摘要
PM_(2.5)是影响开封地区空气质量的首要污染物,利用卫星遥感手段可以快速获得PM_(2.5)浓度的空间分布。通过采用过境开封市的GF-1卫星数据,获取气溶胶光学厚度,结合地面PM_(2.5)监测数据与边界层高度、相对湿度和气温等辅助数据,采用多元线性回归,建立了基于GF-1的PM_(2.5)遥感反演模型。研究表明,2015年6—9月GF-1数据反演得到的PM_(2.5)浓度与地面监测结果较为接近,且有较高的相关性;加入地理加权回归能明显提高模型精度,较好地反映PM_(2.5)的空间分布;但在PM_(2.5)浓度较高时,该模型会出现低估现象。
PM_（2.5） is the key air pollution for air quality of Kaifeng City. With remote sensing technology,the distribution of PM_（2.5） concentration could be determined quickly. In this paper,the authors collected the aerosol optical depth（ AOD） of GF-1,height of planetary boundary layer（ HPBL）,relative humidity（ RH） and air temperature（ AT） over Kaifeng City and then,with multiple regression analysis,revised the coefficients of all variables. After that,the authors built the PM_（2.5） retrieving model from GF-1 in Kaifeng City. The validation from June to September in 2015 showed that the PM_（2.5） concentration from remote sensing was similar to that from four ground-level monitoring sites,and the correlation coefficient was higher than 0. 8. The result of geographically weighted regression（ GWR） was obviously better than that of no GWR. Nevertheless,when PM_（2.5） concentration was high,the model would underestimate PM_（2.5） concentration.
出处
《国土资源遥感》
CSCD
北大核心
2017年第4期161-165,共5页
Remote Sensing for Land & Resources
基金
国家自然科学基金项目"多角度标量信号辅助多角度偏振算法反演陆地气溶胶"(编号:41301358)资助
