Operational Model Based Regional Estimation using Remote Sensing Data

Authors

  • Muhammad Hamza National Center for Big Data and Cloud Computing (NCBC), Department of Computer Science & Information Technology, University of Engineering and Technology, Peshawar, Pakistan
  • Nasru Minallah NCBC, Department of Computer System Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • Waleed Khan NCBC, Department of Computer System Engineering, University of Engineering and Technology, Peshawar, Pakistan

Keywords:

Evapotranspiration, Sentinel-2, Sentinel-3, ECMWF, ERA-5, European Space Agency

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 nations standing 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. This research has been designed 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.

References

N. Bhattarai, S. B. Shaw, L. J. Quackenbush, J. Im, and R. Niraula, “Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate,” Int. J. Appl. Earth Obs. Geoinf., vol. 49, pp. 75–86, Jul. 2016, doi: 10.1016/J.JAG.2016.01.010.

W. Ishaque, R. Tanvir, and M. Mukhtar, “Climate Change and Water Crises in Pakistan: Implications on Water Quality and Health Risks,” J. Environ. Public Health, vol. 2022, 2022, doi: 10.1155/2022/5484561.

T. Khan, H. Nouri, M. J. Booij, A. Y. Hoekstra, H. Khan, and I. Ullah, “Water Footprint, Blue Water Scarcity, and Economic Water Productivity of Irrigated Crops in Peshawar Basin, Pakistan,” Water 2021, Vol. 13, Page 1249, vol. 13, no. 9, p. 1249, Apr. 2021, doi: 10.3390/W13091249.

C. J. Tucker and P. J. Sellers, “Satellite remote sensing of primary production,” Int. J. Remote Sens., vol. 7, no. 11, pp. 1395–1416, 1986, doi: 10.1080/01431168608948944.

M. Aslam, “Agricultural Productivity Current Scenario, Constraints and Future Prospects in Pakistan,” Sarhad J. Agric., vol. 32, no. 4, pp. 289–303, Oct. 2016, doi: 10.17582/JOURNAL.SJA/2016.32.4.289.303.

W. Khan et al., “On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop,” IEEE Access, vol. 8, pp. 103020–103033, 2020, doi: 10.1109/ACCESS.2020.2998079.

N. Minallah, M. Tariq, N. Aziz, W. Khan, A. ur Rehman, and S. B. Belhaouari, “On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network,” PLoS One, vol. 15, no. 9, p. e0239746, Sep. 2020, doi: 10.1371/JOURNAL.PONE.0239746.

N. Minallah and W. Khan, “Comparison of Neural Networks and Support Vector Machines for the Mass Balance Ablation Observation of Glaciers in Baltoro Region,” J. Inf. Commun. Technol. Robot. Appl., vol. 9, no. 2, pp. 37–45, Apr. 2019, Accessed: Feb. 07, 2024. [Online]. Available: https://jictra.com.pk/index.php/jictra/article/view/106

T. Hák, S. Janoušková, and B. Moldan, “Sustainable Development Goals: A need for relevant indicators,” Ecol. Indic., vol. 60, pp. 565–573, Jan. 2016, doi: 10.1016/J.ECOLIND.2015.08.003.

N. Ali, A. Jaffar, M. Anwer, D. M. Raza, and N. Ali, “The Economic Analysis of Tobacco Industry: A Case Study of Tobacco Production in Pakistan.” Jan. 05, 2015. Accessed: Feb. 07, 2024. [Online]. Available: https://papers.ssrn.com/abstract=2600400

Sajjad, Z. U. Haq, J. Iqbal, and M. F. Shahzad, “Understanding the Profitability, Supply, and Input Demand of Tobacco Farms in Khyber Pakhtunkhwa, Pakistan,” Econ. 2022, Vol. 10, Page 59, vol. 10, no. 3, p. 59, Mar. 2022, doi: 10.3390/ECONOMIES10030059.

R. H. Qureshi and M. Ashraf, Water security issues of agriculture in Pakistan. 2019. [Online]. Available: https://www.paspk.org/wp-content/uploads/2019/06/PAS-Water-Security-Issues.pdf

D. T. Lauer, S. A. Morain, and V. V. Salomonson, “The Landsat program: Its origins, evolution, and impacts,” Photogramm. Eng. Remote Sensing, vol. 63, no. 7, pp. 831–838, 1997.

M. A. Wulder et al., “Current status of Landsat program, science, and applications,” Remote Sens. Environ., vol. 225, pp. 127–147, May 2019, doi: 10.1016/J.RSE.2019.02.015.

M. Berger, J. Moreno, J. A. Johannessen, P. F. Levelt, and R. F. Hanssen, “ESA’s sentinel missions in support of Earth system science,” Remote Sens. Environ., vol. 120, pp. 84–90, May 2012, doi: 10.1016/J.RSE.2011.07.023.

R. C. Bispo, F. B. T. Hernandez, I. Z. Gonçalves, C. M. U. Neale, and A. H. C. Teixeira, “Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach,” Agric. Water Manag., vol. 271, p. 107763, Sep. 2022, doi: 10.1016/J.AGWAT.2022.107763.

V. Burchard-Levine et al., “A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems,” Glob. Chang. Biol., vol. 28, no. 4, pp. 1493–1515, Feb. 2022, doi: 10.1111/GCB.16002.

A. Garcia-Pedrero, M. Lillo-Saavedra, D. Rodriguez-Esparragon, and C. Gonzalo-Martin, “Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System,” IEEE Access, vol. 7, pp. 158223–158236, 2019, doi: 10.1109/ACCESS.2019.2950371.

U. Kumar et al., “Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment,” J. Indian Soc. Remote Sens., vol. 49, no. 8, pp. 1939–1950, Aug. 2022, doi: 10.1007/S12524-021-01367-W.

S. Chintala, T. S. Harmya, B. V. N. P. Kambhammettu, S. Moharana, and S. Duvvuri, “Modelling high-resolution Evapotranspiration in fragmented croplands from the constellation of Sentinels,” Remote Sens. Appl. Soc. Environ., vol. 26, p. 100704, Apr. 2022, doi: 10.1016/J.RSASE.2022.100704.

S. Czapiewski and D. Szumińska, “An overview of remote sensing data applications in peatland research based on works from the period 2010–2021,” Land, vol. 11, no. 1, p. 24, Jan. 2022, doi: 10.3390/LAND11010024/S1.

Y. Cui, L. Song, and W. Fan, “Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin,” J. Hydrol., vol. 597, p. 126176, Jun. 2021, doi: 10.1016/J.JHYDROL.2021.126176.

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Published

2024-03-02

How to Cite

Muhammad Hamza, Nasru Minallah, & Khan, W. (2024). Operational Model Based Regional Estimation using Remote Sensing Data. International Journal of Innovations in Science & Technology, 6(1), 185–200. Retrieved from https://journal.50sea.com/index.php/IJIST/article/view/653

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