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Operational Model Based Regional Estimation using Remote Sensing Data

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  • Muhammad Hamza

    (National Center for Big Data and Cloud Computing (NCBC), Department of Computer Science & Information Technology, University of Engineering and Technology, Peshawar, Pakistan)

Abstract

Water serves as the vital hub for sustaining life. There is indisputable evidence that the progress of agriculture, which relies directly on water resources, bears direct responsibility for the current global human population. While undeniably invaluable, our planet's freshwater reserves face a mounting challenge in keeping up with the ever-expanding global population. This is primarily due to inefficiencies prevalent in various residential water applications, with irrigation practices in developing nationsstanding out as a significant contributor to this issue. As our communities continue to grow, it becomes increasingly imperative to address these inefficiencies to ensure sustainable access to this precious resource for generations to come. This dilemma is particularly concerning given the projection of continued population expansion. Concerning irrigation, it is widely acknowledged that more than 60% of water allocated for agricultural purposes is presently being administered in excess, leading to substantial annual wastage. To obtain a precise estimation of the water needed for crop production, it is imperative to devise, develop, and implement a practical and effective method. Employing manual techniques, such as utilizing a lysimeter, for gauging a structure's water requirements is both subjective and financially demanding. Thisresearch has beendesigned to provide a comprehensive measurement of daily ET over a wide geographical area, offering detailed field-specific information. This research work is carried out by utilizing the European Space Agency satellites i.e., Sentinel 2 and 3, and ECMWF meteorological data. The Sentinel-2 data was processed to calculate the biophysical variables, structural parameters, fraction of green vegetation, and aerodynamic roughness. Sentinel 3 data was used to get the land surface temperature. The whole data is then processed to estimate the ET of the chosen area which is discussed in the materials and methods section. Actual water requirement and the water provided to the tobacco crops were compared. The results of the study reveal that estimated ET values were inline with the average surveyed tobacco field values that represents the consistency. However, a significant discrepancy arises due to irregular irrigation practices, indicating a lack of consideration for ET values among farmers. This oversight, coupled with unadjusted irrigation timing and methods, contributes to variance between computed and required ET values, attributed to factors such as human error, insufficient rainfall, and improper practices.

Suggested Citation

  • Muhammad Hamza, 2024. "Operational Model Based Regional Estimation using Remote Sensing Data," International Journal of Innovations in Science & Technology, 50sea, vol. 6(1), pages 185-200, March.
  • Handle: RePEc:abq:ijist1:v:6:y:2024:i:1:p:185-200
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    References listed on IDEAS

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    1. Nasru Minallah & Mohsin Tariq & Najam Aziz & Waleed Khan & Atiq ur Rehman & Samir Brahim Belhaouari, 2020. "On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    2. Sajjad & Zahoor ul Haq & Javed Iqbal & Muhammad Faisal Shahzad, 2022. "Understanding the Profitability, Supply, and Input Demand of Tobacco Farms in Khyber Pakhtunkhwa, Pakistan," Economies, MDPI, vol. 10(3), pages 1-20, March.
    3. Gonçalves, I.Z. & Ruhoff, A. & Laipelt, L. & Bispo, R.C. & Hernandez, F.B.T. & Neale, C.M.U. & Teixeira, A.H.C. & Marin, F.R., 2022. "Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil," Agricultural Water Management, Elsevier, vol. 274(C).
    4. Bispo, R.C. & Hernandez, F.B.T. & Gonçalves, I.Z. & Neale, C.M.U. & Teixeira, A.H.C., 2022. "Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach," Agricultural Water Management, Elsevier, vol. 271(C).
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