.uzh.ch/dam/jcr:ffffffff-9847-0310-0000-00001eb0e644/Liebisch_EARSEL_Poster_ print.pdf [Accessed 2 December 2017]
9. Sidko A., Pugacheva I., Shevyrnogov A. (2009) A study of spectral brightness dynamics of agricultural crops in vegetation period for determination of their species composition by ground and satellite methods in the southern part of the Krasnodar region. Earth Satellite Studies, 2, pp.71-78.
10. Thenkabail P., Lyon J., Huete A. (2011). Hyperspectral Remote Sensing of Vegetation. Boca Raton, Florida, USA: CRC Press.
11. Thenkabail P., Gumma M., Teluguntla P. and Mohammed I. (2014). Hyperspectral Remote Sensing of Vegetation and Agricultural Crops. Photogrammetric Engineering & Remote Sensing, 80 (8). pp. 697-723.
12. Yakushev V., Petrushis A. (2013) Acquisition, processing, and use of remote sensing data for monitoring of meliorative conditions of agricultural fields. Agrophysics, 2 (10), pp. 52-58.
13. Zimin M., Tutubalina O., Golubeva E., Ris G. (2014) Ground spectroradiometric methodology of Arctic plants for satellite images interpretation. Moscow University Bulletin, Series 5 (Geography), 4, pp. 34–41.
For citation:
Akhtman Y., Golubeva E., Tutubalina O., Zimin M. APPLICATION OF HYPERSPECTURAL IMAGES AND GROUND DATA FOR PRECISION FARMING. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2017;10(4):117-128. https://doi.org/10.24057/2071-9388-2017-10-4-117-128
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