Wheat Yield Prediction with Machine Learning based on MODIS and Landsat NDVI Data at Field Scale

Abstract

Accurate estimation of wheat yield using Remote Sensing-based models is critical in determining the effects of agricultural drought and sustainable food planning. In this study, Winter wheat yield was estimated for large fields and producer fields by applying Normalized Difference Vegetation Index (NDVI) based linear models (simple linear regression and multiple linear regression) and Machine Learning (ML) techniques (support vector machine_svm, multilayer perceptron_mlp, random forest_rf). In this study, depending on the ecological zone, crop sampling was carried out from 380 rainfed parcels where wheat was planted. On the basis of crop development periods (CDP), the highest correlation between NDVI and yield occurred during the flowering period. In this period, coefficient of determination (R2) was 63% in TIGEM fields and 50% in producer fields for MODIS data, and 61% and 65% for Landsat data, respectively. In TIGEM fields, the best prediction performance was obtained with the MLP model for MODIS (RMSE:0.23-0.65 t/ha) and Landsat (RMSE: 0.28-0.64 t/ha). On the other hand, the highest forecasting accuracy was acquired with the SVM model in producer fields. The RMSE values ranged from 0.74 to 0.80 t/ha for MODIS and 0.51 to 0.60 t/ha for Landsat 8. The error value obtained with MODIS was approximately 1.4 times higher than the Landsat 8 data in producer fields. For yield estimation, the best estimation can be made 4-6 weeks before the harvest. In regional yield estimations, satellite-based ML techniques outperformed linear models. ML models have shown that it can play an important role in crop yield prediction. In crop yield estimation, it is a priority to consider the impact of climate change and ecological differences on crop development.

Keywords

• Crop yield prediction
• Remote Sensing
• Machine learning
• NDVI
• Wheat

References

1. Abebe, G., Tadesse, T., Gessesse, B. (2022). Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia. Journal of the Indian Society of Remote Sensing, 50(1):143–157.
2. Atzberger, C. (2013). Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens. 5, 949–981.
3. Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114, 1312–1323.
4. Boken, V. K., Shaykewich, C. F. (2002). Improving an operational wheat yield model for the Canadian Prairies using phenological-stage-based normalized difference vegetation index, International Journal of Remote Sensing, 23 (20):4157-4170.
5. Breiman, L. (2001). Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324
6. Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., Xu, T., Asseng, S., Zhang, Y., You, L., & Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric. For. Meteorology, 274, 144–159.
7. Chen, P., Jing, Q. (2017). A comparison of two adaptive multivariate analysis methods (PLSR and MLP) for winter wheat yield forecasting using Landsat-8 OLI images. ScienceDirect, 59, 987–995.
8. Chlingaryan, A., Sukkarieh, S., Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput. Electron. Agric. 151, 61–69.

