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mokhtaran, A., Mohammady, M., Dehghanisanij, H. (2025). Preparation of Rice Water Productivity Maps Using Remote Sensing Methods in Khuzestan Watersheds. , 38(4), 311-339. doi: 10.22092/jwra.2024.367168.1057
ali mokhtaran; Maryam Mohammady; Hossein Dehghanisanij. "Preparation of Rice Water Productivity Maps Using Remote Sensing Methods in Khuzestan Watersheds". , 38, 4, 2025, 311-339. doi: 10.22092/jwra.2024.367168.1057
mokhtaran, A., Mohammady, M., Dehghanisanij, H. (2025). 'Preparation of Rice Water Productivity Maps Using Remote Sensing Methods in Khuzestan Watersheds', , 38(4), pp. 311-339. doi: 10.22092/jwra.2024.367168.1057
mokhtaran, A., Mohammady, M., Dehghanisanij, H. Preparation of Rice Water Productivity Maps Using Remote Sensing Methods in Khuzestan Watersheds. , 2025; 38(4): 311-339. doi: 10.22092/jwra.2024.367168.1057

Preparation of Rice Water Productivity Maps Using Remote Sensing Methods in Khuzestan Watersheds

پژوهش آب در کشاورزی
Article 3, Volume 38, Issue 4, February 2025, Pages 311-339    PDF (2.24 M)
Document Type: Research Paper
DOI: 10.22092/jwra.2024.367168.1057
Authors
ali mokhtaran* 1; Maryam Mohammady2; Hossein Dehghanisanij3
1Assistant Prof., Agricultural Engineering Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, (AREEO), Ahwaz, Iran.
2MSc in Remote Sensing and Geographical Information System, Faculty of Geography, University of Tehran, Tehran, Iran
3Professor, Agricultural Engineering Research Institute (AERI), (AREEO), Karaj, Iran.
Abstract
This research aimed to determine rice cultivation map (RCM) for different managements of planting and irrigation (traditional cultivation with permanent flooding and Khoshkeh-kari" with intermittent irrigation) from June to November 2023 in Khuzestan, using vegetation-, temperature- and soil-indicators and remote sensing data. In this research, the temporal-spatial variations of rice water consumption were evaluated and analyzed based on the value of actual water consumption (ETa) and water productivity (WPET). The Random Forest algorithm with Machine Learning technique was used to classify and achieve the rice cultivated area in the province. Sentinel 2, Landsat 9 and WAPOR product bands were used to calculate the indices and WAPOR database was used in the GEE platform environment to determine ETa and WPET. The results showed that the area under rice cultivation for the traditional system in the entire province is 136,770 ha and 43,172 ha for Khoshkeh-kari method. In these conditions, the overall accuracy of rice field separation according to the type of cultivation was around 99.65% and the Kappa coefficient was 0.87. The total ETa of rice in 2023 for Khuzestan Province was 1.62 BCM, with 1.27 BCM for the total ETa of the traditional cultivation and 354 MCM in Khoshkeh-kari. The results showed that, although the farmers tried to avoid crop yield reduction by daily irrigation in the last 45 days of the growth period, the weighted average of ETa reduction and WPET improvement in this technology compared to the traditional method was, respectively, 13% and 8%. Meanwhile, as long as the periodicity of irrigation (Irrigation interval of 2 to 4 days) was observed in the Khoshkeh-kari, the rate of ETa reduction and WPET improvement in this technology was 24% and 16%, respectively. The results of this research, compared to the field data resulting from the applied research works in the province, estimated the efficiency of water use in rice fields with traditional cultivation at 27% and for Khoshkeh-kari at 34%. This shows that if necessary, rice cultivation should be done by Khoshkeh-kari with intermittent irrigation management and in areas that are in good condition in terms of soil, water, underground water level and drainage engineering.
Keywords
Actual water consumption; Khoshkeh-kari; Permanent flood irrigation; intermittent irrigation
