ESTIMATING CORN YIELD in the United States with MODIS EVI and MACHINE LEARNING METHODS

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 3, Issue , 2016, Pages 131-136

  Kuwata, K ^a   Shibasaki, R ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Artificial Neural Network; Corn Yield; Deep Learning; MODIS EVI; Support Vector Machine; Wavelet Transform

Indexed keywords

EID: 85048923897     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-annals-III-8-131-2016     Document Type: Conference Paper

References (24)

1. Aggarwal, R. and Rathore, S., 2011. Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold. International Journal of Computer Applications 20(5), pp. 14-19.
2. Conradt, S., Bokusheva, R., Finger, R. and Kussaiynov, T., 2014. Yield trend estimation in the presence of farm heterogeneity and non-linear technological change. Quarterly Journal of International Agriculture 53(2), pp. 121-140.
3. Cotter, A., Shamir, O., Srebro, N. and Sridharan, K., 2011. Better mini-batch algorithms via accelerated gradient methods. In: J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira and K. Q. Weinberger (eds), Advances in Neural Information Processing Systems 24, Curran Associates, Inc., pp. 1647-1655.
4. Donoho, D. L., 1995. De-noising by Soft-thresholding. IEEE Trans. Inf. Theor. 41(3), pp. 613-627.
5. Erhan, D., Courville, A. and Vincent, P., 2010. Why Does Unsupervised Pre-training Help Deep Learning? Journal of Machine Learning Research 11, pp. 625-660.
6. FAO, 2011. The State of theWorld's land and water resources for Food and Agriculture, Managing systems at risk.
7. Galford, G. L., Mustard, J. F., Melillo, J., Gendrin, A., Cerri, C. C. and Cerri, C. E., 2008. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sensing of Environment 112(2), pp. 576-587.
8. Hall, F. G. and Badhwar, G. D., 1987. Signature-Extendable Technology: Global Space-Based Crop Recognition. IEEE Transactions on Geoscience and Remote Sensing GE-25(1), pp. 93-103.
9. HLPE, 2013. Biofuels and food security. Technical report, the High Level Panel of Experts on Food Security and Nutrition.
10. Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X. and Ferreira, L., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1-2), pp. 195-213.
11. Jiang, D., Yang, X., Clinton, N. and Wang, N., 2004. An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing 25(9), pp. 1723-1732.
12. Karimi, Y., Prasher, S. O., Madani, A. and Kim, S., 2008. Application of support vector machine technology for the estimation of crop biophysical parameters using aerial hyperspectral observations. Canadian Biosystems Engineering.
13. Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J. and Ng, A. Y., 2011. Building high-level features using large scale unsupervised learning. International Conference in Machine Learning p. 38115.
14. Li, A., Liang, S., Wang, A. and Qin, J., 2007. Estimating Crop Yield from Multi-temporal Satellite Data Using Multivariate Regression and Neural Network Techniques. Photogrammetric Engineering & Remote Sensing 73(10), pp. 1149-1157.
15. Mountrakis, G., Im, J. and Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66(3), pp. 247-259.
16. Nagai, M., Rajbhandari, A., Ono, M. and Shibaski, R., 2014. Information Search, Integration, and Personalization: International Workshop, ISIP 2013, Bangkok, Thailand, September 16-18, 2013. Revised Selected Papers. Springer International Publishing, Cham, chapter Earth Observation Data Interoperability Arrangement with Vocabulary Registry, pp. 128-136.
17. Paswan, R. P. and Begum, S. A., 2013. Regression and Neural Networks Models for Prediction of Crop Production. International Journal of Scientific & Engineering Research 4(9), pp. 98-108.
18. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N. and Ohno, H., 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment 96(3-4), pp. 366-374.
19. Thornton, P., Thorthon, M., Mayer, B., Wilhelmi, N., Wei, Y., Devarakonda, R. and Cook, R., 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2.
20. USDA, NASS, RDD, G. I., 2013. USDA, National Agricultural Statistics Service, Cropland Data Layer for the United States.
21. Vapnik, V. N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY, USA.
22. Vermote, E., 2015. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006.
23. Wang, K. and Zhang, Q., 2013. Crop yield risk assessment. In: Intelligent Systems and Decision Making for Risk Analysis and Crisis Response, Communications in Cybernetics, Systems Science and Engineering & Proceedings, CRC Press, pp. 709-716.
24. Wardlow, B., Egbert, S. and Kastens, J., 2007. Analysis of timeseries MODIS 250m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment 108(3), pp. 290-310.