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COMPUTER SCIENCE

Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies

Erion Bwambale1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, Ghana;3 Department of Agricultural and Biosystems Engineering, Makerere University, Kampala, UgandaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6772-8253View further author information
ORCID Icon,
Zita Naangmenyele1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, Ghana;4 Environmental Chemistry Division, CSIR-Water Research Institute, Tamale, GhanaView further author information
,
Parfait Iradukunda5 Department of Civil and Environmental Engineering, University of Zambia (UNZA), Lusaka, ZambiaView further author information
,
Komi Mensah Agboka6 International Centre of Insect Physiology and Ecology (icipe), Nairobi, KenyaView further author information
,
Eva A. Y. Houessou-Dossou7 Department of Civil Engineering, Pan African University, Institute for Basic Sciences, Technology and Innovation, Nairobi, KenyaView further author information
,
Daniel A. Akansake1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, GhanaView further author information
,
Michael E. Bisa8 Department of Water Resources and Irrigation Engineering, Arba Minch University, Arba Minch, EthiopiaView further author information
,
Abdoul-Aziz H. Hamadou1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, GhanaView further author information
,
Joseph Hakizayezu1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, GhanaView further author information
,
Oluwaseun Elijah Onofua1 West African Centre for Water, Irrigation and Sustainable Agriculture, University for Development Studies, Tamale, Ghana;2 Department of Agricultural Engineering, University for Development Studies, Tamale, GhanaView further author information
&
Sylvester R. Chikabvumbwa9 Department of Civil Engineering, University of Malawi-The Polytechnic, Blantyre, MalawiView further author information
|
Stefania Tomasiello10 Institute of computer science, Tartu Ulikool, EstoniaView further author information
(Reviewing editor) show all
Article: 2100573 | Received 31 Mar 2022, Accepted 07 Jul 2022, Published online: 19 Jul 2022

References

  • Abdalla, F. (2012). Mapping of groundwater prospective zones using remote sensing and GIS techniques: A case study from the central eastern desert, Egypt. Journal of African Earth Sciences, 70 , 8–22. https://doi.org/10.1016/j.jafrearsci.2012.05.003
  • Abioye, E. A., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., AbdRahman, M. K. I., Otuoze, A. O., Ramli, M. S. A., & Ijike, O. D. (2021). IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment. Information Processing in Agriculture, 8(2), 270–283. https://doi.org/10.1016/j.inpa.2020.05.004
  • Abioye, E. A., Abidin, M. S. Z., Aman, M. N., Mahmud, M. S. A., & Buyamin, S. (2021). A model predictive controller for precision irrigation using discrete lagurre networks. Computers and Electronics in Agriculture, 181(July 2020), 105953. https://doi.org/10.1016/j.compag.2020.105953
  • Abioye, E. A., Shukri, M., Abidin, Z., Saiful, M., Mahmud, A., Buyamin, S., Denis, O., Okino, A., Abiodun, A., & Musbau, O. (2023). Smart agricultural technology a data-driven kalman filter-PID controller for fibrous capillary irrigation. Smart Agricultural Technology, 3(April 2022), 100085. https://doi.org/10.1016/j.atech.2022.100085
  • Abu-Hashim, M., Sayed, A., Zelenakova, M., Vranayova, Z., & Khalil, M. (2021). Soil water erosion vulnerability and suitability under different irrigation systems using parametric approach and. Sustainability (Switzerland), 13(3), 1057. https://doi.org/10.3390/su13031057
  • Al-taani, A., Al-husban, Y., & Farhan, I. (2021). The Egyptian journal of remote sensing and space sciences land suitability evaluation for agricultural use using GIS and remote sensing techniques : The case study of Ma ’ an governorate, Jordan. The Egyptian Journal of Remote Sensing and Space Sciences, 24(1), 109–117. https://doi.org/10.1016/j.ejrs.2020.01.001
  • AL-Taani, A., Al-husban, Y., & Farhan, I. (2021). Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an governorate, Jordan. Egyptian Journal of Remote Sensing and Space Science, 24(1), 109–117. https://doi.org/10.1016/j.ejrs.2020.01.001
  • Aldababseh, A., Temimi, M., Maghelal, P., Branch, O., & Wulfmeyer, V. (2018). Multi-criteria evaluation of irrigated agriculture suitability to achieve food security in an arid environment. Sustainability (Switzerland), 10(3), 803. https://doi.org/10.3390/su10030803
  • Alfred, R., Obit, J. H., Chin, C. P. Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks. IEEE Access, 9 , 50358–50380. https://doi.org/10.1109/ACCESS.2021.3069449
  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration. guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Food and Agricultural Organization of the United Nations.
