545
Views
25
CrossRef citations to date
0
Altmetric
Agronomy & Crop Ecology

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

, , , &
Pages 352-364 | Received 08 Feb 2012, Accepted 07 Apr 2013, Published online: 03 Dec 2015

  • Burgers, G., van Leeuwen, P.J. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126: 1719-1724.
  • Cao, W.X., Liu, T.M., Luo, W.H., Wang, S.H., Pan, J. and Guo, W.S. 2002. Simulating organ growth in wheat based on the organweight fraction concept. Plant Prod. Sci. 5: 248-256.
  • Crow, W. and Wood, E. 2003. The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97. Adv. Water Resour. 26: 137-149.
  • Curnel, Y., de Wit, A., Duveiller, G. and Defourny, P. 2011. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment. Agric. For. Meteorol. 151: 1843-1855.
  • Das, N.N., Mohanty, B., Cosh, M. and Jackson, T. 2008. Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04. Remote Sens. Environ. 112: 415-429.
  • de Wit, A.J.W., Boogaard, H.L. and van Diepen, C.A., 2005. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts. Agric. For. Meteorol. 135: 156-168.
  • de Wit, A.J.W. and van Diepen, C.A. 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agric. For. Meteorol. 146: 38-56.
  • Delécollea, R., Maas, S., Guérif, M. and Baret, F. 1992. Remote sensing and crop production models: present trends. ISPRS J. Photogramm. 47: 145-161.
  • Dorigo, W.A., Zurita-Milla, R., de Wit, A.J., Brazile, J., Singh, R. and Schaepman, M.E. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Applied Earth Observation Geoinfo. 9: 165-193.
  • Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99: 10143-10162.
  • Feng, W., Yao, X., Zhu, Y., Tian, Y.C. and Cao, W.X. 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 28: 394-404.
  • Feng, W., Zhu, Y., Yao, X., Tian, Y.C. and Cao, W.X. 2009. Monitoring leaf dry weight and leaf area index in wheat with hyperspectral remote sensing. Chinese J. Plant Ecol. 33: 34-44*.
  • Godwin, D., Ritchie, J., Singh, U., Hunt, L. 1990. A user’s guide to CERES wheat-V2.10. Muscle Shoals: International Fertilizer Development Center.
  • Han, X.J. and Li, X. 2008. Review of the nonlinear filters in the land data assimilation. Adv. Earth Sci. 23: 813-820*.
  • Houtekamer, P. and Mitchell, H. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126: 796-811.
  • Hu, J.C., Cao, W.X., Zhang, J.B., Jiang, D. and Feng, J. 2004. Quantifying responses of winter wheat physiological processes to soil water stress for use in growth simulation model. Pedosphere 14: 509-518.
  • Ide, K., Courtier, P., Ghil, M. and Lorenc, A.C. 1997. Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Jpn. 75: 181-189.
  • Inoue, Y., Susan, M.M. and Horie, T. 1998. Analysis of spectral measurements in paddy field for predicting rice growth and yield based on a simple crop simulation model. Plant Prod. Sci. 1: 269-279.
  • Jamieson, P.D., Porter, J.R. and Wilson, D.R. 1991. A test of the computer simulation model ARC–WHEAT1 on wheat crops grown in New Zealand. Field Crops Res. 27: 337-350.
  • Launay, M. and Guerif, M. 2005. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agr. Ecosyst. Environ. 111: 321-339.
  • Li, C.J., Wang, J.H., Wang, X., Liu, F. and Li, R. 2008. Methods for integration of remote sensing data and crop model and their prospects in agricultural application. Trans. CSAE 24: 295-301*.
  • Lorenc, A.C. 1986. Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112: 1177-1194.
  • Numata, I., Roberts, D.A., Chadwick, O.A., Schimel, J.P., Galvão, L.S. and Soares, J.V. 2008. Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers. Remote Sens. Environ. 112: 1569-1583.
  • Pan, J., Zhu, Y., Cao, W.X., Dai, T.B. and Jiang, D. 2006. Predicting the protein content of grain in winter wheat with meteorological and genotypic factors. Plant Prod. Sci. 9: 323-333.
  • Pan, J., Zhu, Y. and Cao, W.X. 2007. Modeling plant carbon flow and grain starch accumulation in wheat. Field Crops Res. 101: 276-284.
  • Reichle, R.H., Crow, W.T. and Keppenne, C.L. 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. [Online]. Available at http://onlinelibrary.wiley.com. Wiley Online Library. doi: 10.1029/2007WR006357. Water Resour. Res. 44: W03423.
  • Shen, Y., Niu, Z., Chen, F. and Wang, C.Y. 2007. Rational consideration about construction land expansion of Changsha urban area in the last ten years. Geogr. Geoinfo. Sci. 23: 27-30, 54.
  • Slater, A. and Clark, M. 2009. Snow data assimilation via an Ensemble Kalman Filter. J. Hydrometeor 7: 478-493.
  • RSI, 2006. ENVI: Environment for Visualizing Images, Version 4.3. Research Systems Inc., Boulder, CO, USA.
  • Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ.16: 125-141.
  • Wallach, D., Goffinet, B., Bergez, J., Debaeke, P., Leenhardt, D. and Aubertot, J. 2001. Parameter estimation for crop models: a new approach and application to a corn model. Agron. J. 93: 757-766.
  • Wang, D.W., Wang, J.D., Liang, S.L. 2010. Retrieving crop leaf area index by assimilation of MODIS data into crop growth model. Sci. China Earth Sci. 53: 721-730*.
  • Whitaker, J.S. and Hamail, T.M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130: 1913-1924.
  • Xiao, Z.Q., Liang, S.L., Wang, J.D., Jiang, B. and Li, X.J. 2011. Realtime retrieval of leaf area index from MODIS time series data. Remote Sens. Environ. 115: 97-106.
  • Yan, M.C., Cao, W.X., Luo, W.H. and Jiang, H.D. 2000. A mechanistic model of phasic and phenological development of wheat. I. Assumption and description of the model. Chinese J. Appl. Ecol. 11: 355-359*.
  • Yang, S.B., Shen, S.H., Li, B.B., Toan, T.L. and He, W. 2008. Rice mapping and monitoring using ENVISAT ASAR data. IEEE Geosci. Remote Sens. Lett. 5: 108-112.
  • Yuan, J.G., Niu, Z. and Wang, X.P. 2009. Atmospheric correction of Hyperion hyperspectral image based on FLAASH. Spectroscopy Spectral Anal. 29: 1181-1185*.
  • Zhao, Y. X., Zhou, X.J. and Liang, S.L. 2005. Methods and application of coupling remote sensing data and crop growth models advance in research. J. Nat. disasters 14: 103-109*.
  • Zhu, Y., Yao, X., Tian, Y.C., Liu, X.J. and Cao, W.X. 2008. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. Int. J. Appl. Earth Obs. Geoinf.10: 1-10.
  • Zhuang, H.Y., Cao, W.X., Jiang, S.X. and Wang, Z.G. 2004. Simulation on nitrogen uptake and partitioning in crops. Syst. Sci. Comprehen. Stud. Agric. 20: 5-8*.