作物籽粒蛋白质含量遥感监测预报研究进展  

Remote sensing of grain protein content in cereal:a review

作　　者：李振海[1,2] 杨贵军[1,2] 王纪华 徐新刚[1,2] 宋晓宇[1,2] Li Zhenhai;Yang Guijun;Wang Jihua;Xu Xingang;Song Xiaoyu(Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097;Beijing Research Center for Agri-Food Testing and Farmland Monitoring, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097)

机构地区：[1]北京农业信息技术研究中心,北京100097 [2]国家农业信息化工程技术研究中心,北京100097 [3]北京农业质量标准与检测技术研究中心,北京100097

出　　处：《中国农业信息》2018年第1期46-54,共9页China Agricultural Informatics

基　　金：国家自然科学基金项目"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"(41701375);国家自然科学基金国际(地区)合作与交流项目"多平台高光谱遥感信息融合的作物养分精准诊断决策"(61661136003);国家重点研发计划"小麦品质卫星遥感监测与预测"(2016YFD0300603-5)

摘　　要：【目的】梳理目前作物资料蛋白质含量遥感监测预报研究进展,掌握最新该方面的研究方法、技术等。为发展优质专用谷物并依据以蛋白质含量为主导的不同类型谷物分类收获和加工探明发展道路。【方法】通过收集国内外籽粒蛋白质含量遥感监测预测研究文献,整理、分析及归纳当前研究内容,综述前人研究等方法。【结果】概述了3种常规的作物籽粒蛋白质含量检测方法,包括常规的室内分析化学法、近红外分析方法及遥感技术预测方法;介绍了植物碳氮代谢过程与籽粒蛋白质含量形成机理以及作物籽粒蛋白质遥感预测的可行性;然后归纳了4类作物籽粒蛋白质含量遥感监测预测等方法,分别为基于‘遥感信息—籽粒蛋白质含量’模式的经验模型、基于‘遥感信息—农学参数—籽粒蛋白质含量’模式的定量模型、基于遥感数据和生态因子的籽粒蛋白质含量半机理模型、基于遥感信息和作物生长模型结合的机理解释模型,并分别综述了这4类预测模型的国内外研究进展。【结论】明确了当前在籽粒蛋白质含量遥感预测中存在的问题及进一步解决的对策。[ Purpose ] Developing special and high-quality wheat is the only way to achieve quality-efficiency in wheat industrialization. Grain protein content （ GPC ） is an important factor for grain quality in wheat. Remote sensing technology with advantages of instantaneous and spatial continuity has become an effective approach to estimating crop quality across wide regions. [ Method ] In this study, GPC test methods, including lab analytical chemical method, near-infrared analytical method and remote sensed predicting method, were introduced first. [ Result ] The feasibility of GPC prediction by remote sensing was studied based on the nitrogen translocation theory, the mechanism of grain protein formation and relationship among canopy biophysical and biochemical parameters and spectral features. Remote sensing prediction of cereal GPC was emphatically reviewed from four inductive methods, including the remote sensing to GPC method （ RS-GPC ）, the RS to agricultural variables then to GPC method （ RS-AgriVar-GPC ）, Semi-physical method and physical method. [ Conclusion ] Finally, the difficulties of cereal GPC monitoring and predicting were analyzed, and the possible solutions were discussed.

关 键 词：作物 籽粒蛋白质含量 遥感 监测预报 

分 类 号：S127[农业科学—农业基础科学] S312