  • Allen, R. G., Tasumi, M., Morse, A., Trezza, R., Wright, J. L., Bastiaanssen, W., Kramber, W., Lorite, I., & Robison, C. W. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—applications. Journal of Irrigation and Drainage Engineering, 133(4), 395–406. https://doi.org/10.1061/(asce)0733-9437(2007)133:4(395)
  • Alvino, A., & Marino, S. (2017). Remote sensing for irrigation of horticultural crops. Horticulturae, 3(2), 40. https://doi.org/10.3390/horticulturae3020040
  • Andrade, M. A., O’Shaughnessy, S. A., & Evett, S. R. (2016). A GIS-based decision support tool for center pivot irrigation systems. 2016 American society of agricultural and biological engineers annual international meeting, ASABE 2016, December. https://doi.org/10.13031/aim.20162461449
  • Asfaw, E., Suryabhagavan, K. V., & Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 17(3), 250–258. https://doi.org/10.1016/j.jssas.2016.05.003
  • Barsi, Á., Kugler, Z., Juhász, A., Szabó, G., Batini, C., Abdulmuttalib, H., Huang, G., & Shen, H. (2019). Remote sensing data quality model: From data sources to lifecycle phases. International Journal of Image and Data Fusion, 10(4), 280–299. https://doi.org/10.1080/19479832.2019.1625977
  • Bashir, R. N., Bajwa, I. S., & Shahid, M. M. A. (2020). Internet of things and machine-learning-based leaching requirements estimation for saline soils. IEEE Internet of Things Journal, 7(5), 4464–4472. https://doi.org/10.1109/JIOT.2019.2954738
  • Bastiaanssen, W. G. M., Meneti, M., Feddes, R. A., & Holtslag, A. A. M. (1998, January). A remote sensing surface energy balance algorithm for land (SEBAL). Journal of Hydrology, 212-213(213), 198–212. https://doi.org/10.1016/S0022-1694(98)00253-4
  • Bastiaanssen, W. G. M., & Steduto, P. (2017). The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. Science of the Total Environment, 575 , 595–611. https://doi.org/10.1016/j.scitotenv.2016.09.032
  • Basukala, A. K., Oldenburg, C., Schellberg, J., Sultanov, M., & Dubovyk, O. (2017). Towards improved land use mapping of irrigated croplands: Performance assessment of different image classification algorithms and approaches. European Journal of Remote Sensing, 50(1), 187–201. https://doi.org/10.1080/22797254.2017.1308235
  • Bazzi, H., Baghdadi, N., Ienco, D., Hajj, M. E., Zribi, M., Belhouchette, H., Escorihuela, M. J., & Demarez, V. (2019). Mapping irrigated areas using sentinel-1 time series in Catalonia, Spain. Remote Sensing, 11(15), 1–25. https://doi.org/10.3390/rs11151836
  • Belaud, G., Mateos, L., Aliod, R., Buisson, M. C., Faci, E., Gendre, S., Ghinassi, G., Gonzales Perea, R., Lejars, C., Maruejols, F., & Zapata, N. (2020). Irrigation and energy: Issues and challenges. Irrigation and Drainage, 69(S1), 177–185. https://doi.org/10.1002/ird.2343
  • Bhat, S. A., & Huang, N. F. (2021). Big data and ai revolution in precision agriculture: Survey and challenges. IEEE Access, 9 , 110209–110222. https://doi.org/10.1109/ACCESS.2021.3102227
  • Bhunia, G. S., Keshavarzi, A., Shit, P. K., Omran, E.-S. E., & Bagherzadeh, A. (2018). Evaluation of groundwater quality and its suitability for drinking and irrigation using GIS and geostatistics techniques in semiarid region of Neyshabur, Iran. Applied Water Science, 8(6), 1–16. https://doi.org/10.1007/s13201-018-0795-6
  • Blatchford, M., Stokvis, B., Galindo, A., Blatchford, M., & Hoekstra, A. Y. (2018). Database methodology : Level 3 data. The Science of the Total Environment, 653 :241–252. Issue August 2019. https://doi.org/10.1016/j.scitotenv.2018.10.311.
  • Blatchford, M., Mannaerts, C. M., Zeng, Y., Nouri, H., & Karimi, P. Influence of spatial resolution on remote sensing-based irrigation performance assessment using WaPOR data. (2020). Remote Sensing, 12(18), 2949. https://doi.org/10.3390/RS12182949
  • Bwambale, E., Home, P., Raude, J., & Wanyama, J. (2019a). Development of a water allocation model for equitable water distribution at doho rice irrigation scheme, Uganda. Hydrology, 7(4), 62. https://doi.org/10.11648/j.hyd.20190704.12
  • Bwambale, E., Home, P., Raude, J., & Wanyama, J. (2019b). Hydraulic performance evaluation of the water conveyance system of doho rice irrigation scheme in Uganda. Journal of Sustainable Research in Engineering, 5(December), 101–112. http://sri.jkuat.ac.ke/ojs/index.php/sri
  • Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture : A review. Agricultural Water Management, 260(107324), 1–12. https://doi.org/10.1016/j.agwat.2021.107324
  • Bwambale, E., Abagale, F. K., & Anornu, G. K. (2023). Data-driven model predictive control for precision irrigation management. Smart Agricultural Technology, 3(May 2022), 100074. https://doi.org/10.1016/j.atech.2022.100074
  • Campbell, J., & Shin, M. E. (2011). Essentials of Geographical Information Systems, 259 (https://www.saylor.org/books/) ISSN:978-1-4533219-6-6 https://digitalcommons.liberty.edu/textbooks/2 .
