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Crop Area Mapping By Intelligent Pixel Information Inferred Using 250m Modis Vegetation Timeseries In Transboundary Indus Basin

Author

Listed:
  • Muhammad Mohsin Khan

    (Department of Agricultural Engineering, MNS-University of Agriculture, Multan, Pakistan)

  • Muhammad Jehanzeb Masud Cheema

    (Faculty of Agricultural Engineering and Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan)

  • Talha Mahmood

    (Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, Pakistan)

  • Saddam Hussain

    (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, PakistanAuthor-Name: Muhammad Sohail Waqas
    Soil Conservation Group, Agriculture Department (Field Wing), Government of the Punjab, Pakistan)

  • Hafiz Muhammad Nauman

    (Department of Farm Machinery and Power, University of Agriculture Faisalabad, Pakistan)

  • Mohsin Nawaz

    (Department of Agricultural Engineering, MNS-University of Agriculture, Multan, Pakistan)

  • Muhammad Saifullah

    (Department of Irrigation and Drainage, University of Agriculture Faisalabad, Pakistan)

Abstract

Irrigation water could be managed properly by mapping area of various crops. Remote sensing data can provide useful Land Use Land Cover (LULC) for assessment of different crop area and change detection. The present study was carried out with core objective to map crop area within the Indus Basin’s transboundary. Four major crops (i.e. wheat, rice, cotton and sugarcane) were identified using Normalize Difference Vegetation Index (NDVI) time series that was picked up from MODIS sensors aboard Terra (EOS AM) and Aqua (EOS PM) satellites with 250m pixel resolution. Crop phonological information was used to train each pixel intelligently for interpretation of unanalyzed NDVI data into crops. Eight days of time series data was used for identification and mapping of various crops on the basis of their phenology for the years 2008, 2010 and 2013. Error matrix was prepared to reveal mapping accurateness and ground truthing was also done in particular canal commands within the Indus basin. Furthermore, the temporal variation in cropped area was determined and for accuracy check, secondary data was matched with prepared maps. LULC maps for year 2008, 2010 and 2013 were defined for Rabi and kharif seasons.

Suggested Citation

  • Muhammad Mohsin Khan & Muhammad Jehanzeb Masud Cheema & Talha Mahmood & Saddam Hussain & Hafiz Muhammad Nauman & Mohsin Nawaz & Muhammad Saifullah, 2020. "Crop Area Mapping By Intelligent Pixel Information Inferred Using 250m Modis Vegetation Timeseries In Transboundary Indus Basin," Big Data In Water Resources Engineering (BDWRE), Zibeline International Publishing, vol. 1(2), pages 32-35, February.
    • Handle: RePEc:zib:zbdwre:v:1:y:2020:i:2:p:32-35
    DOI: 10.26480/bdwre.02.2020.32.35
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    References listed on IDEAS

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    1. Cheema, M.J.M. & Bastiaanssen, W.G.M., 2010. "Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis," Agricultural Water Management, Elsevier, vol. 97(10), pages 1541-1552, October.
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    Cited by:

    1. Saddam Hussain & Saba Malik & Muhammad Jehanzeb Masud Cheema & Muhammad Umair Ashraf & Muhammad Mazhar Iqbal & Sikandar Ali & Lubna Anjum & Muhammad Aslam & Hassan Afzal, 2020. "An Overview On Emerging Water Scarcity Challange In Pakistan, Its Consumption, Causes, Impacts And Remedial Measures," Big Data In Water Resources Engineering (BDWRE), Zibeline International Publishing, vol. 1(1), pages 22-31, March.

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