Volume 26, Issue 81 (6-2026)
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Kabolizadeh M, Zareie S, Foroughi Rad M. (2026). Evaluating the relationship between two indeces of crop water stress and the SEBAL algorithm for estimating evapotranspiration using satellite images (Study area: agricultural lands of Dasht Abbas district, Dehlran city). jgs. 26(81),
URL: http://jgs.khu.ac.ir/article-1-4340-en.html
URL: http://jgs.khu.ac.ir/article-1-4340-en.html
1- Shahid Chamran University of Ahvaz, Remote Sensing and GIS department , m.kabolizade@scu.ac.ir
2- Shahid Chamran University of Ahvaz, Remote Sensing and GIS department
2- Shahid Chamran University of Ahvaz, Remote Sensing and GIS department
Abstract: (5425 Views)
There are various indicators to monitor and management of agricultural water resources in arid and semi-arid countries including Iran, some of which can be extracted directly in situ, and some can be retrieved using remote sensing technology and satellite images. Aim of this study is to propose the most appropriate and efficient indicators of agricultural water resource management for achieving maximum production and maximum water efficiency using remote sensing technology, therefore, Crop Water Stress Index (CWSI) and Surface Energy Balance Algorithm (SEBAL) were used to estimate Evapotranspiration (ET). In the first step, ET rate was calculated using SEBAL algorithm for six Landsat 8 satellite images related to the wheat growth period. Then, zoning of this index was done in the range of zero to one, in four categories of very low, low, medium and high, which respectively indicate the lowest to the highest amount of ET. In next step, CWSI was calculated based on Idso equation, and its results show different changes both in cold season and in warm months. Comparison of ET and CWSI shows a significant relationship between these two indices in warm months, while in cold months, no significant relationship can be seen. These findings along with the established relationship between ET and CWSI can inform water management strategies in arid environments for sustainable crop production.
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42. Akbari, E. Ebrahimi, M. Faizizadeh, B. and Nejad Soleimani, H. (2014). Evaluating Land Surface Temperature related to the Land use Change Detection by Satellite Image (Case study: Taleghan Basin). Geography and Environmental Planning, 26, Number 4, 151-170. (in Persian)
43. Allen, RG., Bastiaanssen, WGM., Wright, JL., Morse, A., Tasumi, M. & Trezza, R. )2002(. Evapotranspiration from Satellite Images for Water Management and Hydrologic Balances. Proceedings of the 2002 ICID conference. Montreal, Canada, 1-12.
44. Allen, RG., Pereira, LS., Raes, D. & Smith, M. (1998). Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage paper. No. 56, FAO, Rome, Italy.
45. Bezerra, BG., da Silva, BB., dos Santos, CA., & Bezerra, JR. (2017). Actual evapotranspiration estimation using remote sensing: comparison of SEBAL and SSEB approaches. Advances in Remote Sensing. 4(03), 234. [DOI:10.4236/ars.2015.43019]
46. Bhattarai, N., Shaw, SB., Quackenbush, LJ., Im, J., & Niraula, R. (2016). Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate. International Journal of Applied Earth Observation and Geoinformation. 49, 75-86. [DOI:10.1016/j.jag.2016.01.010]
47. Costa-Filho, E., Chavez, JL. & Comas, L. (2020). Determining maize water stress through a remote sensing-based surface energy balance approach. Irrigation Science. 38, 501-518. [DOI:10.1007/s00271-020-00668-1]
48. Ebrahimi H. and Yazdani V. (2013). Estimating evapotranspiration in green landscape by using SEBAL Method (Case Study: Mellat Park of Mashhad). Journal of Water and Soil Conservation, 20(3), 133-151. DOI: 20.1001.1.23222069.1392.20.3.7.9 (in Persian)
49. Esfandiari, A. (2019). Identifying areas prone to sugarcane cultivation in Khuzestan province using satellite images and fuzzy logic. Master's thesis of Shahid Chamran University of Ahvaz. (in Persian)
50. Geerts, S. & Raes, D. (2009). Deficit Irrigation as an On-Farm Strategy to Maximize Crop Water Productivity in Dry Areas. Agriultural Water Management. 96, 1275-1284. [DOI:10.1016/j.agwat.2009.04.009]