  • Casa, R., Rossi, M., Sappa, G., & Trotta, A. (2009). Assessing crop water demand by remote sensing and GIS for the pontina plain, central Italy. Water Resources Management, 23(9), 1685–1712. https://doi.org/10.1007/s11269-008-9347-4
  • Chen, Y., Takara, K., Cluckie, I., & De Smedt, F. H. (2004). GIS and remote sensing in hydrology, water resources and environment. In IAHS Series of Proceedings and Reports (Vol. 289, Issue January 2004).
  • Chen, Z., Wang, J., Sun, J., Duan, A., Liu, Z., Song, N., & Liu, X. (2012). Water-saving irrigation management and decision support system based on WEBGIS. IFIP Advances in Information and Communication Technology, 368 AICT(Part 1), 301–312. https://doi.org/10.1007/978-3-642-27281-3_36
  • Cheng, S., Dou, M., Wang, J., Zhang, S., & Chen, X. (2015). Approach to constructing 3d virtual scene of irrigation area using multi-source data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2W2), 227–233. https://doi.org/10.5194/isprsannals-II-2-W2-227-2015
  • Chikabvumbwa, S. R., Sibale, D., & Chisale, S. W. (2021). Irrigation site selection using hybrid GIS-based approach. International Journal F Human Capital in Urban Management, 6(2), 149–158. https://doi.org/10.22034/IJHCUM.2021.02.04
  • Corwin, D. L., & Scudiero, E. (2019). Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. In Advances in agronomy (Vol. 158 , pp. 1–130). . https://doi.org/10.1016/bs.agron.2019.07.001
  • Debauche, O., El Moulat, M., Mahmoudi, S., Manneback, P., & Lebeau, F. (2018). Irrigation pivot-center connected at low cost for the reduction of crop water requirements. Proceedings - 2018 International conference on advanced communication technologies and networking, commnet 2018, 1–9. https://doi.org/10.1109/COMMNET.2018.8360259
  • Djuraev, A., Mirdjalalov, D., Nuratdinov, A., & Khushvaktov, T. (2021). Evaluation of soil salinity level through NDVI in Syrdarya province, Uzbekistan. E3S Web of Conferences, 03017.
  • El Baroudy, A. A., Ali, A. M., Mohamed, E. S., Moghanm, F. S., Shokr, M. S., Savin, I., Poddubsky, A., Ding, Z., Kheir, A. M. S., Aldosari, A. A., Elfadaly, A., Dokukin, P., & Lasaponara, R. (2020). Modeling land suitability for rice crop using remote sensing and soil quality indicators: The case study of the Nile delta. Sustainability (Switzerland), 12(22), 1–25. https://doi.org/10.3390/su12229653
  • Elnashar, A., Abbas, M., Sobhy, H., & Shahba, M. (2021). Crop water requirements and suitability assessment in arid environments: A new approach. Agronomy, 11(2), 260. https://doi.org/10.3390/agronomy11020260
  • ESRI. (2021). The general suitability modeling workflow—ArcGIS Pro | Documentation. https://pro.arcgis.com/en/pro-app/latest/help/analysis/spatial-analyst/suitability-modeler/the-general-suitability-modeling-workflow.htm
  • ESRI. (2022, February 26). What is GIS? | Geographic Information System Mapping Technology. https://www.esri.com/en-us/what-is-gis/overview
  • FAO. (2018a). The future of food and agriculture – Alternative pathways to 2050. http://www.fao.org/3/I8429EN/i8429en.pdf
  • FAO. (2018b). WaPOR Database methodology: Level 1 data. In Remote Sensing for Water Productivity Technical Report: Methodology Series.September). http://www.fao.org/fileadmin/user_upload/faoweb/RS-WP/pdf_files/Web_WaPOR-beta_Methodology_document_Level1.pdf
  • FAO. (2020). Irrigation Management. https://pdf4pro.com/download/irrigation-management-aims-at-increasing-food-23697e.html#google_vignette
  • Fares, A., Awal, R., Fares, S., Johnson, A. B., & Valenzuela, H. (2016). Irrigation water requirements for seed corn and coffee under potential climate change scenarios. Journal of Water and Climate Change, 7(1), 39–51. https://doi.org/10.2166/wcc.2015.025
  • Forcén-Muñoz, M., Pavón-Pulido, N., López-Riquelme, J. A., Temnani-Rajjaf, A., Berríos, P., Morais, R., & Pérez-Pastor, A. (2022). Irriman platform: Enhancing farming sustainability through cloud computing techniques for irrigation management. Sensors, 22(1), 228. https://doi.org/10.3390/s22010228
