[ Log In | Register]
! Skip Navigation Links
Skip Navigation Links
Image of Journal of Applied Nonlinear Dynamics
ISSN:2325-6192 (print)
ISSN:2325-6206 (online)
Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal

Email: aml@fe.up.pt

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn

Modeling Wheat Evapotranspiration in Semi-Arid Regions Using Satellite Remote Sensing

Journal of Environmental Accounting and Management 13(2) (2025) 143--150 | DOI:10.5890/JEAM.2025.06.003

Tarek Bouregaa

Plant and animal production improvement laboratory, Department of agronomy, Faculty of nature and life sciences, University Ferhat Abbas-Sétf1,19000, Algeria

Download Full Text PDF

 

Abstract

Efficient water management is crucial in semi-arid regions like Setif, Algeria, where wheat production faces challenges due to water scarcity. Recent research has highlighted the potential of remote sensing for retrieving crop water requirements through mathematical modeling and analysis of vegetation indices. This study investigates the application of linear regression models to estimate wheat evapotranspiration in the semi-arid Setif region of Algeria, contributing to the understanding of data-driven dynamical systems in agricultural contexts. Utilizing Sentinel 2 data, we derived vegetation indices (NDVI, NDRE, MSAVI, ReCI, NDMI) and analyzed their relationship with evapotranspiration values obtained from a smartphone application through linear regression analysis. Results revealed strong correlations between the indices and crop water requirements, particularly during the January-March period (R2 values exceeding 0.9 for several indices), with corresponding root mean square error (RMSE) values as low as 1.68 mm/decade. These findings demonstrate the efficacy of satellite remote sensing and vegetation indices, coupled with linear regression techniques, for modeling and estimating crop water needs in semi-arid environments.