51. Ghasemi, R. Abbasi, A. and Hossein E. (1401). Monitoring the water requirements of agricultural products using remote sensing data (case study: Astan Quds Razavi sample farm, Mashhad). 13th National Congress of Civil Engineering. Isfahan, Iran. (in Persian)
52. Daran RR. K, , Kendall, CD., Tsz, HL., Xin, Qiao., Trenton E.F., Hope NN. & Jiaming D.(2022). Crop water stress index computation approaches and their sensitivity to soil water dynamics. Agriultural Water Management. 31 May 2022, 107575 [DOI:10.1016/j.agwat.2022.107575]
53. Khorsand, A. Rezavardinejad, V. Askarzadeh, H. Majnoni Hari, A. Rahimi, A. and Besharat, S. (2018). Irrigation scheduling of Black Gram based on Crop Water Stress Index (CWSI) under drip irrigation. Iranian journal of soil and water research. 50 (9), 2125-2138. (in Persian)
54. Ki Khosravi, Q. and Hosseininia, N. (2018). Climatic zoning of Khuzestan using Demartin, Silianinov and Kopen methods. The second national conference of new ideas and technologies in geographic sciences, Zanjan. https://civilica.com/doc/1348914 (in Persian)
55. Morshedi, A. (1402). Estimation of actual evapotranspiration and water requirement of rose (Rosa damascena Mill.) using SEBAL algorithm. Soil and Water Management and Modeling. 3, 3. 20-36. (in Persian)
56. Nakhjavani Moghadam, M. M. and Ghahreman, B. (2008). Evaluation of crop conopy temperature associated with time of irrigation and yield of winter wheat. Agricultural Sciences and Industries. 22, 1, 4.101-112. (in Persian)
57. Namvar, A., Hadi, H., Seyed Sharifi, R. (2018). Role of exogenous phytoprotectants in mitigation of adverse effects of abiotic stresses. Journal of Iranian Plant Ecophysiological Research. 12(48), 103-128. (in Persian)
58. Nishan, B., Pradeep, W., Prasanna, HG. & Vijaya, GK. (2017). Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions. ISPRS Journal of Photogrammetry and Remote Sensing. 133, 2017, 128-141. [DOI:10.1016/j.isprsjprs.2017.10.010]
59. Pessarakli, M. (2011). Hand book of Plant and Crop Stress, 3rd edn. Published by Taylor & Francis Group.
60. Rahimian, M. H. and Pourmohammadi S. (2012). Estimation of Winter Wheat Actual Evapotranspiration under Stress Condition by Remote Sensing Data and Energy Balance Algorithm Case study: Azadegan Plain, Khuzestan. Journal of water research in agriculture. 26, 2, 235-249. (in Persian)
61. Rahimpour, M. Karimi, N. Behifar, M. and Shesh Angosht, S. (2018). Evaluation of Operational Atmospheric Correction effects on Surface Reflectance and Albedo using Landsat-OLI images. Journal of Geospatial Information Technology. 7, 2, page 113-131. (in Persian) [DOI:10.29252/jgit.7.2.113]
62. Restu, T., Yeni, H. & Suria DT. (2020). Estimating Crop Water Stress of Sugarcane in Indonesia Using Landsat 8. International Conference on Computer Science and Its Application in Agriculture (ICOSICA). Bogor, Indonesia, pp. 1-4. [DOI:10.1109/ICOSICA49951.2020.9243255]
63. Sajad. J., Shahrokh, Z. & Dev, N. (2021). Assessing Crop Water Stress Index of Citrus Using In-Situ Measurements, Landsat, and Sentinel-2 Data. International Journal of Remote Sensing. 42:5, 1893-1916. [DOI:10.1080/01431161.2020.1846224]
64. Savari, M. Barfizadeh, L. and Asadi, Z. (1400). Effects of Social Capital on Achieving Food Security in Drought Conditions (Case Study: Rural Settlements in Dorud County). Geography and Environmental Planning. 32(4), 1-28. (in Persian)
65. Simaie, E. Homaee, Mehdi and Norouzi A. (2012). Evaluating SEBAL model to estimate evapotranspiration using MODIS and TM sensors data. Journal of Water and Soil Resources Protection. 2 (4), 30. (in Persian)
66. Tuteja, N. & Gill, SS. (2013). Crop Improvement under Adverse Conditions. Published by Springer. [DOI:10.1007/978-1-4614-4633-0]
67. Zareie, S. & Kabolizadeh, M. (2020). The natural resources potential assessing aimed at territorial planning using time-varying space data of vegetation index and LST. Environtal Monitoring and Assessment. 192, 503. [DOI:10.1007/s10661-020-08476-y] [PMID]
68. Zynali, B., Asghari SS., Saffarian ZV. (2017). Monitoring and Forecast of Drought in Urmia Lake Basin by SEPI Index and ANFIS Model. Journal of Spatial Analysis Environmental Hazards. 4 (1) :73-96. (in Persian)
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