碳捕捉与储存(carbon capture and storage,CCS)技术可以减少CO2气体排放,从而减缓全球气候变暖。但把CO2液化后进行地质封存具有泄漏的风险,如何大面积、快速、高效地监测CO2泄漏点是一个技术难题。该文通过野外模拟试验,以大豆为试验对象,研究了地下储存的CO2轻微泄漏对地表植被及其遥感特征的影响。大豆在2008年6月4日播种,自7月4日开始CO2气体以1L/min的速度持续注入土壤中,每天测量土壤中CO2体积分数(土壤中CO2气体占土壤中总气体体积含量的百分比)、每周测量1次大豆叶片的SPAD值、光谱数据。试验结果表明,当土壤中CO2体积分数小于15%时,对照(CK)与CO2泄漏胁迫大豆SPAD值无显著性差异(P>0.1),当土壤中CO2体积分数大于等于15%时,CK与CO2泄漏胁迫大豆SPAD值具有极显著性差异(P<0.001),随着胁迫进行大豆会早熟、落叶,甚至枯死。利用连续统去除法对大豆的光谱数据进行处理,发现随着土壤中CO2体积分数的增大,在绿光区的光谱反射率逐渐增大,而其他波段则无明显变化规律。根据CO2泄漏胁迫下大豆的光谱变化特征,设计采用面积植被指数Area(510～590nm)(510～590nm光谱曲线所包围的面积)识别遭受CO2泄漏胁迫的大豆。结果表明,当土壤中CO2体积分数大于等于15%时,Area(510～590nm)指数可以较好地识别出遭受胁迫的大豆,且具有较高的可区分性及稳定性,但当土壤中CO2体积分数小于15%时,该指数在整个生育期内无法准确识别出遭受胁迫的大豆。该研究结果对未来地表生态评估、高光谱遥感监测CO2泄漏点具有重要意义与应用价值。
以北京市为研究区域,联合使用光学遥感数据和雷达数据,对植被覆盖区地表土壤水分进行反演研究。在利用同期光学数据提取出归一化水分指数(normalized differential water index,NDWI)之后,利用water-cloud模型去除植被层在土壤水分后向散射中的贡献,然后考虑到地表粗糙度,在构建后向散射数据库的基础上分别利用HH和HV极化方式的后向散射系数构建土壤水分反演模型,并对反演结果进行对比研究。结果表明,采用HH极化方式反演土壤水分的均方根误差为0.044,相对误差为15.5%;采用HV极化方式反演土壤水分的均方根误差为0.057,相对误差为20.3%;相比而言,HH极化的反演效果更好。
Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.
WANG Hui-fangGUO weiWANG Ji-huaHUANG Wen-jiangGU Xiao-heDONG Ying-yingXUXin-gang
