Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques

Photogrammetric Engineering and Remote Sensing

Volumn 73, Issue 10, 2007, Pages 1149-1157

  Li, Ainong ^a,b,c   Liang, Shunlin ^b   Wang, Angsheng ^c   Qin, Jun ^d  

^a INSTITUTE OF MOUNTAIN HAZARDS AND ENVIRONMENT   (China)

^b UNIVERSITY OF MARYLAND   (United States)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^d BEIJING NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COST ACCOUNTING; ESTIMATION; FOOD SUPPLY; FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; REMOTE SENSING; SATELLITES;

IMPORT/EXPORT PLANS; MULTI-TEMPORAL SATELLITE DATA; MULTIVARIATE LINEAR REGRESSION (MLR) MODELS; POLICY MAKERS;

CROPS;

ARTIFICIAL NEURAL NETWORK; CROP YIELD; ESTIMATION METHOD; FOOD SUPPLY; MAIZE; MODEL VALIDATION; NDVI; NUMERICAL MODEL; PREDICTION; REMOTE SENSING; SATELLITE DATA; SOYBEAN; TIME SERIES;

CORN BELT; GREAT PLAINS; NORTH AMERICA; UNITED STATES;

GLYCINE MAX; ZEA MAYS;

EID: 35148859857     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.73.10.1149     Document Type: Article

References (28)

1. Demuth, H., and M. Beale, 2002. Neural Network Toolbox for Use with MATLAB, URL: http://www.mathworks.com (last date accessed: 24 June 2007).
2. Doraiswamy, P.C., J.L. Hatfield, T.J. Jackson, J.H. Prueger, B. Akhmedov, and A.J. Stern, 2004. Crop condition and yield simulations using Landsat and MODIS, Remote Sensing of Environment, 92:548-559.
3. Drummond, S.T., K.A. Sudduth, A. Joshi, S.J. Birrell, and N.R. Kitchen, 2003. Statistical and neural methods for site-specific yield prediction, Transactions of the American Society of Agncultural Engineers, 46(1):5-14.
4. Duan, Q.Y., S. Sorooshian, and V.K. Gupta. 1994. Optimal use of SCE-UA global optimization method for calibrating watershed models, Journal of Hydrology, 158:265-284.
5. Ermini, L., F. Catani, and N. Casagli, 2005. Artificial neural networks applied to land slide susceptibility assessment, Geomorphology, 66:327-343.
6. Foresee, F.D., and M.T. Hagan, 1997. Gauss-Newton approximation to Bayesian regularization, Proceedings of the 1997 International Joint Conference on Neural Networks, pp. 1930-1935.
7. Groten, S.M.E., 1993. NDVI-crop monitoring and early yield assessment of Burkina Faso, International Journal of Remote Sensing, 14:1495-1515.
8. Guréif, M., and C.L. Duke, 2000. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation, Agriculture, Ecosystems and Environment, 81:57-69.
9. Hansen, J.W., A. Potgieter, and M. Tippett, 2004. Using a general circulation model to forecast regional wheat yields in Northeast Australia, Agricultural and Forest Meteorology, 127:77-92.
10. Kimes, D.S., R.F. Nelson, M.T. Manry, and A.K. Fung, 1998. Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements, International Journal of Remote Sensing, 19(14):2639-2663.
11. Landau, S., R.A.C. Mitchell, V. Barnett, J.J. Colls, J. Craigon, and R.W. Payne, 2000. A Parsimonious, multiple-regression model of wheat yield response to environment, Agricultural and Forest Meteorology, 101:151-166.
12. Liang, S., 2004. Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, pp. 394-496.
13. Liang, S., H. Fang, J. Teasdale, M. Cavigelli, and G. Hoogenboom, 2004. Estimation of crop yield at the regional scale from MODIS observations, Proceedings of the IGARSS, 3:1625-1628.
14. Liu, J., C.E. Goering, and L. Tian, 2001. A neural network for setting target corn yield, Transactions of the American Society of Agricultural Engineers, 44(3):705-713.
15. Long, D.S., 1998. Spatial autoregression modeling of site-specific wheat yield, Geoderma, 85:181-197.
16. Mather, P.M., 2000. Computer Processing of Remotely-sensed Images: An Introduction, John Wiley & Sons, Chichester, U.K.
17. Prince, S.D., and C.O. Justice, 1991. Coarse resolution remote sensing of the Sahelian environment, International Journal of Remote Sensing, 12:1133-1421.
18. Rasmussen, M.S. 1992. Assessment of millet yield and production in northern Burkina Faso using integrated NDVI from the AVHRR, International Journal of Remote Sensing, 13:3431-3442.
19. Sharma, T., K.S. Sudha, N. Ravi, R.R. Navalgund, K.P. Tomar, N.V.K. Chakravarty, and D.K. Das, 1993. Procedures for wheat yield prediction using Landsat MSS and IRS-1A data, International Journal of Remote Sensing, 14:2509-2518.
20. Shibayama, M., and T. Akiyama, 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements, Remote Sensing of Environment, 36:45-53.
21. Steven, M.D., T.J. Malthus, F. Baret, X. Hui, and M.J. Chopping, 2003. Intercalibration of vegetation indices from different sensor systems, Remote Sensing of Environment, 88:412-422.
22. Tao, F.L, M. Yokozawa, Z. Zhang, Y. Xu, and H. Yousay, 2005. Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties, Ecological Modelling, 183:385-396.
23. Tappeiner, U., G. Tappeiner, J. Aschenwald, E. Tasser, and B. Ostendorf, 2001. GIS-based modeling of spatial pattern of snow cover duration in an alpine area, Ecological Modelling, 138:265-275.
24. Uno, Y., S.O. Prasher, R.Lacroix, P.K. Goel, Y. Karimi, A. Viau, and R.M. Patel, 2005. Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data, Computers and Electronics in Agriculture, 47:149-161.
25. Walthall, C., W. Dulaney, M. Anderson, J. Norman, H. Fang, and S. Liang, 2004. A comparison of empirical and neural network approaches for estimating corn and soybeans leaf area index from Landsat ETM+ imagery, Remote Sensing of Environment, 92:465-474.
26. Wheeler T.R., P.Q. Craufurd, R.H. Ellis, J.R. Porter, and P.V. Vara Prasad, 2000. Temperature variability and the yield of annual crops, Agriculture, Ecosystems and Environment, 82:159-167.
27. Wu, B.F., 2004. China crop watch system with remote sensing, Journal of Remote Sensing, 8(6):481-497 (in Chinese).
28. Xiao, X.T., S. Boles, J.Y. Liu, D. Zhuang, S. Frolking, C. Li, W. Salas, and B. Moore III, 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images, Remote Sensing of Environment, 95:480-492.