DSSAT模型结合遥感数据同化和气象预报的冬小麦品质预报机理研究

Grain protein content (GPC) is an important factor for grain quality in wheat. Developing special and high-quality wheat is the only one way of achieving quality-efficiency in wheat industrialization. In current, most GPC prediction models by remote sensing are little mechanism and difficult to expand at interannual and regional scales. Therefore, the objective of this project is to use data assimilation method integrating remote sensing data with DSSAT crop growth model and weather forecasting method to achieve real-time GPC prediction. This project, based on the physiology of nitrogen absorption, nitrogen translocation, and grain protein synthesis, provides a good method to predict GPC in winter wheat at interannual and regional scales. The main work of this project are as follows: (1) integrating remote sensing data and DSSAT model by establishing best data assimilation scheme for GPC prediction; (2) determining weather data to evaluate the precision and accuracy of GPC model based on different forecasting nodes and determining the optimal weather data; (3) real time forecasting GPC in winter wheat by combining data assimilation method of remote sensing data with forecasting weather data into DSSAT model. The expected outcome of this project will be helpful to improve and deepen the theory in remote sensing prediction of winter wheat GPC, and have important significance in optimizing management, grading harvest, hierarchical storage and future trading.

籽粒蛋白质含量是反映小麦品质的重要因素，发展优质专用小麦是实现小麦产业化提质增效的必经之路。当前籽粒蛋白质遥感预测模型机理性不强，并且存在年际扩展和区域扩展性差的问题。因此，本研究拟以DSSAT作物生长模型为基础，依据氮素吸收运转及籽粒蛋白质合成原理，结合遥感数据同化与气象预报方法，开展冬小麦籽粒蛋白质含量动态预报机理研究，为蛋白质含量遥感预报的时空扩展提供新思路。研究内容包括：(1) 针对籽粒蛋白质含量预测的遥感信息和DSSAT模型同化策略构建与优化；(2) 不同预报节点的冬小麦籽粒蛋白质含量预报模型构建与精度评价；(3) 基于遥感数据同化与气象预报的冬小麦籽粒蛋白质含量动态预报。研究结果将有助于完善和深化作物品质遥感预测理论，对于小麦生育期内调优栽培及收获期后收割分类、分级贮存和期货贸易等有重要的指导意义。

开展小麦籽粒蛋白质含量的产中预报研究，并发展优质专用小麦尤其以籽粒蛋白质含量为依据的小麦分级收购和优质优价是实现小麦高产优质，提质增效的必要途径。本研究拟以DSSAT作物生长模型为基础，依据氮素吸收运转及籽粒蛋白质合成原理，结合遥感数据同化与气象预报方法，开展冬小麦籽粒蛋白质含量动态预报机理研究。主要研究内容包括：（1）系统的开展了作物生长模型DSSAT模型的作物敏感性及本地化方法研究；（2）开展了基于作物生长模型耦合遥感数据和气象预报的冬小麦籽粒蛋白质预报方法研究；（3）综合遥感信息和环境因子的冬小麦籽粒蛋白质含量年际及区域预测，并实现京津冀地区及多年籽粒蛋白质含量预测及分级。按照项目研究计划和目标，本课题完成了上述各项研究任务并开展了广泛深入的国内外合作交流；基于研究成果，已发表学术论文12篇（其中SCI论文6篇，EI论文2篇）；撰写学术专著1部；申请授权发明专利2项；项目成果支持获得省部级奖励2项；晋升副高级职称2人，中级职称1人，培养硕士研究生7名。综上，本项目圆满完成了各项预期指标，具体信息详见后述。