基于随机森林的高分辨率 PM2.5 遥感反演 ——以广东省为例
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深圳市科技创新委员会基础研究项目(JCYJ20150831194441446)

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High Resolution PM2.5 Estimation Using Remote Sensing Data Based on Random Forest— a Case Study of Guangdong, China
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    摘要:

    细颗粒物(PM2.5)监测是大气污染治理的重要手段,受限于地面观测点的数量,从遥感反演 PM2.5 是常规地面观测的有效补充,是当前的研究热点。通常遥感反演 PM2.5 的思路是先反演大气气溶胶光学厚度,然后基于统计关系由大气气溶胶光学厚度反演 PM2.5。该方法容易造成误差传递,从而 导致反演模型的不稳定。该文提出了一种基于随机森林算法(一种机器学习算法)的 PM2.5 遥感反演方法,直接建立中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)影像与地 面实测 PM2.5 的关系,可以避免传统反演 PM2.5 时先反演大气气溶胶光学厚度带来的误差,最终得到精度更高的 PM2.5 反演结果。该方法先用随机森林算法对 MODIS 影像和经过克里金插值后的地面监测站PM2.5 数据进行训练和测试;然后,根据测试的均方根误差从多个模型中选取最优(均方根误差最小)的模型;最后,将此模型用于整幅 MODIS 影像,得到整个区域的 PM2.5 反演结果。实验选取了广东省 四个季节多幅 MODIS 影像数据进行验证,并通过决定系数和均方根误差两个表现指标进行对比和分析,验证了所提算法的优越性。

    Abstract:

    PM2.5 monitoring is an important means of air pollution control. Limited by the number of ground observation points, PM2.5 estimation from remote sensing data is an effective complement to conventional ground observation. The key idea of remote sensing estimation of PM2.5 is to retrieve aerosol optical depth firstly, and subsequently to reverse PM2.5 by aerosol optical depth based on the statistical relationship. This approach however is highly possible to cause error transmission, leading to instability of the inversion model. In this paper, we propose a PM2.5 remote sensing estimation method based on random forest algorithm to directly establish the relationship between moderate resolution imaging spectroradiometer (MODIS) image and ground measured PM2.5, so as to avoid the inversion error of atmospheric aerosol optical depth, finally obtain the PM2.5 estimation result with high precision. The method first uses random forest to train and test the MODIS image and ground monitoring station PM2.5 data after kriging interploation, and then selects the best model from multiple models according to the root mean square error (RMSE) of test index. Finally, the approach uses this model in the whole MODIS image to obtain the PM2.5 estimation result of the whole area. This experiment selects many MODIS image data from four seasons in Guangdong province to verify and compare the two performance indicators of R2 and RMSE. The results show that the proposed approach outperforms other approaches significantly.

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引文格式
申 原,陈朝亮,钱 静,等.基于随机森林的高分辨率 PM2.5 遥感反演 ——以广东省为例 [J].集成技术,2018,7(3):31-41

Citing format
SHEN Yuan, CHEN Chaoliang, QIAN Jing, et al. High Resolution PM2.5 Estimation Using Remote Sensing Data Based on Random Forest— a Case Study of Guangdong, China[J]. Journal of Integration Technology,2018,7(3):31-41

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  • 在线发布日期: 2018-05-18
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