References
  1. افشاری­پور، سید کریم و حمزه، سعید، علوی پناه، سیدکاظم و مقبلی­دامنه، اسماعیل.1398. ارزیابی میزان بهره‌وری آب کشاورزی با استفاده از تصاویر ماهواره‌ای و مدل WATPRO (مطالعه موردی؛ اراضی تحت کشت گندم حوزه آبریز دشت جیرفت). تحقیقات منابع آب ایران. 15(1) ، صص. 45-58.
  2. سامانه نیاز آبی گیاهان زراعی و باغی کشور. http://niwr.ir
  3. گیلانی، عبدالعلی، آبسالان، شکراله و جلالی، سامی، مقایسه روش خشکه‌کاری با شیوه‌های رایج کاشت ارقام برنج از نظر میزان آب مصرفی، سازمان تحقیقات، آموزش و ترویج کشاورزی، گزارش نهایی پروژه تحقیقاتی، شماره ثبت: 49802. 27 صص.
  4. گیلانی،عبدالعلی، 1398. مدیریت تولید برنج در روش خشکه‌کاری، موسسه تحقیقات برنج کشور، نشریه فنی، ش41، 23 صص.
  5. فتحی، مهدیه و شاه‌حسینی، رضا، بهبود دقت شناسایی مزارع برنج با استفاده از تصاویر سری زمانی دمای سطح زمین ماهواره لندست8 و الگوریتم‌های یادگیری ماشین، فصلنامه علمی- پژوهشی اطلاعات جغرافیایی (سپهر)،125(32)، صص.53-66.doi: 10.22131/sepehr.2023.535693.2777
  6. مختاران، علی، گیلانی، عبدالعلی، سپهری صادقیان، سالومه و ورجاوند، پیمان، بازچرخانی زهاب در مزارع کشت و صنعت نیشکر جنوب استان خوزستان برای کشت گیاهان زراعی (گندم و برنج) ، نشریه فنی، موسسه تحقیقات فنی و مهندسی کشاورزی (AERI) ، شماره ثبت 61936، 48 صص.
  7. مختاران، علی، گیلانی، عبدالعلی، جلالی، سامی، بهبهانی، لیلا، رضایی، مجتبی و تجددی طلب، کبری، 1402. بررسی تأثیر دور آبیاری در سامانه قطره‌ای نواری بر عملکرد برنج و تغییرات شوری خاک با روش کشت مستقیم در بستر خشک در دشت خوزستان، نشریه پژوهش آب در کشاورزی،37 (2) ، صص.158-139. doi:10.22092/jwra.2023.359561.935
  8. مختاران، علی، دهقانی­سانیج، حسین و محمدی، مریم، تشخیص سطح سامانه‌های مختلف کشت مزارع برنج مبتنی بر مدیریت آبیاری با استفاده از تکنیک یادگیری ماشین، دستنامه فنی، موسسه تحقیقات فنی و مهندسی کشاورزی (AERI) ، شماره ثبت 65242، 70 صص.
  9. مختاران، علی، ناصری، عبدعلی و کشکولی. حیدرعلی، تعیین ضخامت فصل مشترک آب شور-شیرین در اراضی تحت آبیاری و آب زیرزمینی شور و کم عمق، دوازدهمین کنفرانس هیدرولیک ایران، تهران.
  10. مسکینی ویشکایی، فاطمه، گیلانی، عبدالعلی و مختاران، علی، بررسی تأثیر شیوه کاشت برنج بر روی پشته‌های بلند بر مصرف آب و شوری خاک سطحی در روش خشکه‌کاری، هفدهمین کنگره علوم خاک ایران و چهارمین همایش ملی مدیریت آب در مزرعه، کرج، ایران.
  11. یعقوبی، بیژن و رجبیان، مریم، مروری برکشت مستقیم برنج با تأکید بر مدیریت علف‌های هرز، موسسه تحقیقات برنج کشور، نشریه فنی، ش 37، 51 صص.
  12. یوسفی، حسین، کردی، فاطمه، محبتی، فرهاد و قاسمی، لیلا، برآورد آب مصرفی بخش کشاورزی کشور ایران و ارزیابی نتایج به‌دست آمده از سامانه WaPOR با داده‌های زمینی، اکوهیدرولوژی، 8 (3)، صص. 839-8259. doi: 10.22059/ije.2021.324474.1512 
  13. 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), PP. 159–166.
  14. Allen, R. G., Tasumi, M. and Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), PP. 380-394.
  15. Asghari Saraskanrood, S., Asadi, B. and Ghale, E., 2023. Caspian Journal of Environmental Sciences, 21(3), PP.725-735. doi: 10.22124/CJES.2023.6959.
  16. Ajour, S. A. 2021. Evaluation of FAO’s Water Productivity Portal (WAPOR) Yield Over the Bequaa Valley, Lebanon (Thesis for the degree of Master of Science to the Department of Landscape Design and Ecosystem Management of the Faculty of Agricultural and Food Sciences at the American University of Beirut.
  17. Alemayehu, T., Bastiaanssen, S., Bremer, K., Cherinet, Y., Chevalking, S. and Girma, M., 2020. Water Productivity Analyses Using WAPOR Database. A Case Study of Wonji, Ethiopia. Water-PIP technical report series. IHEDelft Institute for Water Education.
  18. Bastiaanssen, W.G.M., Cheema, M.J.M., Immerzeel, W.W., Miltenburg, I.J. and Pelgrum, H., 2012. Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model. Water Resources Research, 48, W11512.
  19. R.C., Hernandez. F.B.T., Gonçalves. I. Z., Neale. C.M.U. and Teixeira. A.H.C., 2022. Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach. Agricultural Water Management. 271.107763.

doi.org/10.1016/j.agwat.2022.107763.

