Multispectral Indices for Crop Water Stress Assessment and Precision Irrigation in Arid Agriculture: A Case Study from Béchar, Algeria
DOI:
https://doi.org/10.22399/ijcesen.3414Keywords:
Precision irrigation, Remote sensing, Water stress, Multispectral imagery, Arid agricultureAbstract
Water scarcity combined with increasing food demand poses significant challenges to agricultural sustainability, particularly in arid regions such as Béchar province in Algeria, where irrigation accounts for approximately 70% of total water consumption. This study presents an innovative satellite-based precision irrigation system leveraging multispectral imagery from Landsat 8 and Sentinel-2 satellites to optimize water use efficiency while maintaining crop productivity. The proposed approach integrates multiple biophysical indices, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and Soil Moisture Index (SMI), to accurately assess crop water stress and irrigation requirements in near real-time. Validation was conducted over four agricultural seasons within the Ouakda zone, encompassing 17 crop types such as lettuce, beetroot, and turnip, over an area of 5.33 km². Results indicate a strong correlation between NDVI values and water stress levels: NDVI below 0.33 signals critical irrigation needs, whereas values above 0.66 correspond to optimal vegetation health. Land cover classification revealed a vegetation coverage of 36.3%, while spectral indices effectively tracked seasonal variations in crop vigor. Winter crops demonstrated enhanced growth under regulated irrigation regimes, whereas summer crops exhibited pronounced water stress. This system delivers actionable irrigation recommendations that could reduce water consumption by up to 30% without compromising yields. By combining remote sensing data with multispectral analysis, this research offers a scalable, adaptable framework for precision irrigation in arid environments, fostering sustainable water management and agricultural resilience. The methodology holds potential for global application in similar water-scarce agroecosystems.
References
[1] Li, Y., & Liu, X. (2024). Surface water extraction from remote sensing images of arid regions in Africa using a deep learning approach combining multiscale information. Proceedings of SPIE, 12565, 125650F. https://doi.org/10.1117/12.3045960. DOI: https://doi.org/10.1117/12.3045960
[2] Call, A., Akiba, S., & Pringle, E. G. (2024). Remote sensing reveals the importance of seminatural habitat and irrigation on aphid biocontrol in arid agroecosystems. Ecological Solutions and Evidence, 5(2), e12377. https://doi.org/10.1002/2688-8319.12377. DOI: https://doi.org/10.1002/2688-8319.12377
[3] Zhang, M., Zhang, H., Deng, W., & Yuan, Q. (2024). Assessment of habitat quality in arid regions incorporating remote sensing data and field experiments. Remote Sensing, 16(19), 3648. https://doi.org/10.3390/rs16193648. DOI: https://doi.org/10.3390/rs16193648
[4] Md-Tahir, H., Mahmood, H. S., Husain, M., Khalil, A. A., Shoaib, M., Ali, M., Ali, M. M., Tasawar, M., Khan, Y., Awan, U. K., & Cheema, M. J. M. (2024). Localized crop classification by NDVI time series analysis of remote sensing satellite data: Applications for mechanization strategy and integrated resource management. AgriEngineering, 6(3), 2429–2444. https://doi.org/10.3390/agriengineering6030142. DOI: https://doi.org/10.3390/agriengineering6030142
[5] Parra-López, C., Abdallah, S. B., Garcia-Garcia, G., Hassoun, A., Trollman, H., Jagtap, S., & others. (2025). Digital technologies for water use and management in agriculture: Recent applications and future outlook [Manuscript submitted for publication]. University of Leicester. https://hdl.handle.net/2381/28333481.v1. DOI: https://doi.org/10.1016/j.agwat.2025.109347
[6] Ambaru, B., Manvitha, R., & Madas, R. (2025). Synergistic integration of remote sensing and soil metagenomics data: advancing precision agriculture through interdisciplinary approaches. Frontiers in Sustainable Food Systems, 8. https://doi.org/10.3389/fsufs.2024.1499973. DOI: https://doi.org/10.3389/fsufs.2024.1499973
[7] Guo, L., Chao, X., Wu, H., Shi, M., & Fan, Y. (2024). The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China. Land, 13(12), 2162. https://doi.org/10.3390/land13122162. DOI: https://doi.org/10.3390/land13122162
[8] Handoko, E. Y., Fahriza, A., & Muryono, M. (2024). Integrating Remote Sensing and GIS for Precision Agriculture: Leveraging Google Earth Engine for Enhanced Agricultural Management. 1418, 012054. https://doi.org/10.1088/1755-1315/1418/1/012054. DOI: https://doi.org/10.1088/1755-1315/1418/1/012054
