Yield prediction is important for agricultural management, food security warning and food trade policy. Remote sensing has been a useful tool for predicting crop yields. In this study, a modified daily process-based ecosystem model (the Boreal Ecosystem Productivity Simulator) is employed
in conjunction with land cover and leaf area index (LAI) products from MODIS to predict summer grain crop yields in the northern area of the Yangtze River in the Jiangsu Province, China. The model was driven by soil texture, land cover, daily meteorological and MODIS LAI data for 2004-2006.
Simulated growing season net primary productivity (NPP) of summer grain crops (November-May) and census data of crop yields in 2004 were used to derive the county-level harvest index, which is then used in conjunction with simulated NPP to predict crop yields in 2005 and 2006. The model captures
89 % and 88 % of variations in crop yields at county-level compared with census data in 2005 and 2006, respectively. The root mean square errors are 265 and 277 kg ha-1 in these two years. The results show the usefulness of a process-based model driven by remote sensing in predicting
crop yields. In such predictions, the considerable spatial variability of the harvest index should be taken into consideration.
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Document Type: Research Article
International Institute of Earth System Science, Nanjing University, Nanjing, Jiangsu, China
Meteorological Observatory of Jiangsu Province, Nanjing, Jiangsu, China, 210008
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, China
Department of Geography, University of Toronto, Toronto, Ontario, Canada
Publication date: 2010-02-01
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