基于高光谱特征分析的冬小麦冻害遥感监测方法研究

Freezing injury is one of the common natural disasters to winter wheat. Nowadays, rebuilding the ground surface temperature is mainly used for freezing injury remote sensing monitoring. This allows monitoring large area crop freezing injury. However, ground temperature is not the only factor occurred for crop freezing injury, some other factors may also affect such as the crop varieties, accumulated temperature, growth period, and etc. .Here, on the basis of appropriately combining several observation approaches: artificial ground observation, portable spectrometer observations, chlorophyll measurements and satellite remote sensing monitoring, our exploration tries to obtain an optimal band or index to identify and evaluate differences and degree of freezing injury, which will be achieved by analyzing the relationship between hyperspectral reflectivity, its countdown, logarithmic, first-order differential and chlorophyll content and obtaining the correlation between hyperspectral reflectivity and freezing injury degree. And the optimal method of remote sensing monitoring freezing injury will be build by analyzing the relationship between hyperspectral characteristics of the ground field scale and remote sensing image of spatial scales. Finally, based on the new proposed method, we will obtain freezing injury indices and rate winter wheat freezing injury grades. This allows us to quickly grasp the freezing damage degree and spatial distribution, adopt in time answer measure, it has important significance for wheat high yield, yield prediction. .To ensure the successful completion of the freezing injury test, during its implementation, the temperature control box test of potted winter wheat will also be carried, in case freezing injury does not occur during this three-year project period.

冻害是冬小麦常见的自然灾害之一，目前遥感监测冻害方法主要是反演地表温度，虽然可以实现大面积监测冻害，但是地面温度不是冻害发生的唯一因素，还受作物品种、积温、发育期等影响。本研究拟通过人工地面观测、便携式光谱仪观测、叶绿素测量和卫星遥感监测相结合，用冻害前后连续观测的高光谱反射曲线特征部位反射率及其倒数对数、一阶微分，与叶绿素含量进行分析；以高光谱反射率曲线不同特征部位值与冻害程度进行相关分析等，选择能表征冻害胁迫的特征值，建立识别和评价冻害差异及程度的波段或指数。通过分析地面田间尺度的高光谱特征与空间尺度的遥感影像之间的关系，建立遥感监测冻害方法。在此基础上建立冬小麦不同发育期冻害指标，对冬小麦冻害程度进行分级。能够在冻害发生后迅速掌握冻害发生程度和空间分布，及时采取应对措施，对小麦高产稳产、产量预测、政府决策等有重要意义。如项目进行前两年没有冻害发生，拟在项目第三年开始进行盆栽冻害试验。

河南省冬小麦越冬冻害主要是温度骤降型和冬季长寒型，从降温强度、降温频度和抗寒锻炼考虑，选取越冬期最大降温幅度、平均气温、负积温距平、极端最低气温、72小时日均温降温≥5℃次数和冬前平均气温等风险评价指标建立冬小麦冻害评价指数。基于1980-2011年河南省115个气象观测站的逐日气象观测数据，对河南省冬小麦越冬冻害进行了气候风险区划。为研究期间大田冻害观测点布设提供了依据。.本项目自2012-2013年度开始利用试验箱进行冬小麦冻害盆栽试验，共进行了3年。观测受冻前后叶片光谱反射率、叶绿素含量并分析其变化规律；通过对高光谱数据进行倒数对数、一阶导数、二阶导数变换，与叶绿素含量进行相关分析，寻找表征冻害胁迫的特征值，获得识别和评价冻害差异程度的波段或指数。结果显示以由蓝边面积（SDb）和红边面积（SDr）计算的VI2（VI2=SDr/SDb）或VI3[VI3=(SDr-SDb）/(SDr+SDb）]为冬小麦冻害监测的敏感指数。研究结果揭示了冬小麦冻害后高光谱特征，可为促进高光谱技术在冬小麦长势监测和估产中的应用，提高冬小麦冻害遥感监测的准确性提供依据。.基于盆栽试验得到冬小麦冻害农学指标，通过尺度转换获得冻害遥感指标。在冻害高光谱敏感指数和遥感监测指标研究基础上，以河南省林州市及其周边地区2012-2013年冻害过程为例，以冻害发生前后的两幅高光谱影像为研究对象，经预处理后，利用冬小麦冻害高光谱敏感指数的研究结果进行冬小麦冻害的高光谱遥感监测。在ArcGIS中根据敏感指数VI2和VI3的变化情况进行冬小麦冻害统计，结果显示，发生重度冻害的比例几乎一致，发生轻度冻害的比例相差2.11%。但从冻害分布图可以看到，两种指数监测的冻害分布情况有较大差异，拟在今后的冻害发生过程中对本研究结论的可靠性进行验证。但本研究结果表明利用HJ-1A 星HSI数据进行冬小麦冻害监测是可行的，为HJ-1A 星高光谱数据在农业遥感监测应用方面提供了参考。