[9] Ouédraogo, S., Kabre, S., Somda, W., Kébré, M. B., & Zougmoré, F. (2024). Sustainable Irrigation and Fertilization Strategies for Maize in Arid Regions: Insights from Numerical Modeling and Simulation. 1–9. https://doi.org/10.1109/mne3sd63831.2024.10812157. DOI: https://doi.org/10.1109/MNE3SD63831.2024.10812157
[10] Frontiers in Plant Science. (2025). Sustainable and Intelligent Phytoprotection. *Frontiers in Plant Science, 16*, Article 1587869. https://doi.org/10.3389/fpls.2025.1587869. DOI: https://doi.org/10.3389/fpls.2025.1587869
[11] Meshram, P. G., Shaniware, Y., Bhondave, G. P., Zol, D. M., & Pandey, P. (2024). Precision Agriculture: UAV-Based Soil Mapping and Remote Sensing Applications. Asian Research Journal of Agriculture. https://doi.org/10.9734/arja/2024/v17i4598 DOI: https://doi.org/10.9734/arja/2024/v17i4598
[12] Zhou, M., Wang, X., Liu, W., & Li, J. (2023). Soil salinity mapping using Sentinel-2 data and machine learning in an arid irrigation district of China. Remote Sensing, 15(1), 187. https://doi.org/10.3390/rs15010187 DOI: https://doi.org/10.3390/rs15010187
[13] Jancevičius, J. (2025). Enhancing land use classification with hybrid machine learning and satellite imagery. New Trends in Computer Sciences, 3(1), 1–17. https://doi.org/10.3846/ntcs.2025.23701 DOI: https://doi.org/10.3846/ntcs.2025.23701
[14] Karnieli, A., Agam, N., Pinker, R., Anderson, M., Imhoff, M., Gutman, G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618–633. https://doi.org/10.1175/2009JCLI2900.1 DOI: https://doi.org/10.1175/2009JCLI2900.1
[15] Folhes, M., Rennó, C. D., & Soares, J. V. (2009). Remote sensing for irrigation water management in the semi-arid Northeast of Brazil. Agricultural Water Management, 96(10), 1398–1408. https://doi.org/10.1016/j.agwat.2009.04.021 DOI: https://doi.org/10.1016/j.agwat.2009.04.021
[16] Saadi, S., Simonneaux, V., Boulet, G., Raimbault, B., Mougenot, B., Fanise, P., Ayari, H., & Lili-Chabaane, Z. (2015). Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia). Remote Sensing, 7(10), 13005-13028. https://doi.org/10.3390/rs71013005. DOI: https://doi.org/10.3390/rs71013005
[17] Niazmardi, S., Homayouni, S., Safari, A., McNairn, H., Shang, J., & Beckett, K. (2018). Histogram-based spatio-temporal feature classification of vegetation indices time-series for crop mapping. International Journal of Applied Earth Observation and Geoinformation, 72, 34–41. https://doi.org/10.1016/j.jag.2018.05.014 DOI: https://doi.org/10.1016/j.jag.2018.05.014
[18] Albertini, C., Gioia, A., Iacobellis, V., Petropoulos, G. P., & Manfreda, S. (2024). Assessing multi-source random forest classification and robustness of predictor variables in flooded areas mapping. Remote Sensing Applications: Society and Environment, 35, Article 101239. https://doi.org/10.1016/j.rsase.2024.101239 DOI: https://doi.org/10.1016/j.rsase.2024.101239
[19] Belayhun, M., Chere, Z., Abay, N. G., Nicola, Y., & Asmamaw, A. (2024). Spatiotemporal pattern of water hyacinth (Pontederia crassipes) distribution in Lake Tana, Ethiopia, using a random forest machine learning model. Frontiers in Environmental Science, 12, Article 1476014. https://doi.org/10.3389/fenvs.2024.1476014 DOI: https://doi.org/10.3389/fenvs.2024.1476014
[20] Kamenova, I., Chanev, M., Dimitrov, P., Filchev, L., Bonchev, B., Zhu, L., & Dong, Q. (2024). Crop type mapping and winter wheat yield prediction utilizing Sentinel-2: A case study from Upper Thracian Lowland, Bulgaria. Remote Sensing, 16(7), Article 1144. https://doi.org/10.3390/rs16071144 DOI: https://doi.org/10.3390/rs16071144
[21] Farhadi, H., Ebadi, H., Kiani, A., & Asgary, A. (2024). Near real-time flood monitoring using multi-sensor optical imagery and machine learning by GEE: An automatic feature-based multi-class classification approach. Remote Sensing, 16(23), Article 4454. https://doi.org/10.3390/rs16234454. DOI: https://doi.org/10.3390/rs16234454
[22] Purnamasari, D., Teuling, A. J., & Weerts, A. H. (2025). Identifying irrigated areas using land surface temperature and hydrological modelling: Application to the Rhine basin. Hydrology and Earth System Sciences, 29, 1483–1503. https://doi.org/10.5194/hess-29-1483-2025. DOI: https://doi.org/10.5194/hess-29-1483-2025
[23] Yu, L., Xie, H., Xu, Y., Li, Q., Jiang, Y., Tao, H., & Aihemaiti, M. (2024). Identification and monitoring of irrigated areas in arid areas based on Sentinel-2 time-series data and a machine learning algorithm. Agriculture, 14(10), 1–23. https://doi.org/10.3390/agriculture14102023 DOI: https://doi.org/10.3390/agriculture14101693
[24] Cao, C., Zhu, X., Liu, K., Liang, Y., & Ma, X. (2025). Satellite-Observed Arid Vegetation Greening and Terrestrial Water Storage Decline in the Hexi Corridor, Northwest China. Remote Sensing, 17(8), 1361. https://doi.org/10.3390/rs17081361. DOI: https://doi.org/10.3390/rs17081361
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
