Evaluating Remote Sensing Technique and Machine Learning Algorithms in Estimating Sugarcane Evapotranspiration

Document Type : Research Paper


1 Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), 490 Couronne St, Quebec, QC G1K 9A9, Canada.



Estimating crop evapotranspiration (ETc) in arid and semi-arid areas can be difficult due to the dynamic nature of this process across both time and space. In addition, obtaining on-site measurements for this variable can be very time-consuming and costly. This study aimed to develop a framework that accurately estimates the sugarcane crop evapotranspiration on a spatio-temporal scale. This was achieved using four machine learning (ML) algorithms (MLR, CART, SVR, and GBRT) combined with remote sensing (RS) data and meteorological variables. Also, to reduce the dependence on several meteorological parameters in conventional ETc equations, the performance of eight different experimental temperature-based methods and four modified Hargreaves & Samani equations was evaluated compared to the standard FAO-Penman-Monteith method. For this purpose, weather data were collected from Hakim Farabi Sugarcane Agro-Industrial meteorological station for three years (2018-2021). Nine combinations of input variables (RS data and meteorological variables) were designed based on the IGR method and then evaluated by the ML algorithms. The results showed that the highest accuracy of ML algorithms based on R2, RMSE, and MAE statistics was obtained in CART (0.99, 0.41, and 0.18) and GBRT algorithms (0.99, 0.65, and 0.26), respectively. Regarding temperature-based methods, Ivanov’s equation had the best performance with an R2 of 0.91, while Baier and Robertson’s equation had the weakest performance with an R2 of 0.78 when estimating ETc. Overall, the combination of RS and ML algorithms effectively produced more precise and reliable ETc values on both temporal and spatial scales.