  1. Blatchford, M.L., Mannaerts, C.M., Njuki, S. M., Nouri, H., Zeng, Y., Pelgrum, H. and Karimi, P., 2020a. Evaluation of WaPOR V2 evapotranspiration products across Africa. Hydrological Processes, 34(15), PP. 3200-3221.
  2. Blatchford, M., M Mannaerts, C., Zeng, Y., Nouri, H. and Karimi, P., 2020b. Influence of spatial resolution on remote sensing-based irrigation performance assessment using WaPOR data. Remote Sensing, 12(18), 2949.
  3. Bouman, B. A. M., Wang, H., Yang, X., Zhao, J. F. and Wang, C. G., 2002. Aerobic rice (Han Dao): a new way of growing rice in water-short areas, in Proceedings of the 12th InternationalSoil ConservationOrganization Conference (Beijing), PP. 175–181.
  4. Cabangon, R. J. and Abdullah, N.B., 2002. Comparing water input and water productivity of transplanted and direct-seeded rice production systems.Agricultural Water Management, 57, PP. 11–31.
  5. Brown, ch., P. Brumby, S., Guzder-Williams, B., Birch, T., Brooks Hyde, S., Mazzariello, J. and M.Tait, A., 2022. Dynamic World, near real-time global 10m land use land cover mapping. Scientific Data9(1), 251PP. doi: 10.1038/s41597-022-01307-4.
  6. Darvishzadeh, R., Matkan, A. A. and Eskandari, N., 2011. Evaluation of ALOS-AVNIR2 spectral indices for prediction of rice biomass. Journal of Geographical Landscape, 6, PP. 11-14. (In Persian).
  7. Dehghanisanij, H., 2012. Current agricultural water and soil resources and productivity in the Iranian highlands: measures and Improvements. Journal of Agriculture, Biotechnology & Ecology. 5(1), PP.1-14.
  8. Delegido, J., Verrelst, J., Rivera, J.P., Ruiz-Verdú, A. and Moreno, J., 2015. Brown and green LAI mapping through spectral indices. International Journal of Applied Earth Observation and Geoinformation, 35, PP. 350–358. doi: 1016/j.jag.2014.10.001.
  9. ESA .2020. https://www.esa.int/ESA_Multimedia/Images/2020/03/Rice_fields_Vietnam.
  10. 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 P.
  11. 2019. Food and agriculture organization of the United Nations. FAOSTAT: Crops. http://www.fao.org/faostat/en/#data/QC.
  12. and Delft, I., 2019. WaPOR quality assessment. Technical report on the data quality of the WaPOR FAO database version 1.0. Rome. 134P.
  13. 2020. WaPOR database methodology: Version2 release, Rome.
  14. FAO & World Bank. 2022. Irrigating from space–Using remote sensing for agricultural water management. Investment brief. Rome. doi: 10.4060/cc3745en.
  15. Feyisa, G.L., Meilby, H., Fensholt, R. & Proud, S.R., 2014. AutomatedWater Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, PP. 23–35. doi:10.1016/j.rse.2013.08.029.
  16. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 202(1), PP.18-27.
  17. Gopal, R., Jat, R.K., Kumar, V., Alam, M.M., Jat, M.L., Mazid, M.A., Saharawat, Y.S., Mcdonald, A. and Gupta, R., 2010. Direct dry seeded rice production technology and weed management in rice-based systems.
  18. Huete, A., Didan, k., Miura, t., Rodriguez, E.P., Gao, X. and Ferreira. L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices.
  19. Javadian, M., Behrangi, A., Gholizadeh, M. and Tajrishy, M., 2019. METRIC and WaPOR estimates of evapotranspiration over the Lake Urmia Basin: comparative analysis and composite assessment. Water, 11(8), 1647.
  20. Inoue, S., Ito, A. and Yonezawa, C., 2020. Mapping Paddy fields in Japan by using Sentinel 1 SAR time series supplemented by Sentinel 2 images on Google Earth Engine, Remote Sensing. 12(10):1622.
  21. Izaddoost, H., Samizadeh, H., Rabiei, B. and Abdollahi, S., 2013. Evaluation of salt tolerance in rice (Oryza sativa L.) cultivars and lines with emphasis on stress tolerance indices. Cereal Research, 3, PP.167-180. (In Persian).

doi.20.1001.1.22520163.1392.3.3.1.2.

