Aerosols are complex compounds that greatly affect the global radiation
balance and climate system and even human health; in addition, aerosols are
currently a large source of uncertainty in the numerical simulation process.
The arid and semi-arid areas have fragile ecosystems with abundant dust but
lack related high-accuracy aerosol data. To solve these problems, we use the
bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data
and multiple environmental covariates, to produce a monthly
advanced-performance, full-coverage, and high-resolution (250 m) AOD product
(named FEC AOD, fusing environmental covariates AOD) covering the arid and
semi-arid areas. Then, based on the FEC AOD products, we analyzed the
spatiotemporal AOD pattern and further discussed the interpretation of
environmental covariates to AOD. The results show that the bagging trees
ensemble model has a good performance, with its verification
Aerosols are a type of complex substance dispersed in the atmosphere that can be from natural or anthropogenic sources (Kaufman et al., 2002). Aerosols can affect the global radiation balance and climate system directly, indirectly, or semi-indirectly by absorbing or scattering solar radiation (Myhre et al., 2013). Concurrently, aerosols seriously endanger human health by mixing, reacting, and dispersing dangerous compounds (Chen et al., 2020; Lelieveld et al., 2019). As one of the most significant optical characteristics of aerosols, the aerosol optical depth (AOD) is the integral of the aerosol extinction coefficient in the vertical direction and indicates the attenuation impact of aerosols on solar energy (Chen et al., 2021a). The AOD is frequently adopted to depict air pollution and indirectly calculate various atmospheric parameters, such as particulate matter 2.5/10; in addition, the AOD is extensively applied in atmospheric environment-related research (Goldberg et al., 2019; He et al., 2020).
Generally, the primary AOD acquisition method is in situ observations, which have high precision. However, in situ observations are restricted by the distribution of observation stations, so the resulting data lack spatial continuity, making it difficult to use these data to meet the objectives of growing regional atmospheric environmental studies (Zhang et al., 2019). Remote sensing (RS) is an effective tool for collecting AOD information over a wide range of spatial scales, significantly offsetting the deficiencies of in situ observations. RS can tackle difficulties connected to insufficient data and uneven geographical distributions to a certain extent (Chen et al., 2020). Nonetheless, RS is not always a silver bullet for acquiring AOD, as RS methods have some problems, such as low spatial resolutions and missing data in some situations (Li et al., 2020). The commonly utilized AOD satellite products derived from various sensors have different emphases in their uses (Table S1). However, the common point is that the spatial resolution of these data is coarse, and the products even contain large numbers of no-data values (Chen et al., 2022; Sun et al., 2021; Chen et al., 2021a; Wei et al., 2021). All these issues restrict the application of satellite AOD products on regional scales and especially on the local scale. Furthermore, the AOD spatial resolution scale often inevitably affects subsequent atmospheric pollutant predictions (Yang and Hu, 2018). These issues not only affect AOD analyses but also mislead numerous pertinent uses of AOD data.
Although methods for resolving AOD RS data deficiencies have been studied, previous research has not addressed this problem completely (Li et al., 2020; Zhao et al., 2019). Considerable related work has concentrated on multisource AOD dataset fusion or AOD gap-filling methods using different models. The initial and most extensively applied method is interpolation, but the AODs obtained in this way show high spatiotemporal variability; thus, this method is not suitable for application to anticipate missing AOD data (Singh et al., 2017). Another widely used method involves merging multiple AOD products; this method can improve the data quality but often fails to completely eliminate missing pixel values, even bringing about offsetting consequences (Bilal et al., 2017; Ali and Assiri, 2019; Wei et al., 2021). Some statistical models, such as linear regression and additive models, have also been employed to fill missing pixel values and improve the spatial resolutions of AOD products. However, the performances of these models are often dubious due to their simple structures (Xiao et al., 2017). Most current methods for obtaining high-resolution AOD forecasts are focused on individual model techniques and rely on a set of assumptions that are frequently not met, leading to inaccurate predictions (Li et al., 2017; Zhang et al., 2018). As computing technology advances, involving the training of multiple models by resampling the training data with the corresponding environmental covariates from their original distribution, ensemble machine learning methods provide new considerations and methods that are less constrained by the hypotheses of single models, with less overfitting and fewer outliers (Li et al., 2018). The strong data-mining ability of ensemble machine learning methods is also good for fitting multisource data, and these methods can achieve higher-precision results at the same time (Zhao et al., 2019). As a result, the present research attempts to adopt ensemble machine learning methods to explore the production of an advanced-performance, high-resolution, full-coverage AOD dataset covering arid and semi-arid areas.
Currently, many previous studies have focused on AOD research in various regions and on various scales, but these studies were concentrated on the eastern coastal areas and lacked related exploration in arid and semi-arid areas. Arid and semi-arid areas, as important components of the Earth's geographic units, have extremely fragile biosystems and are extremely sensitive to climate change and human activities (Huang et al., 2017). Due to the complex surface situation in arid and semi-arid areas, especially those with large desert areas, many AOD retrieval algorithms are not suitable for use in such regions. Although a minority of algorithms can acquire AODs in arid and semi-arid areas, such as the deep blue (DB) algorithm and multiangle implementation of atmospheric correction (MAIAC) algorithm, these algorithms are still limited by their coarse resolution, high uncertainty, or extensive missing-data phenomenon, so the resulting AOD products have difficulty meeting the needs of arid and semi-arid atmospheric environmental research (Wei et al., 2021). However, arid and semi-arid areas are crucial dust sources, with strong variability in the aspects of aerosol loading and optical characteristics. As typical dust sources and AOD data-scarce areas, the AOD variety in arid and semi-arid areas significantly influences global climate change and model simulations. Therefore, manufacturing an AOD dataset covering arid and semi-arid areas with increased quality is necessary for performing local and even global atmospheric environment research.