References

  1. [1]  Khellaf, M. and Benhamouda, L. (2018), The impact of climate change on agricultural production in Algeria, Agronomy for Sustainable Development, 38(1), 1-13. https://doi.org/10.1007/s13593-017-0445-x.
  2. [2]  FAO. (2018), The state of food security and nutrition in the world 2018, Food and Agriculture Organization of the United Nations.
  3. [3]  Bouri, A. and Le Coq, J. (2010), Climate change impact on agricultural water requirements in semi-arid regions of Algeria, Agricultural Water Management, 97(4), 587-596. https://doi.org/10.1016/j.agwat.2009.12.012.
  4. [4]  Foreign Agricultural Service, USDA. Grain and Feed Annual: Algeria. U.S. Department of Agriculture, 30 March 2023. Available at: https://apps.fas.usda.gov/gainfiles/202303/20230330.pdf.
  5. [5]  The Maghreb Times. (2024), Algeria Expected to Import 14 MillionTonnes of Cereals in 2024. Ground News, 26 June 2024. Available at: https://ground.news/article/algeria-expected-to-import-14-million-tonnes-of-cereals-in-2024.
  6. [6]  Food and Agriculture Organization (FAO), GIEWS Country Brief: Algeria. Global Information and Early Warning System, FAO, 2024. Available at: https://www.fao.org/giews/countrybrief/country.jsp?code=DZA.
  7. [7]  Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998), Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56.
  8. [8]  Shuttleworth, W.J. (1993), Evaporation, In Handbook of Hydrology (pp. 4.1-4.73), McGraw-Hill.
  9. [9]  Allen, R.G., et al. (2011), Evapotranspiration and irrigation water requirements. FAO Irrigation and Drainage, Paper No. 56.
  10. [10]  He, Y., et al. (2019), A review of eddy covariance techniques for measuring ecosystem carbon exchange and evapotranspiration, Agricultural and Forest Meteorology, 268, 135-153. https://doi.org/10.1016/j.agrformet.2019.01.001.
  11. [11]  Nemani, R.R., Running, S. W., and Pierce, L. L. (1993), A global data set of leaf area index from satellite data, Remote Sensing of Environment, 44(2-3), 73-87. https://doi.org/10.1016/0034-4257(93)90065-Q.
  12. [12]  Goward, S.N. and Dye, D.G. (2010), Vegetation indices for monitoring terrestrial vegetation: A review, Remote Sensing of Environment, 114(1), 252-271. https://doi.org/10.1016/j.rse.2009.08.013.
  13. [13]  Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., and Holtslag, A.A.M. (1998), A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. Journal of Hydrology, 212-213, 198-212.
  14. [14]  Su, Z. (2001), A Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes from point to continental scale. In: Su, Z. and C.E. Jacobs (Eds.) Advanced earth observation -- land surface climate, final report. pp. 184: Publications of the National Remote Sensing Board (BCRS), USP-2.
  15. [15]  Allen, R.G., Tasumi, M., and Trezza, R. (2007), Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) -- Model, ASCE J. Irrigation and Drainage Engineering, 133(4), 380-394.
  16. [16]  Jensen, J.R. (2000), Remote Sensing of the Environment: An Earth Resource Perspective, Pearson Education.
  17. [17]  Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2015), Remote Sensing and Image Interpretation, John Wiley \& Sons.
  18. [18]  Mu, Q. and He, Y. (2010), A review of remote sensing applications in agricultural water management, Agricultural Water Management, 97(3), 129-140. https://doi.org/10.1016/j.agwat.2010.02.011.
  19. [19]  Allen, R.G., Tasumi, M., and Trezza, R. (2005), An overview of remotely sensed actual evapotranspiration: An operational approach for water resources management, Agricultural Water Management, 70(1-3), 1-25. https://doi.org/10.1016/j.agwat.2004.06.005.
  20. [20]  Bouten, W., Van den Bosch, C., Van der Tol, C., and Van der Kruk, J. (2012), Estimating evapotranspiration from maize in a humid environment using remotely sensed surface temperature and vegetation indices. Agricultural and Forest Meteorology, 161, 129-138. https://doi.org/10.1016/j.agrformet.2012.01.008.
  21. [21]  Bezerra, B.G., da Silva, B.B., dos Santos, C.A., and Bezerra, J.R. (2005), SEBAL model for estimating actual evapotranspiration from remote sensing data, Journal of Irrigation and Drainage Engineering, 131(4), 384-393. https://doi.org/10.1061/(ASCE)0733-9437.
  22. [22]  Bouregaa, T. (2019), Impact of climate change on yield and water requirement of rainfed crops in the Setif region, Management of environmentalquality: An International Journal, 30(4), 851-863.
  23. [23]  Agence Nationale d'Interm{e}diation et de R{e}gulation Fonci\`{e}re (ANIREF) (2023), Monographie wilaya de S{e}tif (online), Retrieved from : https://www.aniref.dz/documentspdf/monographies/MONOGRAPHIE\%20WILAYA\%20SETIF.pdf. Accessed on 20.03.2024.
  24. [24]  J{u}nior, W.M., Valeriano, T.T.B., and de Souza Rolim, G. (2019), EVAPO: A smartphone application to estimate potential evapotranspiration using cloud gridded meteorological data from NASA-POWER system, Computers and Electronics in Agriculture, 156, 187-192.
  25. [25]  Bai, J., Hoogenboom, G., McClendon, R.W., and Urich, P. (2010), Evaluation of NASA satellite- and model-derived weather data for simulation of maize yield potential in China, Agronomy Journal, 102(1), 9-21.
  26. [26]  Bandaru, V., Martin, K.L., Campbell, P., Bair, K., and Volk, T.A. (2017), Impact of biases in gridded weather datasets on biomass estimates of short rotation woody cropping systems. BioEnergy Research, 10(1), 135-149.
  27. [27]  Kumar, U., Srivastava, A., Kumari, N., Rashmi, Sahoo, B., Chatterjee, C., and Raghuwanshi, N.S. (2021), Evaluation of spatio-temporal evapotranspiration using satellite-based approach and lysimeter in the agriculture dominated catchment, Journal of the Indian Society of Remote Sensing, 49, 1939-1950, https://doi.org/10.1007/s12524-021-01367-w.
  28. [28]  Nagy, A., Kiss, N.E., Buday-Bodi, E., Magyar, T., Cavazza, F., Gentile, S.L., Abdullah, H., Tamas, J., and Feher, Z.Z. (2024), Precision estimation of crop coefficient for maize cultivation using high-resolution satellite imagery to enhance evapotranspiration assessment in agriculture, Plants, 13, 1212. https://doi.org/10.3390/ plants13091212.
  29. [29]  Wang, X., Lei, H., Li, J., Huo, Z., Zhang, Y., and Qu, Y. (2023), Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model, Agricultural Water Management, 282, 108294.
  30. [30]  Adamala S., Rajwade Y.A., and Reddy Y.V.K. (2016), Estimation of wheat crop evapotranspiration using NDVI vegetation index, Journal of Applied and Natural Science, 8(1), 159-166. https://doi.org/10.31018/jans.v8i1.767.
  31. [31]  Chao, Z., Liu, J., Dong, T., Pattey, E., Shang, J., Tang, M., Cai, H., and Saddique Q. (2019), Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand, Remote Sensing, 11(14), 1684. https://doi.org/10.3390/rs11141684.
  32. [32]  FAO (Food and Agriculture Organization) (2023), Remote sensing determination of evapotranspiration -- Algorithms, strengths, weaknesses, uncertainty and best fit-forpurpose. Food and Agriculture Organization of the United Nations. Cairo.

Copyright © 2011-2024 L & H Scientific Publishing. All rights reserved. Wildcard SSL Certificates