Winter oilseed rape and winter wheat growth prediction using remote sensing methods  

https://doi.org/10.17221/412/2015-PSECitation:Domínguez J.A., Kumhálová J., Novák P. (2015): Winter oilseed rape and winter wheat growth prediction using remote sensing methods  . Plant Soil Environ., 61: 410-416.
download PDF

Remote sensing is often used for yield prediction as well as for crop monitoring. This paper describes how Landsat satellite data can be used to derive a growth model calculated from normalised difference vegetation index that can predict winter wheat (Triticum aestivum) and winter oilseed rape (Brassica napus) phenological state using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie scale. Time series of Landsat images were chosen from the years 2004, 2008 and 2012, when winter oilseed rape was grown, and 2005, 2009, 2011 and 2013, when winter wheat was grown in the same experimental field. The images were selected from the whole growing season of both crops. An advantage of this method is the easy availability of the remote sensing and its easy application for deriving a prediction model from vegetation indices. Our results showed that Landsat images, after correct pre-processing, can be used for winter wheat and winter oilseed rape growth model prediction.

References:
Bartoszek Krzysztof (2014): Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape. Zemdirbyste-Agriculture, 101, 445-452  https://doi.org/10.13080/z-a.2014.101.057
 
Bégué A., Todoroff P., Pater J. (2008): Multi-time scale analysis of sugarcane within-field variability: Improved crop diagnosis using satellite time series? Precision Agriculture, 9: 161–171.
 
Behrens Torsten, Kraft Martin, Wiesler Franz (2004): Influence of measuring angle, nitrogen fertilization, and variety on spectral reflectance of winter oilseed rape canopies. Journal of Plant Nutrition and Soil Science, 167, 99-105  https://doi.org/10.1002/jpln.200321235
 
Bernstein Lawrence S. (2012): Quick atmospheric correction code: algorithm description and recent upgrades. Optical Engineering, 51, 111719-  https://doi.org/10.1117/1.OE.51.11.111719
 
Bleiholder H.T., Boom V.D., Langelüddecke P., Stauss R. (1989): Einheitliche Codierung der phänologischen Stadien bei Kultur und Schadpflanzen. Gesunde Pflanzen, 41: 381–384.
 
Birth Gerald S., McVey George R. (1968): Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1. Agronomy Journal, 60, 640-  https://doi.org/10.2134/agronj1968.00021962006000060016x
 
Chao Rodríguez Y., el Anjoumi A., Domínguez Gómez J. A., Rodríguez Pérez D., Rico E. (): Using Landsat image time series to study a small water body in Northern Spain. Environmental Monitoring and Assessment, , -  https://doi.org/10.1007/s10661-014-3634-8
 
Chuvieco E. (1990): Fundamentals of remote sensing. Madrid, Ediciones Rial S.A. (In Spanish)
 
Domínguez J.A., Marcos C., Chao Y., Delgado G., Rodríguez D. (2011): Freshwater study using remote sensing. Madrid, UNED. (In Spanish)
 
DORAISWAMY P (2004): Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92, 548-559  https://doi.org/10.1016/j.rse.2004.05.017
 
Faměra O., Mayerová M., Burešová I., Kouřimská L., Prášilová M. (): Influence of selected factors on the content and properties of starch in the grain of non-food wheat. Plant, Soil and Environment, 61, 241-246  https://doi.org/10.17221/13/2015-PSE
 
Franch B., Vermote E.F., Becker-Reshef I., Claverie M., Huang J., Zhang J., Justice C., Sobrino J.A. (2015): Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information. Remote Sensing of Environment, 161, 131-148  https://doi.org/10.1016/j.rse.2015.02.014
 
Haboudane D (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, 337-352  https://doi.org/10.1016/j.rse.2003.12.013
 
Hunkár M., Vincze E., Szenyán I., Dunkel Z. (2012): Application of phenological observations in agrometeorological models and climate change research. Quarterly Journal of the Hungarian Meteorological Service, 116: 195–209.
 
Hunt E. Raymond, Doraiswamy Paul C., McMurtrey James E., Daughtry Craig S.T., Perry Eileen M., Akhmedov Bakhyt (2013): 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  https://doi.org/10.1016/j.jag.2012.07.020
 
Jongschaap Raymond E.E., Schouten Léon S.M. (2005): Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. Agronomy for Sustainable Development, 25, 481-489  https://doi.org/10.1051/agro:2005048
 
Jamali Sadegh, Jönsson Per, Eklundh Lars, Ardö Jonas, Seaquist Jonathan (2015): Detecting changes in vegetation trends using time series segmentation. Remote Sensing of Environment, 156, 182-195  https://doi.org/10.1016/j.rse.2014.09.010
 
Julien Y., Sobrino J.A., Jiménez-Muñoz J.-C. (2011): Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method. International Journal of Applied Earth Observation and Geoinformation, 13, 711-720  https://doi.org/10.1016/j.jag.2011.05.008
 
Krček V., Baranyk P., Pulkrábek J., Urban J., Škeříková M., Brant V., Zábranský P. (2014): Influence of crop management on winter oilseed rape yield formation – Evaluation of first year of experiment. In: Proceedings of the Conference Mendelnet 2014, Brno, 57–63.
 
