Prediction of Canola Yield in Some of Growth Stages by Using Landsat Satellite, OLI Sensor

Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

3 Assistant Professor, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), karaj, Iran

4 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

Abstract

Canola is a source of edible oil and its cultivation in Iran and the world is growing. Only few studies have been carried out by remote sensing for canola yield estimation,. In 2017-2018, in order to predict the canola yield by Landsat satellite, OLI sensor, three farms were evaluated. The satellite images were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturing, and some of vegetation indices were extracted based on the ratio of the bands. The pixel network of each farm was determined and the Real Time Kinematic Global Positioning System (RTKGPS) was used to increase the precision of pixels location in the farms. Sampling was done inside farms pixels during harvesting time and canola yield was measured. Totally, 28 pixels from three studied farms were used to develop and validate the predictive models. Simple and multivariate linear regression models were used to assess the relationship between canola yield and vegetation indices. The results showed that, on the basis of simple linear regression models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturing. The coefficient of determination in all vegetation indices was low in the before flowering stage (less than 10 percent) and relatively medium (24- 52 percent) in the early flowering and dry maturing stages. According to this model, the NDYI with 67 percent in the peak of flowering stage, and the RVI with 64 percent in the green maturing stage had the highest coefficient of determination compared to other vegetation indices. The stepwise multivariate linear regression models, with four visible and near infrared bands, resulted to the best yield predictive model in the peak of flowering stage, with 78 and 74 percent of coefficient of determination, for its implementation and validation, respectively.

