作物生长模型和遥感数据同化的双尺度作物氮素预测方法研究

项目名称： 作物生长模型和遥感数据同化的双尺度作物氮素预测方法研究

项目编号： No.41471285

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 天文学、地球科学

项目作者： 赵春江

作者单位： 北京市农林科学院

项目金额： 83万元

中文摘要： 作作物氮素状况是进行田间管理和产量品质预报的重要指标。田间调查取样分析法和遥感反演方法均难以实现作物氮素状况的连续动态监测与预测。项目拟以冬小麦为研究对象，基于田间实验观测，借助作物生长模型和遥感同化手段，开展叶片尺度氮含量和群体尺度氮素累积量（即双尺度氮素）的连续动态监测与预测研究。主要内容包括：① 本地化DSSAT-CERES作物模型参数，特别是氮素模块参数的校正，利用作物生长模型开展作物双尺度氮素水平的精确模拟研究；② 综合利用光谱信息和农学知识模型，构建作物双尺度氮素指标的遥感精确反演模型；③ 分别采用以叶片尺度氮含量、群体尺度氮素累积量及双尺度氮素指标共同作为状态变量等3种同化方式，开展作物模型和遥感氮素同化研究；④对所构建的模型方法进行多站点验证，定量评价其稳定性和实际效用。研究成果将进一步丰富作物氮素状况监测方法体系，实现作物氮素状况的连续动态监测与预测，提高其实际应用价值。

中文关键词： 作物生长模型；高光谱遥感；数据同化；氮素营养

英文摘要： Crop nitrogen status is an important indicator in field management and crop yield and quality prediction. Difficulties exist in crop nitrogen dynamic monitoring and prediction by conventional methods with destructive sampling and expensive laboratory analysis and by remote sensing with instantaneous satellite imageries. The objective of the project is to achieve the dynamic monitoring and prediction of two-scale nitrogen status (i.e. leaf nitrogen content for leaf scale and crop nitrogen accumulation for canopy scale) based on assimilation of DSSAT-CERES-Wheat model and remotely sensed data. With field experiments of winter wheat, the main work of this study are as follows: 1) localizing the parameters of DSSAT-CERES model, especially those of the nitrogen simulation module, and then carrying out the two-scale nitrogen status simulation with the crop model; 2) constructing the inversion models of two-scale nitrogen by combining crop spectral information and the agronomy knowledge and models; 3) establishing the approaches for three assimilation ways of crop model and remotely sensed nitrogen data, which using the leaf nitrogen content for leaf scale, the crop nitrogen accumulation for canopy scale and both the two-scale nitrogen parameters respectively as the assimilation state variables; 4) validating the above proposed models and approaches at different sites and quantitatively assessing their stability and performances. The expected outcomes of this project will further enrich the monitoring method system of crop nitrogen status, which will be useful in carrying out the dynamic monitoring and prediction of crop nitrogen status and improving its practical application value.

英文关键词： crop growth model;hyper-spectral remote sensing;data assimilation;nitrogen nutrition