A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation

Satellite remote sensing has been increasingly employed for the estimation of ground-level atmospheric PM2.5. There have been several cross-validation (CV) approaches applied for the validation of satellite-based PM2.5 estimation models. However, these validation approaches often lead to confusion, due to the unclear applicable conditions. For this, we fully analyze and assess the existing validation approaches, and provide suggestions on applicable conditions for them. Furthermore, the existing validation approaches still have limitations to disregard the uneven distribution of ground stations, and tend to overestimate the performance of the PM2.5 estimation models. To this end, a CV-based validation approach considering the uneven spatial distribution of monitoring stations (denoted as SDCV) is proposed. SDCV introduces the spatial distance between validation station and modeling station into the CV process, and evaluates the spatial performance through a strategy of excluding modeling stations within a specific distance. Meanwhile, this approach has designed reasonable evaluation indices for the model validation. Taking China as a case study, the results indicate that SDCV can yield a more complete and effective evaluation for the popular PM2.5 estimation models than the traditional validation approaches.