  • Fortes, P. S., Platonov, A. E., & Pereira, L. S. (2005). GISAREG - A GIS based irrigation scheduling simulation model to support improved water use. Agricultural Water Management, 77(1–3), 159–179. https://doi.org/10.1016/j.agwat.2004.09.042
  • Fue, K. G., & Sanga, C. (2015). Remote scheduling system for drip irrigation system using geographic information system. Journal of Geographic Information System, 7(5), 551–563. https://doi.org/10.4236/jgis.2015.75044
  • Garrido-Rubio, J., Calera Belmonte, A., Fraile Enguita, L., Arellano Alcázar, I., Belmonte Mancebo, M., Campos Rodríguez, I., & Bravo Rubio, R. (2018). Remote sensing-based soil water balance for irrigation water accounting at the Spanish Iberian Peninsula. Proceedings of the international association of hydrological sciences, 380, 29–35. https://doi.org/10.5194/piahs-380-29-2018
  • Garrido-Rubio J, Calera A, Arellano I, Belmonte M, Fraile L, Ortega T, Bravo R and González-Piqueras J. (2020). Evaluation of Remote Sensing-Based Irrigation Water Accounting at River Basin District Management Scale. Remote Sensing, 12(19), 3187 10.3390/rs12193187
  • Gebeyehu, M. N. (2019). Remote sensing and GIS application in agriculture and natural resource management. International Journal of Environmental Sciences & Natural Resources, 19(2), 556009. https://doi.org/10.19080/IJESNR.2019.19.556009
  • Giacomo, R., & David, G. (2018). Gerard , Sylvester. In E-agriculture in action: Drones for agriculture. FAO and ITU 978-92-5-130246-0s https://www.fao.org/3/I8494EN/i8494en.pdf .
  • Gorji, T., Tanik, A., & Sertel, E. (2015). Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science, 15 , 507–512. https://doi.org/10.1016/j.proeps.2015.08.062
  • Govind, A. (2021). Physics of remote sensing and sensor characteristics : A Primer. Remote Sensing Determination of Evapotranspiration.
  • Gumma, M. K., Thenkabail, P. S., Hideto, F., Nelson, A., Dheeravath, V., Busia, D., & Rala, A. (2011). Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sensing, 3(4), 816–835. https://doi.org/10.3390/rs3040816
  • Guo, F., & Wang, Y. (2012). Study of remote sensing and remote sensing robot technology. Applied Mechanics and Materials, 214 , 914–918. https://doi.org/10.4028/www.scientific.net/AMM.214.914
  • Huisman, O. (2009). . In H. Otto & de B. Rolf A (Eds.), Principles of geographical information systems (Vol. 1 ITC Educational Textbook Series). H. Otto & de B. Rolf A (Eds.). The International Institute for Geo-Information Science and Earth Observation (ITC).
  • Iradukunda, P., & Bwambale, E. (2021). Reservoir sedimentation and its effect on storage capacity–A case study of murera reservoir, Kenya. Cogent Engineering, 8(1), 0–14. https://doi.org/10.1080/23311916.2021.1917329
  • Jacob, A. (2019, June 26). What is 3D GIS and what are its uses? – Geospatial World. GeoSpatial World Blogs. https://www.geospatialworld.net/blogs/what-is-3d-gis-and-what-are-its-uses/
  • Jasmin, I., & Mallikarjuna, P. (2015). Evaluation of groundwater suitability for irrigation in the araniar river basin, south India — a case study using gis approach †. Irrigation and Drainage, 64(5), 600–608. https://doi.org/10.1002/ird.1930
  • Jin, N., Tao, B., Ren, W., Feng, M., Sun, R., He, L., Zhuang, W., & Yu, Q. (2016). Mapping irrigated and rainfed wheat areas using multi-temporal satellite data. Remote Sensing, 8(207), 1–19. https://doi.org/10.3390/rs8030207
  • Karthikeyan, L., Chawla, I., & Mishra, A. K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586(March), 124905. https://doi.org/10.1016/j.jhydrol.2020.124905
  • Kashyap, P. K., Kumar, S., Jaiswal, A., Prasad, M., & Gandomi, A. H. (2021). Towards precision agriculture : IoT-Enabled. IEEE Sensors Journal, 21(16), 17479–17491. https://doi.org/10.1109/JSEN.2021.3069266
  • Ketchum D, Jencso K, Maneta M P, Melton F, Jones M O and Huntington J. (2020). IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing, 12(14), 2328. https://doi.org/10.3390/rs12142328.
