Figure 4. Density scatter plots of global AOD retrievals derived from Landsat imagery against ground measurements over land from 2013 to 2022 using the (a) sample-based, (b) station-based, (c) month-based 10-fold cross-validation methods, and (d) the spatiotemporally independent validation method (i.e., using data samples from the years 2015 to 2020 for training and the remaining years for testing). The solid black line is the 1:1 line, and the dashed lines outline the EE envelope.

Spatiotemporal predictability

To further assess the model’s predictive ability in new spatiotemporal domains, we employ a variety of spatiotemporally independent validation methods. Initially, by isolating the spatial dependence through the independent control of each continent (withhold), our model demonstrates proficiency in predicting AOD values for various regions, where most of them yielded moderate correlations exceeding 0.5 and maintained low median biases within ± 2% (Table S5). Furthermore, approximately more than half (49%) to 92% of the spatial predictions aligned with the EEs for each region. Similarly, by independently controlling each year (withhold) to reduce temporal correlations, our model effectively captured AOD loads for the remaining years, achieving high correlations and minimal uncertainties (e.g., R = 0.80–0.92, MAE = 0.04–0.06, and RMSE = 0.07–0.11) compared with ground measurements (Table S6). Moreover, a significant proportion of the temporal predictions, at least 79% and 41%, met the EE and GCOS requirements, respectively.
We also constructed the model using data from intermediate years (2015–2020) and subsequently performed AOD retrievals and validation for the remaining years (2013, 2014, 2021, and 2022). Across the globe, AOD predictions exhibit notable accuracy, with moderate correlations exceeding 0.5 at more than 74% of the sites. Moreover, approximately 76% and 78% of the sites displayed low MAE and RMSE values less than 0.08 and 0.12, respectively (Figure 5). However, in regions with elevated AOD levels, such as Africa and Eastern China, sites were more prone to underestimation possibly due to the strong absorbing aerosols, showing higher RMSE and MAE values. Nonetheless, a substantial portion (61%) of the sites demonstrated nearly ”unbiased” estimates (within ± 2%). In addition, more than 76% and 60% of the sites had a significant proportion of the retrievals falling within the EE (> 70%) and GCOS (> 40%) envelopes. Regionally, the predictive performance was generally considerable, with relatively small biases (Table 2). The highest correlations were observed in Southern Africa and Eastern Asia (R = 0.895 and 0.882), while Europe and Oceania exhibited relatively lower correlations, attributable to historically clear air with low AOD loads. However, they had higher fractions of retrievals falling within the EE (> 80%) and GCOS (> 46%) envelopes. Globally, the predictive accuracy of our model is substantial, with the correlation between retrievals and observations reaching 0.826 and the median bias approaching zero (Figure 4d). The average MAE and RMSE values are 0.057 and 0.096, respectively. Overall, approximately 80.73% of the collocated points fall within the EE envelope, and approximately half (49.64%) of them meet the GCOS requirements. These findings highlight the strong adaptability and stability of the ART-GCT-GEE model, which can effectively predict both historical and future AOD levels worldwide.