  1. Karimi, P., Molden, D., Bastiaanssen, W.G.M. and Cai, X., 2012. Water accounting to assess use and productivity of water: evolution of a concept and new frontiers. In Godfrey, J. M.; Chalmers, K. (Eds). Water accounting: international approaches to policy and decision-making. Cheltenham, UK: Edward Elgar. PP. 76-88.
  2. Kaplan, G. and Avdan, U., 2017. Water extraction technique in mountainous areas from satellite images, Journal of Applied Remote Sensing, 11(4), 046002.

doi: 10.1117/1.JRS.11.046002.

  1. Kaune, A. and Opstal, JV. 2020. Water Productivity Technical Report. Agência de desenvolvimento do Vale Zambeze (ADVZ). FutureWater, Report 195.
  2. Liu, L., Huang, J., Xiong, Q., Zhang, H., Song, P., Huang, Y., Dou, Y. and Wang, X., 2020. Optimal MODIS data processing for accurate multi- year paddy rice area mapping in China. GIScience & Remote Sensing, 57(5), PP. 687-703.
  3. Magidi, J., Nhamo, L., Mpandeli, S. and Mabhaudhi, T., 2021. Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine. 13, 876.

doi: 10.3390/rs13050876.

  1. McCallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S. and Nilsson, S., 2009. Satellite-based terrestrial production efficiency modeling. Carbon Balance and Management, 4(1), 8P.
  2. Monteith, J., 1972. Solar radiation and productivity in tropical ecosystems.Journal of applied ecology, 9(3), PP. 747-766.
  3. Pan, S., Tian, H., Dangal, S. R. S., Ouyang, Z., Tao, B., Ren, W., Lu, C. and Running, S. W., 2014. Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Ecosystem and Conservation Sciences FacultyPublications. 45.https://scholarworks.umt.edu/decspubs/45. doi: 10.1155/2014/965936.
  4. Roy, R., Chan, N.W. and Xenarios, S., 2016. Sustainability of rice production systems: an empirical evaluation to improve policy. Environ Dev Sustain 18: PP. 257–278. doi:10.1007/s10668-015-9638-x.
  5. Rongali,G.,Keshari,A.K.,Gosain,A.K.andKhosa,R.,2018.SplitWindow Algorithm for Retrieval of Land Surface Temperature Using Landsat8 Thermal Infrared Data. Journal of Geovisualization and Spatial Analysis. 14(2), PP. 1-19. doi:10.1007/s41651-018-0021-y
  6. Schmidt, J., Marques, M.R., Botti, S. and Marques, M.A., 2019. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput. Mater.  5, PP. 1–36.
  7. Torbick, N., Chowdhury, D., Salas, W. and Qi, J., 2017. Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sensing. 9(2), 119P. doi:10.3390/rs9020119
  8. D.M.G., Gao. J., Macinnis-Ng. C. and Shi. Y., 2021. Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine. Geo-Spatial Information Scinece. doi:10.1080/10095020.2021.1984183
  9. Veroustraete, F., Sabbe, H. and Eerens, H., 2002. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment, 83(3), PP. 376-399.
  10. Wang, J., Huang, J., Wang, X., Jin, M., Zhou, Z., Guo, Q., Zhao, Z., Huang, W., Zhang, Y. and Song, X., 2015. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat8 OLI vegetation indices time- series images. Journal of Zhejiang University- Science B, 16 (10), PP. 832-844.
  11. Wang, H., Boumam, B. A. M., Zhao, D., Wang, C. and Moya, P. F., 2002. “Aerobic rice in northern China: opportunities and challenges,” in Proceedings of the International Workshop on Water-Wise Rice Production, Water-Wise Rice Production, 8-11 April, eds B. A. M. Bouman, H. Hengsdijk, B. Hardy, P. S. Bindraban, T. P. Tuong, and J. K. Ladha (Los Baños: International Rice Research Institute), PP. 143–154.
  12. Yilma, W. A., Opstal, J. V., Karimi, P. and Bastiaanssen, W. G. M., 2017. Computation and Spatial Observation of Water Productivity in Awash River Basin. UNESCO-IHE, Delft.
  13. Zhan, P., Zhu, W. and Li, N., 2021. An automated rice mapping method based on flooding signals in synthetic aperture radar time series. Remote Sensing of Environment. 252:112112. doi: 1016/j.rse.2020.112112
  14. C., Dong. J., Xie. Y., Zhang. X. and Ge. Q., 2022. Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine. International Journal of Applied Earth Observations and Geoinformation, 112 (2022), 102888. doi:10.1016/j.jag.2022.102888
  15. Ziaeian Firouzabadi, P., Sayad Bidhendi, L. and Eskandari Noudeh, M., 2009. Mapping and acreage estimating of rice agricultural land using radarsat a satellite images. Physical Geography Research Quarterly, 41, PP.45-58. (In Persian).
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