  • Kyagulanyi, J., Kabenge, I., Banadda, N., Muyonga, J., Mulamba, P., & Kiggundu, N. (2016). Estimation of spatial and temporal water requirements of grain amaranth using satellite, local and virtual weather stations datasets in Uganda. International Journal of Agricultural and Biological Engineering, 9(2), 85–97. https://doi.org/10.3965/j.ijabe.20160902.1676
  • Kywe, K., & Aye, N. (2019). Possible solutions to the challenges of irrigation water pricing for saedawgyi irrigated area. American Scientific Research Journal for Engineering, Technology, and Sciences ASRJETS, 52(1) , 176–188 https://core.ac.uk/display/235050773?utm_source=pdf&utm_medium=banner&utm_campaign=pdf-decoration-v1.
  • Laipelt, L., Henrique Bloedow Kayser, R., Santos Fleischmann, A., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., & Melton, F. (2021). Long-term monitoring of evapotranspiration using the SEBAL algorithm and google earth engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 178 , 81–96. https://doi.org/10.1016/j.isprsjprs.2021.05.018
  • Li, X., Zhang, J., Liu, J., Liu, J., Zhu, A., Lv, F., & Zhang, C. (2011). A modified checkbook irrigation method based on GIS-coupled model for regional irrigation scheduling. Irrigation Science, 29(2), 115–126. https://doi.org/10.1007/s00271-010-0221-9
  • Lin, Y. P., Mukhtar, H., Huang, K. T., Petway, J. R., Lin, C. M., Chou, C. F., & Liao, S. W. (2020). Real-time identification of irrigation water pollution sources and pathways with a wireless sensor network and blockchain framework. Sensors (Switzerland), 20(13), 1–22. https://doi.org/10.3390/s20133634
  • Liu, H. (2021). Agricultural water management based on the internet of things and data analysis. Acta Agriculturae Scandinavica Section B: Soil and Plant Science, 1–12. https://doi.org/10.1080/09064710.2021.1966496
  • Lozoya, C., Mendoza, C., Mejía, L., Quintana, J., Mendoza, G., Bustillos, M., Arras, O., & Solís, L. (2014). Model predictive control for closed-loop irrigation. IFAC Proceedings, 19(January), 4429–4434. https://doi.org/10.3182/20140824-6-za-1003.02067 Volumes (IFAC-PapersOnline)
  • Maina, M. M., Amin, M. S. M., & Yazid, M. A. (2014). Web geographic information system decision support system for irrigation water management: A review. Acta Agriculturae Scandinavica Section B: Soil and Plant Science, 64(4), 283–293. https://doi.org/10.1080/09064710.2014.896935
  • Mandal, B., Dolui, G., & Satpathy, S. (2018). Land suitability assessment for potential surface irrigation of river catchment for irrigation development in Kansai watershed, Purulia, West Bengal, India. Sustainable Water Resources Management, 4(4), 699–714. https://doi.org/10.1007/s40899-017-0155-y
  • McCarthy, A. C., Hancock, N. H., & Raine, S. R. (2014). Simulation of irrigation control strategies for cotton using Model Predictive Control within the VARIwise simulation framework. Computers and Electronics in Agriculture, 101 , 135–147. https://doi.org/10.1016/j.compag.2013.12.004
  • Megahed, H. A. (2020). GIS-based assessment of groundwater quality and suitability for drinking and irrigation purposes in the outlet and central parts of Wadi El-Assiuti, Assiut governorate, Egypt. Bulletin of the National Research Centre, 44(187), 1–31. https://doi.org/10.1186/s42269-020-00428-3
  • Mirauda, D., Erra, U., Agatiello, R., & Cerverizzo, M. (2017). Applications of mobile augmented reality to water resources management. Water (Switzerland), 9(9), 699. https://doi.org/10.3390/w9090699
  • Mohammed, H. R., Alkanaani, H. A., & Alowaid, H. K. (2021). Using GIS technology for land suitability analysis to select drainage project location : Nasiriya city south of Iraq as a case study using GIS technology for land suitability analysis to select drainage project location : Nasiriya city south of Iraq as a. Journal of Physics: Conference Series, 01( 20). https://doi.org/10.1088/1742-6596/1895/1/012001
  • Mohd, M. M., Soom, M., Amin, M., Kamal, R., & Wayayok, A. (2014). Application of web geospatial decision support system for tanjung karang rice precision irrigation water management. International Conference on Agriculture, Food and Environmental Engineering (ICAFEE’2014) Jan. 15-16, 2014, 24–28.
  • Mokhtari, A., Ahmadi, A., Daccache, A., & Drechsler, K. (2021). Actual evapotranspiration from UAV images: A multi-sensor data fusion approach. Remote Sensing, 13(12), 2315. https://doi.org/10.3390/rs13122315
  • Mostafa, G. (2019 Developing a Mobile GIS Application for Facilitating Information Communications in Agri-Environmental Programs. (The University of GuelphIn)Accessed 12 February 2022 https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17664). .
