Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods

Agricultural and Forest Meteorology

Volumn 218-219, Issue , 2016, Pages 74-84

  Johnson, Michael D ^a   Hsieh, William W ^a   Cannon, Alex J ^b,e   Davidson, Andrew ^c   Bédard, Frédéric ^d  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF VICTORIA   (Canada)

^c AGRICULTURE AND AGRI FOOD CANADA   (Canada)

^d STATISTICS CANADA   (Canada)

^e ENVIRONMENT CANADA   (Canada)

Author keywords

Artificial neural networks; Canadian Prairies; Crop yield forecasting; EVI; Machine learning; MODIS; NDVI; Remote sensing

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AVHRR; CROP YIELD; MACHINE LEARNING; MODIS; NDVI; NUMERICAL MODEL; REGRESSION ANALYSIS; YIELD RESPONSE;

CANADA; PRAIRIE PROVINCES;

BRASSICA NAPUS VAR. NAPUS; HORDEUM; TRITICUM AESTIVUM;

EID: 84954145006     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2015.11.003     Document Type: Article

References (33)

1. Alvarez R. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. Eur. J. Agron. 2009, 30:70-77.
2. Bhat P.C., Prosper H.B. Bayesian neural networks. Proceedings of Statistical Problems in Particle Physics 2005, vols. 12-15:151-154. Astrophysics and Cosmology, Oxford, UK.
3. Bolton D.K., Friedl M.A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. Forest Meteorol. 2013, 173:74-84.
4. Cannon A.J., Whitfield P.H., Lord E.R. Synoptic map-pattern classification using recursive partitioning and principal component analysis. Monthly Weather Rev. 2002, 130:1187-1206.
5. Fensholt R., Sandholt I. Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi-arid environment. Int. J. Rem. Sens. 2005, 26:2561-2594.
6. Finnis J., Hsieh W.W., Lin H., Merryfield W. Nonlinear post-processing of numerical seasonal climate forecasts. Atmos.-Ocean 2012, 50:207-218.
7. Gilleland E. Confidence Intervals for Forecast Verification 2010, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.174.5002%26rep=rep1%26type=pdf.
8. Gitelson A.A., Kaufman Y.J. MODIS NDVI optimization to fit the AVHRR data series-spectral considerations. Rem. Sens. Environ. 1998, 66:343-350.
9. Guo W.W., Xue H. An incorporative statistic and neural approach for crop yield modeling and forecasting. Neural Comput. Appl. 2012, 21:109-117.
10. Hsieh W.W., Tang B. Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Am. Meteorol. Soc. 1998, 79:1855-1870.
11. Hsieh W.W. Machine Learning Methods in the Environmental Sciences 2009, Cambridge University Press, Cambridge, 349 pp.
12. Huete A., Didan K., Miura T., Rodriguez E.P., Gao G., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Rem. Sens. Environ. 2002, 83:195-213.
13. Huete A.R. A soil-adjusted vegetation index (SAVI). Rem. Sens. Environ. 1988, 25:295-309.
14. Jensen J.R. Remote Sensing of the Environment: An Earth Resource Perspective 2007, Prentice Hall, Upper Saddle River, NJ, USA.
15. Jiang D., Yang X., Clinton N., Wang N. An artificial neural network model for estimating crop yields using remotely sensed information. Int. J. Rem. Sens. 2004, 25(9):1723-1732.
16. Justice C.O., Eck T.F., Tanré D., Holben B.N. The effect of water vapour on the normalized difference vegetation index derived for the Sahelian region from NOAA AVHRR data. Int. J. Rem. Sens. 1991, 12:1165-1187.
17. Kaul M., Hill R.L., Walthall C. Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 2005, 85:1-18.
18. Li A., Liang S., Wang A., Qin J. Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogramm. Eng. Rem. Sens. 2007, 73(10):1149-1157.
19. Li Z., Li X., Wei D., Xu X., Wang H. An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in northern Hebei Province, China. Procedia Environ. Sci. 2010, 2:964-969.
20. Mkhabela M.S., Bullock P., Raj S., Wang S., Yang Y. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric. Forest Meteorol. 2011, 151:385-393.
21. Murtagh F., Legendre P. Ward's hierarchical clustering method: clustering criterion and agglomerative algorithm 2011, arXiv:1111.6285, http://arxiv.org/abs/1111.6285.
22. Myneni R.B., Ramakrishna R., Nemani R.R., Running S.W. Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Rem. Sens. 1997, 35:1380-1393.
23. Prasad A.K., Chai L., Singh R.P., Kafatos M. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Observ. Geoinform. 2006, 8:26-33.
24. Qian B., De Jong R., Warren R., Chipanshi A., Hill H. Statistical spring wheat yield forecasting for the Canadian Prairie provinces. Agric. Forest Meteorol. 2009, 149:1022-1031.
25. Rasmussen C.E. A practical Monte Carlo implementation of Bayesian learning. Adv. Neural Inform. Process. Syst. 1996, 8:598-604.
26. Singh K., Xie M. Bootstrap: A Statistical Method 2008, http://www.stat.rutgers.edu/home/mxie/rcpapers/bootstrap.pdf.
27. Trishchenko A.P., Cihlar J., Li Z. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Rem. Sens. Environ. 2002, 81:1-18.
28. van Hinsbergen C., van Lint J., van Zuylen H. Bayesian committee of neural networks to predict travel times with confidence intervals. Transport. Res. C: Emerg. Technol. 2009, 17:498-509.
29. Vermote E.F., Saleous N.Z. Calibration of NOAA16 AVHRR over a desert site using MODIS data. Rem. Sens. Environ. 2006, 105:214-220.
30. Vincente-Serrano S.M., Saz-Sanchez M.A., Cuadrat J.M. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate Res. 2003, 24:161-180.
31. Wilks D.S. Statistical Methods in Atmospheric Sciences 2006, Elsevier, Amsterdam, 627 pp. 2nd edition.
32. Zeileis A., Hothorn T., Hornik K. Model-based recursive partitioning. J. Comput. Graph. Stat. 2008, 17:492-514.
33. Zeng Z., Hsieh W.W., Shabbar A., Burrows W.R. Seasonal prediction of winter extreme precipitation over Canada by support vector regression. Hydrol. Earth Syst. Sci. 2011, 15:65-74.