Ocean College of Minjiang University, Fuzhou, 350118, China.
College of Environment and Resources, Institute of Remote Sensing Information Engineering, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350116, China.
J Environ Manage. 2020 Oct 15;272:111061. doi: 10.1016/j.jenvman.2020.111061. Epub 2020 Jul 13.
Previous studies that have used remote sensing data to estimate the PM concentrations mainly focused on the retrieval of aerosol optical depth (AOD) with moderate-to-low spatial resolution. However, the complex process of retrieving AOD from satellite Top-of-Atmosphere (TOA) reflectance always generates the missingness of AOD values due to the limitation of AOD retrieval algorithms. This study validated the possibility of using satellite TOA reflectance for estimating PM concentrations, rather than using conventional AOD products retrieved from remote sensing imageries. Given that the TOA-PM relationship cannot be accurately expressed by simple linear correlation, we developed a random forest model that integrated satellite TOA reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B product, meteorological fields and land-use variables to estimate the ground-level PM concentrations. The highly-polluted Yangtze River Delta (YRD) region of eastern China was employed as our study case. The results showed that our model performed well with a site-based and a time-based CV R of 0.92 and 0.88, respectively. The derived annual and seasonal distributions of PM concentrations exhibited high PM values in northern YRD region (i.e., Jiangsu province) and relatively low values in southern region (i.e., Zhejiang province), which shared a similar distribution trend with the observed PM concentrations. This study demonstrated the outstanding performance of random forest model using satellite TOA reflectance, and also provided an effective method for remotely sensed PM estimation in regions where AOD retrievals are unavailable.
先前使用遥感数据估算 PM 浓度的研究主要集中在利用中等至低空间分辨率来反演气溶胶光学厚度 (AOD)。然而，从卫星顶空气体 (TOA) 反射率反演 AOD 的复杂过程由于 AOD 反演算法的限制，总会导致 AOD 值的缺失。本研究验证了使用卫星 TOA 反射率估算 PM 浓度的可能性，而不是使用传统的从遥感成像中提取的 AOD 产品。鉴于 TOA-PM 关系不能用简单的线性相关来准确表示，我们开发了一个随机森林模型，该模型集成了中分辨率成像光谱仪 (MODIS) Level 1B 产品的卫星 TOA 反射率、气象场和土地利用变量，以估算地面 PM 浓度。中国东部高度污染的长江三角洲 (YRD) 地区被用作我们的研究案例。结果表明，我们的模型表现良好，基于站点和基于时间的 CV R 分别为 0.92 和 0.88。得出的 PM 浓度年际和季节性分布表现出北部 YRD 地区（即江苏省）的 PM 值较高，而南部地区（即浙江省）的 PM 值较低，与观测到的 PM 浓度分布趋势相似。本研究展示了使用卫星 TOA 反射率的随机森林模型的出色性能，也为在无法获取 AOD 反演的地区进行遥感 PM 估算提供了一种有效方法。
