Wheat yield prediction based on Sentinel-2, regression, and machine learning models in Hamedan, Iran

Document Type : Article


1 Remote Sensing and GIS Research Center, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

2 Faculty of Earth Sciences, Arak University of Technology, Arak, Iran

3 Department of Applied Remote Sensing, Iranian Space Research Center, Tehran, Iran


An accurate forecast of wheat yield prior to harvest is of great importance to ensure the sustainability of food production in Iran. The primary objective of this study is to determine the best remote sensing features and regression model for wheat yield prediction in Hamedan, Iran. In addition, the effects of various time windows on different regression models are verified. For this purpose, several vegetation indices (VIs) and reflectance values obtained from Sentinel-2, as input to regression models, are used in different time windows. As a result, Gaussian process regression (GPR) and random forest (RF) represented the top two best methods, and the best results were achieved for the GPR model with the SAVI, NDVI, EVI2, WDRVI, SR, GNDVI and GCVI indices corresponding to the image captured at the end of May. The best model yielded a root mean square error (RMSE) of 0.228 t/ha and coefficient of determination R^2 = 0.73. Moreover, different regression methods regarding the number of training data are compared. The neural network and linear regression were the most and stepwise regression was the model affected the least by the number of training samples. Experimental results provide a technical reference for estimating large scale wheat yield.


