Agricultural Product Classification for Optimal Water Resource Management Using the Data Time Series of Landsat8

Document Type : Research Article


1 Master Student of Remote Sensing, Faculty of Geography, University of Tehran, Tehran, Iran

2 Associate Professor, Faculty of Geography, University of Tehran, Tehran, Iran

3 Assistant Professor, Faculty of Geography, University of Tehran, Tehran, Iran

4 Professor, Faculty of Geography, University of Tehran, Tehran, Iran


Distinguishing the type of agricultural production usingfor optimal water resource management the Remote Sensing, despite the low availability of direct access to agricultural land, would significantly reduce costs in the agricultural area. However, agricultural classification is not very accurate due to the high similarity of different products using one image of multi-spectral. One of the ways to dominate this problem is to use the data time series of the satellite. The purpose of this study is to increase the precision of agricultural product separation by using time series data. In this study after the process of optical data, various vegetation indices, as well as albedo and surface temperature of the optical time series data were calculated and using the TIMESAT model, the key phonological parameters of the plant were extracted in part of the Miandoab plain. Given the availability of ground truth data and, vegetation status such as distribution and vegetation characteristics were examined. Then, using all of these features, a map of agricultural products was created using the support vector machine classification algorithm. The support vector machine classification algorithm is due to the high flexibility of this algorithm for different situations and purposes with a total accuracy of 92% and a kappa of 0.91 if the classification process involves the combination of bands, vegetation indices GNDVI and the ALBEDO, LST index, made the most careful distinction between crops.


Main Subjects

[1]. Asadi Rashd e. Mirbaghari, V. Abkar, A."Estimation of yield of wheat in Ghazvin plain using leaf area index produced from satellite images", IRS Geomatics conference, 2008.[Persian]
[2]. Bendini, H., Sanches, I. D., Körting, T. S., Fonseca, L. M. G., Luiz, A. J. B., and Formaggio, A. R. " Using Landsat 8 Image Time Series for Crop Mapping in a Region of Cerrado, Brazil", , The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8 , 2016.
[3]. Blaes, X., P. Defourny, U. Wegmuller, A. Della Vecchia, L. Guerriero, and Ferrazzoli, P., C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model, IEEE Trans. Geosci. Remote Sens., 44(4), 791–800, 2006.
[4]. Blaschke, T., Object based image analysis for remote sensing, ISPRS journal of photogrammetry and remote sensing, 65(1), pp.2-16, 2010.
[5]. Brenning, A., “Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection”, In: Remote Sensing of Environment113(1), pp. 239–247, 2009.
[6]. Fathian, F., Study of land use change process using remote sensing technology and weather variables in Urmia Lake Basin, Researcher, Researcher, Water Resources Engineering Department, Tarbiat Modares University, 2011. [Persian]
[7]. Jahan-Afrooz, Jahan afroz, A. Bardideh, M. Nasiri, N and Ghasemi, MM., Estimation of wheat cultivar using the technology of age measurement (case study of Arsanjan city), Geomatics conference, 2010. [Persian]
[8]. Jensen, J., ‏Introductory digital image processing: A remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Prentice Hall. 526 pp, 2005.
[9]. Karjalainen, M., Kaartinen, H., Hyyppä, J., Agricultural Monitoring Using Envisat Alternating Polarization SAR Images, Journal of The American Society For Photogrammetry And Remote Sensing 74(1): 117-128, 2008.
[10]. Lu, D. and Q. Weng., A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28 (5): 823-870, 2007.
[11]. Otukei, J. and T. Blaschke, Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms, In: International Journal of Applied Earth Observation and Geoinformation 12, S27–S31, 2010.
[12]. Report of the President of the 2008 Food Security Conference., Climate Change and Energy Challenges at the FAO Summit,
Emphasizing Strategic Recommendations of the President of the Islamic Republic of Iran, Attached to the Quarterly of Special Issue of the Islamic State, July 30, 2008. [Persian]
[13]. Roghancheraghi, N. Rangzan, K. Meskarbashi, M Moradzadeh, M. And Ghasemi-mofard, MA., Application of Spectral Data to Estimate the Wheat Crop Needs, Geomatics National Conference, May 2011. [Persian]
[14]. Ziaeian-Firoozabadi, P., L. Sayad-Bydhndy, and M. Eskandari-Nodeh., Mapping and estimating the area under rice cultivation in Sari city using satellite images Radarst, Geography Research Natural 68: 45-58, 2009. [Persian]
Volume 5, Issue 4
January 2019
Pages 1267-1283
  • Receive Date: 21 May 2018
  • Revise Date: 21 September 2018
  • Accept Date: 02 October 2018
  • First Publish Date: 22 December 2018
  • Publish Date: 22 December 2018