  • Naboureh, A., Li, A., Ebrahimy, H., Bian, J., Azadbakht, M., Amani, M., Lei, G., & Nan, X. (2021). Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal landsat imagery and a sample migration technique within google earth engine. International Journal of Applied Earth Observation and Geoinformation, 105 25 December 2021 , 102607. https://doi.org/10.1016/j.jag.2021.102607
  • Narany, T. S., Ramli, M. F., Fakharian, K., & Aris, A. Z. (2016). A GIS-index integration approach to groundwater suitability zoning for irrigation purposes. Arabian Journal of Geosciences, 9(7), 502. https://doi.org/10.1007/s12517-016-2520-9
  • Narziev, J., Nikam, B., & Gapparov, F. (2021). Infrastructure mapping and performance assessment of irrigation system using GIS and remote sensing. E3S Web of Conferences, 03005.
  • Ng Cheong, L. R., & Teeluck, M. (2018). Development of an irrigation scheduling software for sugarcane. Sugar Tech, 20(1), 36–39. https://doi.org/10.1007/s12355-017-0517-7
  • Nigussie, W., Hailu, B. T., & Azagegn, T. (2019). Mapping of groundwater potential zones using sentinel satellites (−1 SAR and −2A MSI) images and analytical hierarchy process in ketar watershed, main Ethiopian rift. Journal of African Earth Sciences 160 , 103632. https://doi.org/10.1016/j.jafrearsci.2019.103632
  • Nikam, B. R., Garg, V., Thakur, P. K., & Aggarwal, S. P. (2020). Application of remote sensing and GIS in performance evaluation of irrigation project at disaggregated level. Journal of the Indian Society of Remote Sensing, 48(7), 979–997. https://doi.org/10.1007/s12524-020-01128-1
  • Nina, A., Radovanović, M., & Popović, L. (2021). Extraterrestrial influences on remote sensing in the earth’s atmosphere. Remote Sensing, 13(5), 1–4. https://doi.org/10.3390/rs13050890
  • NOAA. (2021, February 26). What is remote sensing? https://oceanservice.noaa.gov/facts/remotesensing.html
  • Ortega-Farias, S., Esteban-Condori, W., Riveros-Burgos, C., Fuentes-Peñailillo, F., & Bardeen, M. (2021). Evaluation of a two-source patch model to estimate vineyard energy balance using high-resolution thermal images acquired by an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology 304-305 , January 108433. https://doi.org/10.1016/j.agrformet.2021.108433
  • Owusu, S., Mul, M. L., Ghansah, B., Kofi, P., Owusu, O., Awotwe, V., & Davie, P. (2017). Assessing land suitability for aquifer storage and recharge in northern Ghana using remote sensing and GIS multi-criteria decision analysis technique. Modeling Earth Systems and Environment, 3(4), 1383–1393. https://doi.org/10.1007/s40808-017-0360-6
  • Ozdogan, M., Yang, Y., Allez, G., & Cervantes, C. (2010). Remote sensing of irrigated agriculture: Opportunities and challenges. Remote Sensing, 2(9), 2274–2304. https://doi.org/10.3390/rs2092274
  • Patra, S., Mishra, P., & Mahapatra, S. C. (2018). Delineation of groundwater potential zone for sustainable development: A case study from ganga alluvial plain covering hooghly district of India using remote sensing, geographic information system and analytic hierarchy process. Journal of Cleaner Production, 172 , 2485–2502. https://doi.org/10.1016/j.jclepro.2017.11.161
  • Paul, M., Negahban-Azar, M., Shirmohammadi, A., & Montas, H. (2020). Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis. Agricultural Water Management, 231(April 2019):105987. https://doi.org/10.1016/j.agwat.2019.105987.
