Crop growth simulation models, such as the crop simulation models (CSMs)–Crop Environment Resource Synthesis (CERES) estimate crop production, water and nitrogen balances, and carbon dynamics through a deterministic scheme with input data such as field and climate conditions, crop characteristics, and management practice. Remote sensing has helped providing input parameters and validating model outputs for crop growth simulation models.

Prof. FANG Hongliang, Institute of Geographic Sciences and Natural Resources Research (IGSNRR) and his colleagues have studies the integration of remote sensing data and crop growth simulation models for regional corn yield estimation. They developed an integrated crop simulation system (ICSS) which facilitates the assimilation of moderate resolution (about 1 km) remote sensing data with the CSM-CERES crop growth simulation models. Remote sensing parameters, such as leaf area index (LAI) and vegetation index can be compared with values simulated from crop models. Remote sensing LAI and vegetation index can also be applied individually or jointly to readjust the input parameters for crop models in order to get a better performance. Users can choose the input variables in the automatic readjustment process, such as planting date, planting density, row space, and nitrogen application. In the initial phase, ICSS has automated the assimilation of the CSM-CERES-Maize model and both LAI and vegetation index products derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The system has been successfully applied to estimate regional corn yield in Indiana, USA.

The studies have been published in the International Journal of Remote Sensing 201132(4), 2008 29(10).

References:

Fang, H., S. Liang, G. Hoogenboom, 2011. Integration of MODIS products and a crop simulation model for crop yield estimation. International Journal of Remote Sensing, 32(4): 1039-1065.doi: 10.1080/01431160903505310.

Fang, H., S. Liang, G. Hoogenboom, J. Teasdale, and M. Cavigelli, 2008. Crop yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing, 29(10): 3011-3032. doi: 10.1080/01431160701408386.

For more information, contact

Hongliang FANG, Ph.D, Professor

LREIS, Institute of Geographic Sciences and Natural Resources Research

Chinese Academy of Sciences

11A Datun Road, Beijing, 100101, China

Tel: +86 10 6488 8005

Fax: +86 10 6488 9630

Email: fanghl@lreis.ac.cn