一种基于差值指数的颗粒物PM2.5浓度反演新方法

从分析对颗粒物PM_2.5敏感的光谱特性入手，提出了一种基于差值指数的颗粒物PM_2.5浓度反演新方法。使用Avafield-1光谱仪(测量范围300～1 100 nm)测量了典型地物植被、土壤在不同的颗粒物PM_2.5污染条件下的光谱曲线，发现颗粒物使得植被和裸土的光谱曲线在红光波段反射率增加，在近红外波段反射率下降，且其变化量较为稳定，因此使用对颗粒物敏感的红光波段和近红外波段，构建差值指数(difference index，DI)以表征颗粒物PM_2.5的浓度变化。以北京市为研究区，选择TM影像，求取差值指数，结合北京市及周边地区地面空气质量监测站提供的PM_2.5逐时数据，反演了北京市的颗粒物PM_2.5的浓度。结果表明，2016年3月1日(监测站PM_2.5浓度均值为105.8)预测模型相关系数r为0.796，精度表现良好，12月14日(监测站PM_2.5均值为15.8)预测模型相关系数r为0.628，即颗粒物污染程度较低情况下，差值指数模型预测精度低于颗粒物污染程度较高的情况，分析原因是颗粒物浓度较低时，由颗粒物引起的地物光谱特征变化比较不明显，造成差值指数模型精度较低。由于空气质量为重度及严重污染时，获取的遥感影像质量较差，影响颗粒物浓度反演，因此该方法适合轻度、中度污染情况下的颗粒物PM_2.5浓度反演。该方法获取污染数据简单，反演结果空间分辨率较高(30m)，且可根据需要选取包含敏感波段数据的遥感影像用以获取不同时间、空间分辨率的颗粒物PM_2.5浓度分布，具有广泛的应用前景。

One New Method of PM2.5 Concentration Inversion Based on Difference Index

This paper proposes a new method of PM_2.5 concentration inversion based on difference index through analyzing the spectrum characterization which is sensitive to the particular PM_2.5. In addition, the spectral curves of typical land culture vegetation and soil are measured using Avafield-1 spectrometer (range of measurement 300～1 100 nm) under different PM_2.5 concentration. The result showsthat the PM_2.5 makes the reflectance of vegetation and soilincrease in red band and decrease in near-infrared. Therefore, the difference index (difference, index, DI) of sensitive red and near-infrared is used to characterize the particlesconcentration. This paper uses TM image to obtain the difference index and the PM_2.5 concentration inversion of Beijing with the measured dataprovided by the ground air quality monitoring station in Beijing and surrounding areas. The result of the fitting analysis shows that the accuracy (r=0.796) of DI model onMar 1st(average PM_2.5=105.8) is higher than that (r=0.628) on Dec14st (average PM_2.5=15.8), namelythe accuracy of the DI models is lower when haze pollution degree is weak because the spectral characteristics change caused by particles are not obvious with low particulate content. On the other hand, the quality of remote sensing image is poor when the haze pollution degree is too serious, so this method is suitable for particle concentration inversion under mild and moderate haze pollution. The new method can obtain high spatial resolution (30 m) result with very simple inversion process. In addition, this method can obtain the PM_2.5 concentration distribution of different temporal and spatial resolution just by selecting different remote sensing imagescontaining sensitive band data. The new method has wide application prospect.