  • Peragón, J. M., Pérez-Latorre, F. J., Delgado, A., & Tóth, T. (2018). Best management irrigation practices assessed by a GIS-based decision tool for reducing salinization risks in olive orchards. Agricultural Water Management, 202(July 2017), 33–41. https://doi.org/10.1016/j.agwat.2018.02.010
  • Pereira, L. S., & Alves, I. (2013). Crop water requirements. In Reference module in earth systems and environmental sciences (issue may), 322-334. Elsevier Inc. ISBN: 978-0-12-409548-9. https://doi.org/10.1016/b978-0-12-409548-9.05129-0
  • Ramírez-Cuesta, J. M., Mirás-Avalos, J. M., Rubio-Asensio, J. S., & Intrigliolo, D. S. (2018). A novel ArcGIS toolbox for estimating crop water demands by integrating the dual crop coefficient approach with multi-satellite imagery. Water (Switzerland), 11(1), 38. https://doi.org/10.3390/w11010038
  • RAND. (2020, May 12). Unmanned Aerial Vehicles. https://www.rand.org/topics/unmanned-aerial-vehicles.html
  • Rehman, A., Saba, T., Kashif, M., Fati, S. M., Bahaj, S. A., & Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12(1), 127. https://doi.org/10.3390/agronomy12010127
  • Reyes-González, A., Kjaersgaard, J., Trooien, T., Hay, C., & Ahiablame, L. (2018). Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index. Advances in Meteorology, 2018(1), 1–12. https://doi.org/10.1155/2018/4525021
  • Rowshon M, Kwok C and Lee T. (2003). GIS-based scheduling and monitoring of irrigation delivery for rice irrigation system. Agricultural Water Management, 62(2), 105–116. https://doi.org/10.1016/S0378-3774(03)00092-1
  • Rowshon M K, Amin M S, Lee T S and Shariff A R. (2009). GIS-Integrated Rice Irrigation Management Information System for a River-Fed Scheme. Water Resour Manage, 23(14), 2841–2866. https://doi.org/10.1007/s11269-009-9412-7
  • Rowshon, M. K., Iqbal, M., Mojid, M. A., Amin, M. S. M., & Lai, S. H. (2018). Optimization of equitable irrigation water delivery for a large-scale rice irrigation scheme. International Journal of Agricultural and Biological Engineering, 11(5), 160–166. https://doi.org/10.25165/j.ijabe.20181105.3536
  • Roy, S. K., Misra, S., Raghuwanshi, N. S., & Das, S. K. (2021). AgriSens: IoT-Based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet of Things Journal, 8(6), 5023–5030. https://doi.org/10.1109/JIOT.2020.3036126
  • Rudra, R. P., Dickinson, W. T., & Sharma, D. N. (1993). Application of GIS in watershed management. New Techniques for Modelling of Stormwater Quality Impacts, January 2016 , 469–482. https://doi.org/10.14796/jwmm.r175-21
  • Sankar, G., Ali, B., Pravat, K., Shit, K., Sayed, E., Omran, E., & Bagherzadeh, A. (2018). Evaluation of groundwater quality and its suitability for drinking and irrigation using GIS and geostatistics techniques in semiarid region of Neyshabur, Iran. Applied Water Science, 8(6), 1–16. https://doi.org/10.1007/s13201-018-0795-6
  • Santos, C., Lorite, I. J., Tasumi, M., Allen, R. G., & Fereres, E. (2008). Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level. Irrigation Science, 26(3), 277–288. https://doi.org/10.1007/s00271-007-0093-9
  • Santos B, Gonçalves J, Martins A M, Pérez-Cano M T, Mosquera-Adell E, Dimelli D, Lagarias A and Almeida P G. (2021). GIS in Architectural Teaching and Research: Planning and Heritage. Education Sciences, 11(6), 307 10.3390/educsci11060307
  • Saroj, A., Ashish, P., & C, U. C. (2014). Use of geographical information systems in irrigation management: A review. Journal of Indian Water Resources Society, 34(2), 32–39. https://docplayer.net/14420348-Use-of-geographic-information-systems-in-irrigation-management-a-review.html.
  • Sawadogo, A., Kouadio, L., Traoré, F., Zwart, S. J., Hessels, T., & Gündoğdu, K. S. (2020). Spatiotemporal assessment of irrigation performance of the kou valley irrigation scheme in Burkina Faso using satellite remote sensing-derived indicators. ISPRS International Journal of Geo-Information, 9(8), 484. https://doi.org/10.3390/ijgi9080484
  • Sebnie, W., Adgo, E., & Kendie, H. (2020). Irrigation suitability evaluation of zamra irrigation scheme, abergelle district of wag ‑ himra zone, Amhara region Ethiopia. Environmental Systems Research, 9(1), 1–9. https://doi.org/10.1186/s40068-020-00200-6
  • Seif-Ennasr, M., Bouchaou, L., El Morjani, Z. E. A., Hirich, A., Beraaouz, E. H., & Choukr-Allah, R. (2020). GIS-based land suitability and crop vulnerability assessment under climate change in chtouka Ait Baha, Morocco. Atmosphere, 11(11), 1167. https://doi.org/10.3390/atmos11111167
  • Seyedmohammadi, J., Sarmadian, F., Asghar, A., & Mcdowell, R. W. (2019). Geoderma development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture. Geoderma, 352(Feb 2018): 80–95. https://doi.org/10.1016/j.geoderma.2019.05.046
  • Singh, A. K., Dubey, O. P., & Ghosh, S. K. (2016). Irrigation scheduling using intervention of geomatics tools—A case study of Khedli minor. Agricultural Water Management, 177 , 454–460. https://doi.org/10.1016/j.agwat.2016.05.024
  • Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 1–31. https://doi.org/10.3390/rs12193136
  • Sultan, D. (2013). Assessment of irrigation land suitability and development of map for the fogera catchment using gis, south gonder. Asian Journal of Agriculture and Rural Development, 3(1), 7–17. https://doi.org/10.22004/ag.econ.198027
  • Suzuki, S., & Takemi, M. (2012). Remote sensing for medical and health care applications. In ItechOpen. Ed., Remote Sensing Applications, (vol. 32 (ItechOpen), pp. 137–144). http://www.intechopen.com/books/trends-in-telecommunications-technologies/gps-total-electron-content-tec-prediction-at-ionosphere-layer-over-the-equatorial-region%0AInTec
  • Taghvaeian, S., Neale, C. M. U., Osterberg, J. C., Sritharan, S. I., & Watts, D. R. (2018). Remote sensing and GIS techniques for assessing irrigation performance: Case study in southern California. Journal of Irrigation and Drainage Engineering, 144(6), 05018002. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001306
  • Tantawy, I. (2019). The application of the FAO WaPOR data portal to monitor efficient water use in agriculture: A case study on the eastern Nile river basin. TU DELFT.
