结合形态和营养指标的小麦长势遥感监测方法

Monitoring crop growth condition timely and accurately is import for field management strategy design to obtain good quality and high yield. Most of the current researches on crop growth monitoring pay much attention on the morphological indices e.g. leaf area index, which cannot meet the requirements of agriculture production. Our project aims to develop a new crop growth monitoring method by integrating with the morphological and nutritional indices based on the new launched satellites. The main contents include: 1) analyzing the spectral characteristics of morphological and nutritional indices under different wheat growth conditions by conducting theoretical simulation and field experiments, to understand the mechanisms of growth monitoring with new growth index integrated with leaf area index and leaf chlorophyll content; 2) based on the analysis of the monitoring mechanisms with the new index, extracting spectral features and constructing spectral index to monitor the new growth index; 3) fully exploring the prior knowledge of new satellites and field measurements to optimize the spectral index and develop optimal model for the new index estimation, and then accurately monitor the wheat growth with satellite remote sensing observations. Our research will not only provide references for the application of remote sensing in the research field of agriculture, but also help to guide agricultural production and ensure food security.

及时准确地监测作物长势是肥水管理的重要依据和获得高产优质的关键。现有主要倚重叶面积指数等形态指标的遥感监测方法已不能满足作物长势高精度监测需求。本项目拟基于国内外新发卫星优势，研究小麦形态和营养指标相结合的长势遥感监测方法。主要内容包括：1）通过理论和观测实验，分析小麦在不同肥水条件下长势状况差异及光谱响应特征，阐明能够有效融合叶面积指数（形态指标）和叶绿素含量（营养指标）的新型综合长势指标的光谱探测机理；2）基于地面实测高光谱数据，分析提取新型指标的敏感光谱特征和时相特征，并采用代数组合方式综合各敏感特征，构建新型指标遥感监测的光谱指数；3）针对新发卫星传感器特性优化光谱指数，并结合地表先验知识构建新型综合长势指标的新发卫星遥感定量反演模型，提高小麦长势遥感监测精度。研究成果可为农业遥感技术的深入应用提供有效的理论和方法支撑，对指导农业生产以切实保障国家粮食安全具有重要意义。

当前作物长势监测主要倚重叶面积指数等形态指标，无法满足农业生产对作物形态结构和营养状况同时高精度监测的应用需求。本项目通过开展小区肥水控制实验和区域星地同步观测实验，以小麦为研究对象，开展了以下研究：1）在不同长势的农田场景中，分析了形态指标叶面积指数和营养指标叶绿素含量在光谱维和时间维的变化信息，通过筛选敏感波段，提取其时序变化特征，阐明了两类指标随时间的光谱响应规律；2）在冠层尺度上，综合光谱和时相特征构建叶面积指数和叶绿素含量与光谱数据之间的定量关系，不断优化模型实现了基于地面高光谱的形态和营养指标的高精度时序反演；3）在区域尺度上，筛选适用于Sentinel卫星和EnMAP卫星的最优光谱特征，并结合时序分析方法提取时相变化信息，建立了基于卫星多光谱和高光谱的长势指标定量反演模型；4）综合形态和营养指标开展了小麦长势的定量监测，并对小麦受氮胁迫和病害胁迫下的长势信息进行了时空分布提取，将两类指标相结合的长势监测方法应用至正常管理和受胁迫的小麦农田场景中。研究成果对实现大面积作物长势定量监测，以指导田间肥水管理进而保障农业生产和粮食安全具有重要意义。