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Bias correction of satellite based crop water stress index using machine learning methods

Author

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  • Zoratipour, Elahe
  • Veysi, Shadman
  • Mohammadi, Amir Soltani
  • Nasab, Saeed Boroomand
  • Naseri, Abd Ali

Abstract

Accurate estimation of the Crop Water Stress Index (CWSI) is essential for supporting irrigation scheduling in water-limited regions. Traditionally, CWSI has been computed from field-based canopy temperature and meteorological measurements; however, these approaches are often limited by high costs and sparse spatial coverage. Satellite remote sensing offers a practical alternative by providing large-scale and repeated observations of crop water stress. Nevertheless, uncertainties in satellite-derived inputs, particularly land surface temperature (LST), can introduce significant biases in CWSI computations. This study addresses the challenge of mitigating biases in the CWSI computations, utilizing satellite data within sugarcane fields in southwest Iran. For this goal, twenty-four Landsat 8/9 satellite images were acquired (June–September 2023). Concurrent with the satellite overpass times, data from 18 points across the sugarcane fields, were gathered to compute CWSI based on Idso method as a benchmark for bias correction. Field measurement includes, canopy temperature (Tc) and meteorological variables (i.e. Tmin, Tmax, RHmin, and RHmax) from sensor-equipped points. LST was calculated using the single-channel algorithm (SC) in google earth engine (GEE) platform. Three distinct machine learning (ML) methods, namely Random Forest (RF), Support Vector Machine (SVM), and extreme gradient boosting (XGBoost), were employed to mitigate bias in LST as main driver of CWSI based on satellite data. The results demonstrated that the RF model performed exceptionally well for LST bias correction. Overall, CWSI enhance accuracy, about with R2 = 35 %, nRMSE= 47 % and rMBE= 50 %. This research highlights the effectiveness of ML methods in improving CWSI estimates based on satellite imagery and taking a big step towards calculating CWSI for optimizing irrigation management in arid regions, supporting sustainable water use and food security with minimal ground monitoring.

Suggested Citation

  • Zoratipour, Elahe & Veysi, Shadman & Mohammadi, Amir Soltani & Nasab, Saeed Boroomand & Naseri, Abd Ali, 2025. "Bias correction of satellite based crop water stress index using machine learning methods," Agricultural Water Management, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:agiwat:v:320:y:2025:i:c:s0378377425005761
    DOI: 10.1016/j.agwat.2025.109862
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    1. Saboori, Mojtaba & Mokhtari, Ali & Afrasiabian, Yasamin & Daccache, Andre & Alaghmand, Sina & Mousivand, Yousef, 2021. "Automatically selecting hot and cold pixels for satellite actual evapotranspiration estimation under different topographic and climatic conditions," Agricultural Water Management, Elsevier, vol. 248(C).
    2. Er-Raki, S. & Chehbouni, A. & Boulet, G. & Williams, D.G., 2010. "Using the dual approach of FAO-56 for partitioning ET into soil and plant components for olive orchards in a semi-arid region," Agricultural Water Management, Elsevier, vol. 97(11), pages 1769-1778, November.
    3. Nouri, Milad & Veysi, Shadman, 2024. "CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms," Agricultural Water Management, Elsevier, vol. 306(C).
    4. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    5. Solgi, Shahin & Ahmadi, Seyed Hamid & Seidel, Sabine Julia, 2023. "Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields," Agricultural Water Management, Elsevier, vol. 280(C).
    6. Paredes, Paula & Pereira, Luis S. & Rodrigues, Gonçalo C. & Botelho, Nuno & Torres, Maria Odete, 2017. "Using the FAO dual crop coefficient approach to model water use and productivity of processing pea (Pisum sativum L.) as influenced by irrigation strategies," Agricultural Water Management, Elsevier, vol. 189(C), pages 5-18.
    7. Gontia, N.K. & Tiwari, K.N., 2008. "Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry," Agricultural Water Management, Elsevier, vol. 95(10), pages 1144-1152, October.
    8. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.
    9. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).
    10. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    11. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    12. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    13. Kumari, Arti & Singh, D.K. & Sarangi, A. & Hasan, Murtaza & Sehgal, Vinay Kumar, 2024. "Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling," Agricultural Water Management, Elsevier, vol. 305(C).
    14. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    15. Khorsand, Afshin & Rezaverdinejad, Vahid & Asgarzadeh, Hossein & Majnooni-Heris, Abolfazl & Rahimi, Amir & Besharat, Sina, 2019. "Irrigation scheduling of maize based on plant and soil indices with surface drip irrigation subjected to different irrigation regimes," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    16. Prashant Srivastava & Tanvir Islam & Manika Gupta & George Petropoulos & Qiang Dai, 2015. "WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2267-2284, May.
    17. Das, Susanta & Kaur, Samanpreet & Sharma, Vivek, 2024. "Determination of threshold crop water stress index for sub-surface drip irrigated maize-wheat cropping sequence in semi-arid region of Punjab," Agricultural Water Management, Elsevier, vol. 301(C).
    18. Zhu, Shidan & Cui, Ningbo & Jin, Huaan & Jin, Xiuliang & Guo, Li & Jiang, Shouzheng & Wu, Zongjun & Lv, Min & Chen, Fei & Liu, Quanshan & Wang, Mingjun, 2024. "Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors," Agricultural Water Management, Elsevier, vol. 294(C).
    19. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
    20. Corbari, Chiara & Salerno, Raffaele & Ceppi, Alessandro & Telesca, Vito & Mancini, Marco, 2019. "Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling," Agricultural Water Management, Elsevier, vol. 212(C), pages 283-294.
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