Accurate crop growth monitoring and yield forecasting are significant to food security and sustainable development of agriculture. However, regional crop growth simulation faces the difficulties in determining the spatial distribution of some model parameters and initial conditions. In this study, regional biomasses at turn-green stage of winter wheat were re-estimated by linking WOFOST model and Soil Adjusted Vegetation Index (SAVI) synthesized from remote sensing data. Moreover, we proposed a way of combining evapotranspiration derived from satellite remote sensing data to crop grow simulation model. Thus, the regional initial available soil water and irrigation at earring stage were re-initialized and re-estimated by using remote sensing data. Those methods were well applied to simulate the growth and development for winter wheat at local site. After regionalizing of weather data, crop model parameters and initial conditions, those methods were used to estimate winter wheat yields in North China during the growing season from 2001 to 2002 at the scale of 0.25 degrees. The results showed that both soil water and final winter wheat yields estimation were improved and the relative root mean square error (RRMSE) decreased from 0.63 without remote sensing data to 0.20 with remote sensing data for 32 sites. The relative errors of the aggregated yields for three provinces were -4.9%, 4.3% and 8.6%, respectively. These results illustrated that remote sensing data can be used to improve winter wheat yields simulation at regional scale.

Date Published: 27 September 2006
PDF: 12 pages
Proc. SPIE 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III, 62980P (27 September 2006); doi: 10.1117/12.678232

Published in SPIE Proceedings Vol. 6298:
Remote Sensing and Modeling of Ecosystems for Sustainability III
Wei Gao; Susan L. Ustin, Editor(s)