Spring Row Crops Productivity Prediction Using Normalized Difference Vegetation Index

• Autorzy: Vozhehova Raisa , Maliarchuk Mykola , Biliaieva Iryna , Lykhovyd Pavlo , Maliarchuk Anastasiia , Tomnytskyi Anatoliy

Identyfikatory

DOI

10.12911/22998993/123473

• Języki publikacji: EN

Abstrakty

EN

The results of statistical modelling for the yields prediction of spring row crops, namely, maize, sorghum and soybean, depending on the values of the remotely sensed normalized difference vegetation index (NDVI) at critical stages of the crops growth and development were presented. The spatial NDVI data obtained from the Sentinel-2 satellite were used to create the models. Quadratic regression analysis was applied to develop the yielding models based on true yield data of the crops obtained in the period of 2017 and 2018 at the experimental field of the Institute of Irrigated Agriculture of NAAS, Ukraine. The results of statistical modelling revealed that the method is suitable for precise yield prediction, and the best stages for NDVI screening and use in this purpose are different for the studied crops. The best accuracy of prediction could be obtained at the stage of tasselling (VT) or silking (R1) for maize (the mean absolute percentage error MAPE is 8.75%); at the stage of second trifoliate (V2) for soybean (MAPE is 3.75%), and at the stage of half bloom (S6) for sorghum (MAPE is 17.62%). The yield predictions by NDVI are reliable at a probability level of 95% (p < 0.05).

Słowa kluczowe

EN

maize; sorghum; soybean; spatial monitoring; yield prediction

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2020
• Tom: Vol. 21, nr 6
• Strony: 176--182
• Opis fizyczny: Bibliogr. 35 poz., tab.

Twórcy

autor

Vozhehova Raisa

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

autor

Maliarchuk Mykola

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

autor

Biliaieva Iryna

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

autor

Lykhovyd Pavlo

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

autor

Maliarchuk Anastasiia

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

autor

Tomnytskyi Anatoliy

• Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine

Bibliografia

• 1. Báez-González, A.D., Chen, P.Y., Tiscareño-López, M., Srinivasan, R. 2002. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop Science, 42, 1943–1949.
• 2. Bolton, D.K., Friedl, M.A. 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173, 74–84.
• 3. Carlson, T.N., Ripley, D.A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote sensing of Environment, 62, 241–252.
• 4. De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F. 2016. Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48.
• 5. de Oliveira, M.F., Ormond, A.T.S., de Freitas Noronha, R.H., dos Santos, A.F., Zerbato, C., Furlani, C.E.A. 2019. Prediction Models of Corn Yield by NDVI in Function of the Spacing Arrangement. Journal of Agricultural Science, 11, 493–500.
• 6. Everitt, B.S., Skrondal, A. 2010. The Cambridge Dictionary of Statistics. Cambridge Univ Press, Cambridge, UK.
• 7. Fang, H., Liang, S., Hoogenboom, G. 2011. Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation. International Journal of Remote Sensing, 32, 1039–1065.
• 8. Fernandes, J.L., Ebecken, N.F.F., Esquerdo, J.C.D.M. 2017. Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. International Journal of Remote Sensing, 38(16), 4631–4644.
• 9. Fernandez-Ordoñez, Y.M. and Soria-Ruiz, J. 2017. Maize crop yield estimation with remote sensing and empirical models. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp. 3035–3038.
• 10. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Penuelas, J., Valentini, R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5, 28–41.
• 11. Gong, Z., Aldeen, M., & Elsner, L. 2002. A note on a generalized Cramer’s rule. Linear algebra and its applications, 340, 253–254.
• 12. Herold, M., Scepan, J., Clarke, K.C. 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A, 34, 1443–1458.
• 13. Johnson, D.M. 2014. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 141, 116–128.
• 14. Johnson, D.M. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International journal of applied earth observation and geoinformation, 52, 65–81.
• 15. Kustas, W.P. and Norman, J.M. 1996. Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, 41, 495–516.
• 16. Liaghat, S. and Balasundram, S.K. 2010. A review: The role of remote sensing in precision agriculture. American journal of agricultural and biological sciences, 5, 50–55.
• 17. Lykhovyd, P.V. 2020. Sweet corn yield simulation using normalized difference vegetation index and leaf area index. Journal of Ecological Engineering, 21, 228–236.
• 18. Maresma, A., Chamberlain, L., Tagarakis, A., Kharel, T., Godwin, G., Czymmek, K.J., Shields, E., Ketterings, Q.M. 2020. Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing. Computers and Electronics in Agriculture, 169, 105236.
• 19. McWilliams, D.A., Berglund, D.R., Endres, G.J. 1999. Soybean growth and management quick guide.
• 20. Moreno, J.J.M., Pol, A.P., Abad, A.S., Blasco, B.C. 2013. Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25, 500–506.
• 21. Mukaka, M. 2012. Statistics Corner: A guide to appropriate use of. Malawi Medical Journal, 24, 69–71.
• 22. Mulla, D.J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114, 358–371.
• 23. Nafziger, E. 2013. Corn. Illinois Agronomy Handbook. Crop Science Extension and Outreach, Urbana IL, USA.
• 24. Nagy, A., Fehér, J., Tamás, J. 2018. Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151, 41–49.
• 25. Petersen, L.K. 2018. Real-time prediction of crop yields from MODIS relative vegetation health: A continent-wide analysis of Africa. Remote Sensing, 10, 1726.
• 26. Raines, C.A. 2011. Increasing photosynthetic carbon assimilation in C3 plants to improve crop yield: current and future strategies. Plant physiology, 155, 36–42.
• 27. Rogan, J. and Chen, D. 2004. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in planning, 61, 301–325.
• 28. Roozeboom, K.L. and Prasad, P.V. 2019. Sorghum growth and development. In: Sorghum: State of the Art and Future Perspectives. American Society of Agronomy and Crop Science Society of America, Inc.
• 29. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309.
• 30. Stas, M., Van Orshoven, J., Dong, Q., Heremans, S., Zhang, B. 2016. A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT. In 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1–5). IEEE.
• 31. Zhu, X.G., Long, S.P., Ort, D.R. 2010. Improving photosynthetic efficiency for greater yield. Annual review of plant biology, 61, 235–261.
• 32. Zinke-Wehlmann, C., De Franceschi, P., Catellani, M., Dall’Agata, M. 2019. Early within-season yield prediction and disease detection using Sentinel satellite imageries and machine learning technologies in biomass sorghum. In Software Technology: Methods and Tools: 51st International Conference, TOOLS 2019, Innopolis, Russia, October 15–17, 2019, Proceedings. Springer Nature, pp. 227–234.
• 33. Tiwari, P. and Shukla, P. 2020. Artificial neural network-based crop yield prediction using NDVI, SPI, VCI feature vectors. In: Information and Communication Technology for Sustainable Development. Springer, Singapore, pp. 585–594.
• 34. Wang, R., Cherkauer, K., Bowling, L. 2016. Corn response to climate stress detected with satellitebased NDVI time series. Remote Sensing, 8, 269.
• 35. Xu, C. and Katchova, A.L. 2019. Predicting soybean yield with NDVI using a flexible Fourier transform model. Journal of Agricultural and Applied Economics, 51, 402–416.