Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data

文献类型： 外文期刊

第一作者： Ren, Jianqiang

作者： Ren, Jianqiang;Chen, Zhongxin;Zhou, Qingbo;Tang, Huajun;Ren, Jianqiang;Chen, Zhongxin;Zhou, Qingbo;Tang, Huajun;Li, Su

作者机构：

关键词： remote sensing;yield estimation;winter wheat;biomass;NPP;PAR;fPAR;TOMS;MODIS

期刊名称：IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET

ISSN： 2153-6996

年卷期： 2007 年

页码：

收录情况： SCI

摘要： Crop yield data is a key indicator for national food security and sustainable development of society. Winter wheat is one of the most important main crops and Huanghuaihai Plain is the most important productive region in North China. So, the authors had a research on regional yield prediction for winter wheat based on crop biomass estimation using multi-source data in the Plain. In this paper, the quantitative relationship between crop biomass and yield was mainly used to estimate crop yield . The total crop biomass is cumulative biomass in the critical crop growth stages which are significant to final yield. Crop biomass was calculated by net primary production (NPP) model. Then NPP was converted into crop biomass considering factors such as carbon content of winter wheat, harvest index (HI), water content of grain, etc. When NPP was calculated, the model: NPP =epsilon * fPAR * PAR was used. The photosynthetically active radiation (PAR, 400-700nm) was calculated using ultraviolet (UV) reflectance data at 370 nm which came from the Total Ozone Mapping Spectrometer (TOMS). The fraction of photosynthetically active radiation (fPAR) could be retrieved from MODIS NDVI imagery using linear regression between WAR and NDVI. The biological conversion efficiency of dry matter (epsilon) was calculated depending on relationship between r and its affecting factors such as average temperature, rainfall and relative moisture of soil. Finally, the method and established relationships were applied in a larger region. Through validation, the absolute mean error of estimated yield was 232.7 kg.ha(-1) and relative error of estimated yield was 4.28%. Judged from the results of validation, the method based on crop biomass estimation using multi-source data was effective and practical in yield estimation of winter wheat in a larger region.

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