  • Tempfli, K., Kerle, N., Huurneman, G. C., Janssen, L. L. F., Bakker, W. H., Feringa, W., Gieske, A. S. M., Gorte, B. G. H., Grabmaier, K. A., Hecker, C. A., Horn, J. A., Huurneman, G. C., Janssen, L. L. F., Kerle, N., Meer, F. D., Van Der, P., Pohl, G. N., Reeves, C., Ruitenbeek, C. V., … Woldai, T. (2009). Principles of remote sensing : An introductory textbook. The International Institute for Geo-Information Science and Earth Observation (ITC).
  • Tessema Roba, N., Kebede Kassa, A., & Yadeta Geleta, D. (2021). Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda. Water Practice and Technology, 1–22. https://doi.org/10.2166/wpt.2021.033
  • Thapliyal, A., Panwar, A., Space, U., Centre, A., Sum, W., & Classification, Q. (2019). Agriculture potential zone identification using gis technique in the hilly terrain block of uttarkashi district. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII(February), 18–20. https://doi.org/10.5194/isprs-archives-XLII-3-W6-439-2019
  • Ungureanu, N., Vlăduț, V., & Voicu, G. (2020). Water scarcity and wastewater reuse in crop irrigation. Sustainability (Switzerland), 12(21), 1–19. https://doi.org/10.3390/su12219055
  • USC. (2021, February 23). The Future of GIS - USC GIS Online. //Gis.Usc.Edu/Blog. https://gis.usc.edu/blog/the-future-of-gis/
  • Valencia J, Monserrate F, Casteleyn S, Bax V, Francesconi W and Quintero M. (2020). A GIS-based methodological framework to identify superficial water sources and their corresponding conduction paths for gravity-driven irrigation systems in developing countries. Agricultural Water Management, 232, 106048. https://doi.org/10.1016/j.agwat.2020.106048
  • Villa, R Orlando, P. (2011). Remote sensing applications in archaeology. Archeologia E Calcolatori, 22, 147–168 https://core.ac.uk/display/33151603?utm_source=pdf&utm_medium=banner&utm_campaign=pdf-decoration-v1.
  • Vogels, M. F. A., de Jong, S. M., Sterk, G., & Addink, E. A. (2019). Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis – A case study in the central rift valley, Ethiopia –. International Journal of Applied Earth Observation and Geoinformation, 75(May 2018):118–129. https://doi.org/10.1016/j.jag.2018.07.019.
  • Xiao Y and Watson M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research, 39(1), 93–112. 10.1177/0739456X17723971
  • Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google earth engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126 , 225–244. https://doi.org/10.1016/j.isprsjprs.2017.01.019
  • Yang, B., Chen, D., Ren, B., & Wu, Y. (2016). Application of GIS and 3D visualization simulation in irrigation management system-an effective solution of visualization and dynamic for water resourses management International Conference on Applied Mathematics, Simulation and Modelling. , 219–222. https://doi.org/10.2991/amsm-16.2016.50
  • Younes, M., & Salman, A. (2021). A cloud-based application for smart irrigation management system. 2021 8th International Conference on Electrical and Electronics Engineering, ICEEE 2021, 85–92. https://doi.org/10.1109/ICEEE52452.2021.9415913
  • Zafar, A., Prathapar, S., & Bastiaanssen, W. (2021). Optimization of canal management based on irrigation performance analysis using satellite measurements. (165). http://dx.doi.org/10.22617/BRF210022-2
  • Zeyliger, A. M., Muzalevskiy, K. V., Zinchenko, E. V., & Ermolaeva, O. S. (2022). Field test of the surface soil moisture mapping using Sentinel-1 radar data. Science of the Total Environment, 807(2) , 151121. https://doi.org/10.1016/j.scitotenv.2021.151121
  • Ziadat, F. M., & Sultan, K. A. (2011). Combining current land use and farmers ’ knowledge to design land-use requirements and improve land suitability evaluation. Renewable Agriculture and Food Systems, 26(4), 287–296. https://doi.org/10.1017/S1742170511000093

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