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References

Alavipanah, S.K. (2016). Fundamentals of modern remote sensing and interpretation of Satellite images and aerial photos. University of Tehran Press. (In Farsi)
Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L. & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92 (1), 83-91.
Ash, M. (2017). Canola Production and Processing. Retrieved March 27, 2018, from https://www.ers.usda.gov/topics/crops/soybeans-oil-crops/canola.aspx.
Basnyat, P., McConkey, B., Lafond, G. P., Moulin, A. & Pelcat, Y. (2004). Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies. Canadian Journal of Plant Science, 84 (1), 97–103.
Birth, G.S. & McVey, G.R. (1968). Measuring color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60, 640-649.
Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14, 711–722.
Darvishzadeh, R., Matkan, A. & Eskandari, N. (2012). Evaluation of the spectral indices from ALOS-AVNIR2 images for estimation of rice biomass. Journal of Human Settlement Planning Studies, 14, 61-73. (In Farsi)
Dominguez, J.A., Kumhalova, J. & Novak, P. (2017). Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield. Agronomy Research, 15(1), 055–068.
Fang, H., Song, H.Y., Cao, F., He, Y. & Qiu, Z.J. (2007). Study on the relationship between spectral properties of oilseed rape leaves and their chlorophyll content. Journal of Spectroscopy and Spectral Analysis, 27, 1731–1734.
Gallego, J., Carfagna, E. & Baruth, B. (2010). Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. In: Agricultural Survey Methods. John Wiley & Sons, Ltd., pp. 193–211.
Gitelson, A.A., Kaufman, Y.J., Stark, R. & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetative fraction. Journal of Remote Sensing of Environment, 80, 76–87.
Goel, P.K., Prasher, S.O., Landry, J.A., Patel, R.M., Viau, A.A. & Miller, J.R. (2003). Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Transactions of the ASAE, 46 (4), 1235–1246.
Hogya, P., Franzaring, J., Schwadorf, K., Breuer, J., Schütze, W. & Fangmeier, A. (2010) .Effects of free-air CO2 enrichment on energy traits and seed quality of oilseed rape. Journal of Agriculture, Ecosystems & Environment, 139, 239–244.
Huete, A. R., Justice, C. & Van Leeuwen, W. (1996). MODIS vegetation index (mod13). Algorithm theoretical basis document. Version 2. NASA Goddard Space Flight Center. Greenbelt, Maryland 20771, USA.
Jago, R.A., Cutler, M.E.J. & Curran, P.J. (1999). Estimating canopy chlorophyll concentration from field and airborne spectra. Journal of Remote Sensing of Environment, 68 (3), 217–224.
Johnson, D.M. (2016). A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geo information, 52, 65–81.
Kazem, M., Mirzaei, S. & Maadi, B. (2016). Canola cultivation. Tak Press. (In Farsi)
Lee, K.S., Cohen, W.B., Kennedy, R.E., Maiersperger, T.K., Gower, S.T., (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Journal of Remote Sensing of Environment, 91, 508–520.
Liu, F., Jin, Z.L., Naeem, M.S., Tian, T., Zhang, F., He, Y., Fang, H., Ye, Q.F. & Zhou, W.J. (2011). Applying near-infrared spectroscopy and chemo metrics to determine total amino acids in herbicide-stressed oilseed rape leaves. Journal of Food Bioprocess Technol, 4, 1314–1321.
Liu, F., Zhang, F., Jin, Z.L., He, Y., Fang, H., Ye, Q.F. & Zhou, W.J. (2008). Determination of acetolactate synthase activity and protein content of oilseed rape (Brassicanapus L.) leaves using visible/near-infrared spectroscopy. Journal of Analytica Chimica Acta, 629, 56–65.
Matsushita, B., Yang, W., Chen, J., Onda, Y. & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Journal of Sensors, 7(11), 2636-2651.
McBratney, A., Whelan, B., Ancev, T. & Bouma, J. (2005). Future Directions of Precision Agriculture. Journal of Precision Agriculture, 6(1), 7–23.
Mohammadi, E., Kamkar, B., & Abdi, O. (2016). Comparison of the geostatistical method and using data of remote sensing for yield prediction on some of stages plant growth. Journal of Production of Agronomy Plants, 8 (2), 51-76. (In Farsi)
Mollaei, K., Ahmadi, A., Alavipanah, S.K., Rajabipor, A. & Noormohammadi, J. (2008). Evaluation of sugar beet yield by satellite images. In: Proceeding of 5th National Congress on Engineering of Farming Machinery and Mechanization, 27-28 Aug., Ferdosi University, Mashhad, Iran (In Farsi)
Petisco, C., García-Criado, B., Vázquez-de-Aldana, B.R., Haro, A. & García-Ciudad, A. (2010). Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy. Journal of Industrial Crops and Products, 32, 139–146.
Piekarczyk, J. (2011). Winter oilseed-rape yield estimates from hyperspectral radiometer measurements. Journal of Quaestiones Geographicae, 30 (1), 77–84.
Pirnazar, M. & Zandkarimi, A. (2016). Guide of ENVI application and satellite processing images. Naghoos Press. (In Farsi)
Pratt, S. (2013). Satellite crop estimate too low: Analysts. The Western Producer. Retrieved March 28, 2018, from https://www.producer.com/2013/10/satellite-crop-estimate-too-low-analysts.
Raun, W.R., Solie, J.B., Stone, M.L., Lukina, E.V., Thomason, W.E. & Schepers, J.S. (2001). In season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93, 131-138.
Rezaei, A. & Mirmohammadi, S.A. (2011). Statistics and probability, application in agriculture. Jahad Daneshgahi Sanati Esfahan Press. (In Farsi)
Rischbeck, R., Elsayed, S., Mistele, B., Barmeier, G., Heil, K. & Schmidhalter, U. (2016). Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Europ. J. Agronomy, 78, 44–59.
Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, NASA SP-351 I: 309-317.
Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N. & Ghaemi, M. (2014). Wheat yield estimation using Landsat images and observation. Journal of Plant Production, 20 (4), 45-63. (In Farsi)
Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Schlemmer, M.R. & Major, D.J. (2001). Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583-589.
Sulik, J.J. & Long, D.S. (2016). Spectral considerations for modeling yield of canola. Journal of Remote Sensing of Environment, 184, 161–174.
Vigneau, N., Ecarnot, M., Rabatel, G. & Roumet, P. (2011). Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in wheat. Journal of Field Crops Res, 122, 25–31.
Weber, V.S., Araus, J.L., Cairns, J.E., Sanchez, C., Melchinger, A.E. & Orsini, E. (2012). Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Journal of Field Crops Res, 128, 82–90.
Xiaolei, Z. & Yong, H. (2013). Rapid estimation of seed yield using hyperspectral images of oilseed rape leaves. Journal of Industrial Crops and Products, 42, 416– 420.
Yamamoto, K., Guo, W., Yoshioka, Y. & Ninomiya, S. (2014). On plant detection of intact tomato fruits using image analysis and machine learning methods. Journal of Sensors (Basel), 14 (7), 12191–12206.
Zou, X.B., Shi, J.Y., Hao, L.M., Zhao, J.W., Mao, H.P., Chen, Z.W., Li, Y.X. & Holmes, M. (2011). In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Anal. Journal of Analytica Chimica Acta, 706, 105–112.
Iranian Journal of Biosystems Engineering
Volume 50, Issue 1
April 2019
Pages 101-113

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History

  • Receive Date: 25 April 2018
  • Revise Date: 24 September 2018
  • Accept Date: 06 October 2018

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