1. Becker-Reshef, I., Vermote, E., Lindeman, M., et al. "A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data", Remote Sensing of Environment, 114(6), pp. 1312-1323 (2010).
2. Liaqat, M.U., Cheema, M.J.M., Huang, W., et al. "Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin", Comput. Electron. Agric., 138, pp. 39- 47 (2017).
3. Silvestro, P.C., Pignatti, S. Pascucci, S., et al.  Estimating wheat yield in China at the field and district scale from the assimilation of satellite data into the Aquacrop and simple algorithm for yield (SAFY) models", Remote Sensing, 9(5), p. 509 (2017).
4. Skakun, S., Vermote, E., Roger, J.C., et al. "Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale", AIMS Geosci, 3(2), pp. 163-186 (2017).
5. Zhuo, W., Huang, L., Li, J., et al. "Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation", Remote Sensing, 11(13), pp. 1-17 (2019).
6. Aranguren, M., Castellon, A., and Aizpurua, A. "Wheat yield estimation with NDVI values using a proximal sensing tool", Remote Sensing, 12(17), p. 2749 (2020).
7. Du, M. and Noguchi, N. "Monitoring of wheat growth status and mapping of wheat yield's within-field spatial variations using color images acquired from UAVcamera system", Remote Sensing, 9(3), pp. 1-14 (2017).
8. Franch, B., Vermote, E.F., Roger, J.-C., et al. "A 30+ year AVHRR land surface reflectance climate data record and its application to wheat yield monitoring", Remote Sensing, 9(3), pp. 1-14 (2017).
9. Ren, J., Chen, Z. Zhou, Q., et al. "Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China", International Journal of Applied Earth Observation and Geoinformation, 10(4), pp. 403-413 (2008).
10. Huang, J., Tian, L., Liang, Sh., et al. "Improving winter wheat yield estimation by assimilation of the leaf area index from landsat TM and MODIS data into the WOFOST model", Agricultural and Forest Meteorology, 204, pp. 106-121 (2015).
11. Nazeer, A., Waqas, M.M. Ali, S., et al. "Land use land cover classification and wheat yield prediction in the lower Chenab Canal system using remote sensing and GIS", Big Data in Agriculture (BDA), 2(2), pp. 47-51 (2020).
12. Sehgal, V.K., Sastri, C.V.S., and Kalra, N. "Farmlevel yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model", Journal of the Indian Society of Remote Sensing, 33(1), pp. 131-136 (2005).
13. Nigam, R., Vyas, S.S., Bhattacharya, B.K., et al. "Retrieval of regional LAI over agricultural land from an Indian geostationary satellite and its application for crop yield estimation", Journal of Spatial Science, 62(1), pp. 103-125 (2017).
14. Silvestro, P.C., Pignatti, S. Pascucci, S., et al.  Estimating wheat yield in China at the field and district scale from the assimilation of satellite data into the Aquacrop and simple algorithm for yield (SAFY) models", Remote Sensing, 9(5), pp. 1-24 (2017).
15. Zecha, C.W., Peteinatos, G.G., Link, J., et al. "Utilisation of ground and airborne optical sensors for nitrogen level identification and yield prediction in wheat", Agriculture, 8(6), pp. 1-13 (2018).
16. Vallentin, C., Harfenmeister, K., Itzerott, S., et al. "Suitability of satellite remote sensing data for yield estimation in northeast Germany", Precision Agriculture, 23, pp. 52-82 (2021).
17. Fieuzal, R., Bustillo, V., Collado, D., et al. "Combined use of multi-temporal Landsat-8 and Sentinel-2 images for wheat yield estimates at the intra-plot spatial scale", Agronomy, 10(3), pp. 1-15 (2020).
18. Tuvdendorj, B., Wu, B., Zeng, H., et al., "Determination of appropriate remote sensing indices for spring wheat yield estimation in Mongolia", Remote Sensing, 11(21), pp. 1-21 (2019).
19. Jelinek, Z., Kumhalova, J., Chyba, J., et al. "Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic", International Agrophysics, 34(3), pp. 391-406 (2020).
20. Du, M. and Noguchi, N. "Monitoring of wheat growth status and mapping of wheat yield's within-field spatial variations using color images acquired from uavcamera system", Remote Sens, 9(3), p. 289 (2017).
21. Wu, Y., Xu, W., Huang, H., et al. "Winter wheat yield estimation at the field scale by assimilating Sentinel-2 LAI into crop growth model", IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 4383-4386 (2020).
22. Wang, Y., Xu, X., Huang, L., et al. "An Improved CASA model for estimating winter wheat yield from remote sensing images", Remote Sensing, 11(9), pp. 1-19 (2019).
23. Li, H., Chen, Z., Liu, G., et al. "Improving winter wheat yield estimation from the CERES-wheat model to assimilate leaf area index with different assimilation methods and spatio-temporal scales", Remote Sensing, 9(3), pp. 1-23 (2017).
24. Padilla, F.L.M., Maas, S.J., Gonzalez-Dugo, M.P., et al. "Monitoring regional wheat yield in southern Spain using the GRAMI model and satellite imagery", Field Crops Research, 130, pp. 145-154 (2012).
25. Wang, X., Qadir, M. Rasul, F., et al. "Response of soil water and wheat yield to rainfall and temperature change on the Loess Plateau, China", Agronomy, 8(7), pp. 1-13 (2018).
26. Donga, J., Lub, H. Wangb, Y., et al. "Estimating winter wheat yield based on a light use efficiency model and wheat variety data", ISPRS Journal of Photogrammetry and Remote Sensing, 160, pp. 18-32 (2020).
27. Roell, Y.E., Beucher, A. Mller, P.G., et al. "Comparing a random forest based prediction of winter wheat yield to historical yield potential", Agronomy, 10(3), pp. 1-17 (2020).
28. Nagy, A., Szabo, A., Adeniyi, O.D., et al. "Wheat yield forecasting for the Tisza river catchment using Landsat 8 NDVI and SAVI time series and reported crop statistics", Agronomy, 11(4), pp. 1-13 (2021).
29. Unal, E., Yildiz, H., Mermer, A., et al. "Yield estimation of winter wheat in pre-harvest season by satellite imagery based regression models", Turkish Journal of Agricultural Engineering Research, 1(2), pp. 390-403 (2020).
30. Vannoppen, A., Gobin, A., Kotova, L., et al. "Wheat yield estimation from NDVI and regional climate models in Latvia", Remote Sensing, 12(14), pp. 1-20 (2020).
31. Korohou, T., Okinda, C., Li, H., et al. "Wheat grain yield estimation based on image morphological properties and wheat biomass", Journal of Sensors, 2020, p. 1571936 (2020).
32. Stephen, P. and Jaganathan, S. "Linear regression for pattern recognition", In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (2014).
33. Yang, C., Everitt, J.H., Bradford, J.M., et al. "Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability", Precision Agriculture, 5(5), pp. 445-461 (2004).
34. Al-Qahtani, F.H. and Crone, S.F. "Multivariate knearest neighbour regression for time series data - A novel algorithm for forecasting UK electricity demand", The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1-8 (2013).
35. Deepali Patil, D., Badarpura, S., Jain, A., et al. "Rainfall prediction using linear approach & neural networks and crop recommendation based on decision tree", International Journal of Engineering Research & Technology (IJERT), 9(4), pp. 394-399 (2020).
36. Breiman, L. "Random forests", Machine Learning, 45(1), pp. 5-32 (2001).
37. Smola, A.J. and Scholkopf, B. "A tutorial on support vector regression", Statistics and Computing, 14, pp. 99-222 (2004).
38. Schulz, E., Speekenbrink, M., and Krause, A. "A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions", Journal of Mathematical Psychology, 85, pp. 1-16 (2018).