Main Subjects

  1. Ahmadi, S. H., & Javanbakht, Z. (2020). Assessing the physical and empirical reference evapotranspiration (ETo) models and time series analyses of the influencing weather variables on ETo in a semi-arid area. Journal of Environmental Management, 276, 111278.‏
  2. Akhavan, S., Kanani, E., & Dehghanisanij, H. (2019). Assessment of different reference evapotranspiration models to estimate the actual evapotranspiration of corn (Zea mays ) in a semiarid region (case study, Karaj, Iran). Theoretical and Applied Climatology, 137, 1403-1419.
  3. Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy, 300 pp.
  4. Baier, W., & Robertson, G. W. (1965). Estimation of latent evaporation from simple weather observations. Canadian Journal of Plant Science, 45, 276-284.
  5. Berti, A., Tardivo, G., Chiaudani, A., Rech, F., & Borin, M. (2014). Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy. Agricultural Water Management. 140, 20–25.
  6. Blaney, H.F., & Criddle, W.D. (1950). Determining water requirements in irrigated areas from climatological and irrigation data. Soil conservation service technical paper 96; Soil conservation service. US Department of Agriculture, Washington.
  7. Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. In Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA.
  8. Braga, P., Crusiol, L. G. T., Nanni, M. R., Caranhato, A. L. H., Fuhrmann, M. B., Nepomuceno, A. L., Neumaier, N., Farias, J.R.B., Koltun, A., Goncalves, L.S.A., & Mertz Henning, L. M. (2021). Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean. Precision Agriculture, 22, 249-266.‏
  9. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks. In: Cole Advanced Books and Software.
  10. Didari, S., & Ahmadi, S.H. (2019). Calibration and evaluation of the FAO56-PenmanMonteith, FAO24-radiation, and Priestly-Taylor reference evapotranspiration models using the spatially measured solar radiation across a large arid and semi-arid area in southern Iran. Theoretical and Applied Climatology, 136 (1-2), 441-455.
  11. Droogers, P., & Allen, R.G. (2002). Estimating reference evapotranspiration under inaccurate data conditions. Irrigation and Drainage Systems, 16, 33-45.
  12. Eshaghi, A., Motamedvaziri, B., & Feiznia, S. (2010). Landslides Hazard Zonation Using Logistic Regression Method (Case Study: Safaroud Watershed). Territory, 24(6), 67-77.
  13. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 29(5), 1189-1232.
  14. Gao, B.C. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environment, 58, 257-266.
  15. Gates, D.M., Keegan, H. J., Schleter, J. C., & Weidner, V. R. (1965). Spectral properties of plants. Applied optics. 4(1), 11 20
  16. Ge, J., Zhao, L., Yu, Z., Liu, H., Zhang, L., Gong, X., & Sun, H. (2022). Prediction of greenhouse tomato crop evapotranspiration using XGBoost machine learning model. Plants, 11(15), 1923.
  17. Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms-A comparative study. Agricultural Water Management, 217, 303-315.
  18. Hargreaves, G.H., & Samani, Z.A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1, 96-99.
  19. Huete, A., Justice, C., & Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing. Environment, 49, 224-234.
  20. Huete, A.R. (2012). Vegetation Indices, Remote Sensing and Forest Monitoring. Geography Compass, 6, 513-532.
  21. Ivanov, N. N. (1954). About potential evapotranspiration estimation. Izv VGO, 86, 189-196.
  22. Jensen, M.E. (1968). In: In: Kozlowski, T.T. (Ed.), Water Consumption by Agricultural Plants. Water Deficits and Plant Growth, vol. 2. Academic Press, New York, pp. 1-22.
  23. Kharrufa, N. (1985). Simplified equation for evapotranspiration in arid regions. Beiträge Hydrol, 5, 39-47.
  24. Liu, Y., Yue, Q., Wang, Q., Yu, J., Zheng, Y., Yao, X., & Xu, S. (2021). A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products. Remote Sensing, 13(18), 3643.
  25. Mosre, J., & Suárez, F. (2021). Actual evapotranspiration estimates in arid cold regions using machine learning algorithms with in situ and remote sensing data. Water, 13(6), 870.
  26. Nouraki, A., Akhavan, S., Rezaei, Y., & Fuentes, S. (2021). Assessment of sunflower water stress using infrared thermometry and computer vision analysis. Water Supply, 21(3), 1228-1242.‏
  27. Nouraki, A., Golabi, M., Albaji, M., Naseri, A., & Homayouni, S. (2023). Spatial-temporal modeling of soil moisture using optical and thermal remote sensing data and machine learning algorithms. Iranian Journal of Soil and Water Research, 54(4), 637-653.‏ (In Persian).
  28. Ravazzani, G., Corbari, C., Morella, S., Gianoli, P., & Mancini, M. (2012). Modified Hargreaves-Samani equation for the assessment of reference evapotranspiration in Alpine River Basins. Journal of Irrigation and Drainage Engineering, ASCE 138 (7), 592-599.
  29. Rodrigues, G. C., & Braga, R. P. (2021). Estimation of reference evapotranspiration during the irrigation season using nine temperature-based methods in a hot-summer Mediterranean climate. Agriculture, 11(2), 124.‏
  30. Schendel, U. (1967).Vegetationswasserverbrauch und-wasserbedarf. Habilitation; Kiel, p 137.
  31. Schneider, P., Roberts, D.A., & Kyriakidis, P.C. (2008). A VARI-based relative greenness from MODIS data for computing the Fire Potential Index. Remote Sensing of Environment, 112, 1151-1167.
  32. Shao, G., Han, W., Zhang, H., Liu, S., Wang, Y., Zhang, L., & Cui, X. (2021). Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices. Agricultural Water Management, 252, 106906.‏
  33. Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical review, 38, 55-94.
  34. Trajkovic, S. (2007). Hargreaves versus penman–Monteith under humid conditions. Journal of Irrigation and Drainage Engineering, ASCE 133(1), 38-42.
  35. Yamaç, S. S., & Todorovic, M. (2020). Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management, 228, 105875.‏
  36. Yang, W., Kobayashi, H., Wang, C., Shen, M., Chen, J., Matsushita, B., Tang, Y., Kim, Y., Bret-Harte, M.S., Zona, D.; Oechel, W., & Kondoh, A. (2019). A Semi-Analytical Snow-Free Vegetation Index for Improving Estimation of Plant Phenology in Tundra and Grassland Ecosystems. Remote Sensing of Environment, 228, 31–44.
  37. Yebra, M., Van Dijk, A., Leuning, R., Huete, A., & Guerschman, J.P. (2013). Evaluation of Optical Remote Sensing to Estimate Actual Evapotranspiration and Canopy Conductance. Remote Sensing of Environment, 129, 250-261.