9. Cooper, M., Voss Fels, K. P., Messina, C., Tang, T., Hammer, G. L. (2021). Tackling G×E×M interactions to close on farm yield gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. Theoretical and Applied Genetics (2021)134:1625–1644.
10. Dempewolf, J., Adusei, B., Becker, I., Hansen, M., Potapov, P., Khan, A., Barker, B. (2014). Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sens. 6 (10):9653–9675.
11. Fischer, R. A., Byerlee, D., Edmeades, G. O. (2014). Crop Yields and Global Food Security: Will Yield Increase Continue to Feed the World; Australian Centre for International Agricultural Research: Canberra, Australia, Volume 158, p. 634.
12. Gomez, D., Salvador, P., Sanz, J., Casanova, J. L. (2021). Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2020.108317
13. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. Retrieved in May 11, 2020 from http://www. deeplearningbook.org Gunn, S. R. (1998). Support vector machines for classification and regression. ISIS Tech. Rep., 8, 14, 5–16.
14. Guo, Y., Fu, Y., Hao, F., Zhang, X., Wu, W., Jin, X., Bryant, C. R., & Senthilnath, J. (2021). Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol. Ind., 120, 106935.
15. Han, J., Zhang,, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., Zhang, J. (2020). Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China. Remote Sens. 2020, 12, 236. https://doi:10.3390/rs12020236
16. Hearst, M. A., Dumais, S .T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intell. Syst. Appl., 13, 18–28.
17. Huang, J., Wang, X., Li, X., Tian, H., Pan, Z. (2013). Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR. Plos One, 8(8). https://doi:10.1371/journal. pone.0070816
18. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ, 83, 195–213.
19. Jelínek, Z., Kumhálová, J., Chyba, J., Wohlmuthová, M., Madaras, M., Kumhála, F. (2020). 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. Int. Agrophys., 2020, 34, 391-406. https://doi:10.31545/intagr/126593
20. Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K. M., Gerber, J. S., Reddy, V. R., Kim, S. (2016). Random forests for global and regional crop yield predictions. PLoS One 11, e0156571. https://doi.org/10.1371/journal.pone.0156571
21. Ji, B., Sun, Y., Yang, S., Wan, J. (2007). Artificial neural networks for rice yield prediction in mountainous regions. Journal of Agricultural Science, 145, 249–261.
22. Ji, Z., Pan, Y., Zhu, X., Zhang, D., & Wang, J. (2022). A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics. Ecological Indicators, 137, 108759.
23. Johnson, M. D., Hsieh, W. W., Cannon, A. J., Davidson, A., Bédard, F. (2016). Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods, 218-219 74-84.
24. Johnson, D. M. (2016). A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. Intern J Appl Earth Obs Geoinform 52: 65-81.
25. Joshi, V. R., Kazula, M. J., Coulter, J. A., Naeve, S. L., Garcia, A. G. (2021). In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt. International Journal of Biometeorology, 65:489–502. https://doi.org/10.1007/s00484-020-02039-z
26. Jönsson, P., Eklundh, L. (2004). TIMESAT-A program for analyzing time-series of satellite sensor data. Computers and Geoscience, 30, 833−845.
27. Ju, S., Lim, H., Won Ma, J., Kim, S., Lee, K., Zhao, S., Heo, J. (2021). Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models. Agricultural and Forest Meteorology 307, 108530.
28. Kamir, E., Waldner, F., Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS J. Photogramm. Remote Sens. 160, 124–135.
29. Kaul, M., Hill, R. L., Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85, 1–18.
30. Klompenburg, T. V., Kassahun, A., Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709.
31. Kouadio, L., Duveiller, G., Djaby, B., El Jarroudi, M., Defourny, P., Tychon, B. (2012). Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data, International Journal of Applied Earth Observation and Geoinformation, 18, 111–118.
32. Li, Z., Chen, Z., Cheng, Q., Duan, F., Sui, R., Huang, X., Xu, H. (2022). UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat. Agronomy 2022, 12, 202. https://doi.org/10.3390/agronomy12010202
33. Lopresti, M. F., Di Bella, C. M., Degioanni, A. (2015). Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province. Argentina, Information Processing In Agriculture, 73–84.
34. Lyle, G., Lewis, M., Ostendorf, B. (2013). Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale. Remote Sens., 5, 1549-1567.
35. Mashaba, Z., Chirima, G., Botai, J. O., Combrinck, L., Munghemezulu, C., Dube, E. (2017). Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region. South African Journal of Science, 113 (11/12). http://dx.doi.org/10.17159/sajs.2017/20160201
36. Mirasi, A., Mahmoudi, A., Navid, H., Kamran, K. V., Asoodar, M. A. (2019). Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data. Geocarto International, https://doi.org/10.1080/10106049.2019.1641561
37. Mkhabela, M. S., Bullock, P., Raj, S., Wang, S., Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3), 385–393.
38. Nagy, A., Szabó, A., Adeniyi, O. D., Tamás, J. (2021). Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics. Agronomy, 11, 652.
39. Panek, E., Gozdowski, D. (2021). Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries. Agronomy, 11, 340.
40. Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., Athanasiadis, I. N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems. Volume 187.
41. Rasmussen, M. S. (1997). Operational yield forecast using AVHRR NDVI data: reduction of environmental and interannual variability. International Journal of Remote Sensing, 18(5), 1059 -1077.
42. Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., Chatterjee, S. (2019). Climate change has likely already affected global food production. PLoS ONE 14(5): e0217148.
43. Ren, J., Chen, Z., Zhou, Q., Tang, H. (2008). Regional yield estimation for winter wheat with 5 MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10, 403–413.
44. Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. In Third ERTS Symposium, Washington DC, USA, NASA SP 351 I 309-317.
45. Satir, O., Berberoglu, S., (2016). Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crops Res. 192, 134–143.
46. Savitzky, A., Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627– 1639.
47. Sayago, S., Bocco, M. (2018). Crop yield estimation using satellite images: comparison of linear and non-linear models. AGRISCIENTIA, 2018, VOL. 35: 1-9.
48. Shiferaw, B., Smale, M., Braun, H. J., Duveiller, E., Reynolds, M., Muricho, G. (2013). Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 5, 291–317.
49. Skakun, S., Vermote, E., Roger, J. C., Franch, B. (2017). Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale. AIMS Geosci, 3, 163–186.
50. Teasdale, R. J., Cavigelli, A. M. (2017). Meteorological fluctuations define long-term crop yield patterns in conventional and organic production systems. Scientific Reports .7: 688.
51. TUİK. (2021). Turkish Statistical Institute. Retrieved September 09, 2021 from https://biruni.tuik.gov.tr/medas/ Vannoppen, A., Gobin, A. (2021). Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy,11(5):946. https://doi.org/10.3390/agronomy11050946
52. Wang, Y., Zhang, Z., Feng, L., Du, Q., Runge, T. (2020). Combining Multi-Source Data and Machine Learning Approaches to PredictWinter Wheat Yield in the Conterminous United States. Remote Sens., 12, 1232; https://doi.org/10.3390/rs12081232