Kumhálová Jitka, Moudrý Vítězslav (2014): Topographical characteristics for precision agriculture in conditions of the Czech Republic. Applied Geography, 50, 90-98  https://doi.org/10.1016/j.apgeog.2014.02.012
 
Kumhálová J., Zemek F., Novák P., Brovkina O., Mayerová M. (2014): Use of Landsat images for yield evaluation within a small plot. Plant, Soil and Environment, 60: 501–506.
 
LANCASHIRE PETER D., BLEIHOLDER H., BOOM T. VAN DEN, LANGELÜDDEKE P., STAUSS R., WEBER ELFRIEDE, WITZENBERGER A. (1991): A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology, 119, 561-601  https://doi.org/10.1111/j.1744-7348.1991.tb04895.x
 
Laurila Heikki, Karjalainen Mika, Kleemola Jouko, Hyyppä Juha (2010): Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing. Remote Sensing, 2, 2185-2239  https://doi.org/10.3390/rs2092185
 
Li P., Jiang L., Feng Z. (2014): Cross-comparison of vegetation indices derived from Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) sensors. Remote Sensing, 6: 310–329.
 
Li Xing-Mao, He Zhong-Hu, Xiao Yong-Gui, Xia Xian-Chun, Trethowan Richard, Wang Hua-Jun, Chen Xin-Min (2015): QTL mapping for leaf senescence-related traits in common wheat under limited and full irrigation. Euphytica, 203, 569-582  https://doi.org/10.1007/s10681-014-1272-4
 
McDonald A.J., Gemmell F.M., Lewis P.E. (1998): Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests. Remote Sensing of Environment, 66, 250-272  https://doi.org/10.1016/S0034-4257(98)00057-1
 
Michener W.K., Houhoulis P.F. (1997): Detection of vegetation changes associated with extensive flooding in a forested ecosystem. Photogrammetric Engineering and Remote Sensing, 63: 1363–1374.
 
Mistele Bodo, Schmidhalter Urs (2008): Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Research, 106, 94-103  https://doi.org/10.1016/j.fcr.2007.11.002
 
Myneni R.B, Asrar G (1994): Atmospheric effects and spectral vegetation indices. Remote Sensing of Environment, 47, 390-402  https://doi.org/10.1016/0034-4257(94)90106-6
 
Pan Zhuokun, Huang Jingfeng, Wang Fumin (2013): Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area. International Journal of Applied Earth Observation and Geoinformation, 25, 21-29  https://doi.org/10.1016/j.jag.2013.03.002
 
Peltonen-Sainio Pirjo, Jauhiainen Lauri, Trnka Miroslav, Olesen Jörgen E., Calanca Pierluigi, Eckersten Henrik, Eitzinger Josef, Gobin Anne, Kersebaum Kurt Christian, Kozyra Jerzy, Kumar Suresh, Marta Anna Dalla, Micale Fabio, Schaap Ben, Seguin Bernard, Skjelvåg Arne O., Orlandini Simone (2010): Coincidence of variation in yield and climate in Europe. Agriculture, Ecosystems & Environment, 139, 483-489  https://doi.org/10.1016/j.agee.2010.09.006
 
Rouse J.W., Haas R.H., Schel J.A., Deering D.W. (1974): Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Third ERTS-1 Symposium, NASA Goddard, NASA SP-351, 309–317.
 
Song C., Woodcock C.E., Seto K.C., Lenney M.P., Macomber S.A. (2001): Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75: 230–244.
 
Tornos Lucia, Huesca Margarita, Dominguez Jose Antonio, Moyano Maria Carmen, Cicuendez Victor, Recuero Laura, Palacios-Orueta Alicia (2015): Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 110-124  https://doi.org/10.1016/j.isprsjprs.2014.12.006
 
Zhu L., Xu J.F., Huang J.F., Wang F.M., Liu Z.Y., Wang Y. (2008): Study on hyperspectral estimation model of crop vegetation cover percentage. Spectroscopy and Spectral Analysis, 28: 1827–1831. (In Chinese)
 
download PDF

© 2020 Czech Academy of Agricultural Sciences