To better solve the issue associated with the lack of AOD data in arid and semi-arid areas, this research aims to acquire an advanced-performance, high-resolution, full-coverage AOD dataset that can serve as the foundation for future studies. To achieve this goal, the main work of this study includes the following steps: (1) based on the MAIAC AOD product combined with multiple environmental covariates and utilizing a machine learning method, the FEC AODs (fusing environmental covariates AODs) are obtained for the 2000–2019 period; (2) Aerosol Robotic Network (AERONET) ground observation data and the MCD19A2 and MxD04L2 AOD satellite products are collected to verify the applicability of the FEC AOD product; (3) the FEC AOD spatiotemporal patterns are analyzed; and (4) the dominant environmental covariates of the FEC AOD dataset are explored.
Figure 1 shows the arid and semi-arid areas in northwest China (73
Study area. The figure shows the typical arid and semi-arid areas
and AERONET site distributions; five provinces/autonomous regions in
northwest China. The five ecological zones were derived from © Google Earth (
The MAIAC AOD product, which is named MCD19A2, is based on the MODIS
instrument onboard Terra and Aqua in combination with the MAIAC algorithm.
The MAIAC algorithm is an advanced AOD retrieval method that uses
time-series analyses and image-based spatial processing to acquire AOD data
from densely vegetated areas and bright desert regions (Lyapustin et
al., 2018, 2011). The MAIAC AOD product's temporal and
spatial resolutions are 1 d and 1 km
MYD04L2 and MOD04L2 are the level-2 atmospheric aerosol products from Aqua
and Terra, respectively, and the spatial and temporal resolutions of these
products are 10 km
AERONET (Aerosol Robotic Network) is a network that monitors aerosols on the
ground, providing 0.340–1.060 m aerosol optical characteristics at a high
temporal resolution (15 min) (Holben et al., 1998). AERONET currently
includes more than 500 sites and covers the major regions of the world with
a long time series. The AERONET AOD data have a low uncertainty (0.01–0.02)
and are considered the highest accuracy AOD data available; these data are
widely used as a reference in RS AOD product validations (Almazroui, 2019).
In this study, data from a total of 12 AERONET sites in northwest China were
selected, most of which were from the third version of the level 2.0 AERONET
AOD, except for the Mt_WLG station data (Level 1.5) (Yan et
al., 2022; Giles et al., 2019). Related information about these AERONET
sites is available in Table S2 and Fig. 1. Satellite products provide
mainly 550 nm wavelength AODs, so the AERONET AOD at 550 nm was computed via
the Ångström exponent algorithm to better match the AODs observed by
the satellites (Ångström, 1964). In the temporal dimension, we
computed the average AERONET AODs over the Aqua and Terra overpass periods.
In the spatial dimension, we matched the satellite and in situ observed AODs
over a
The environmental covariates selected in this study comprised 12 covariates in three categories (meteorological parameters, surface properties, and terrain factors). The covariates were selected based on two criteria: first, each variable had to be considered important to the AOD and to have a vital influence on the AOD formation, accumulation, and migration processes, referring to existing research and expert experience (Zhao et al., 2019; Chen et al., 2020; Yan et al., 2022); and second, the data must be freely released to the public, meaning the datasets must be freely available on the national or global scale (Li et al., 2020). Detailed information on these covariates is listed in Table 1. In this study, we computed environmental variable datasets at two spatial resolutions (1 km and 250 m). The 1 km spatial resolution data were obtained with the aim of modeling with the MAIAC 1 km AOD, and the 250 m spatial resolution data were the target resolution of the FEC AODs. To normalize the covariables on this basis, we interpolated the geo-datasets to 1 km and 250 m spatial resolutions in ArcGIS (the bilinear method was used for the continuous covariates and the nearest neighbor method was used for classified covariates) and reprojected the results to the 1984 World Geodetic System (WGS) coordinates. The environmental covariates were divided into static and dynamic variables. Static variables were defined as those that did not change essentially with time, i.e., slowly changing factors. For dynamic covariates, the averaging method was adopted to obtain monthly average data. The static variables, similar to the baseline conditions, played an initial constraint role in the downscaling of the monthly AODs, while the dynamic variables played a more dynamic evolution role (Yan et al., 2022). Notably, the relevant operations are not limited to ArcGIS, and relevant open-source software such as QGIS could also be implemented.
The meteorological parameters included temperature, precipitation,
evapotranspiration, and wind speed. The temperature and precipitation data
were obtained from National Tibetan Plateau/Third Pole Environment Data
Center (TPDC) at temporal and spatial resolutions of 1 month and 1 km
To describe the surface properties, we employed the land use and land cover
(LUCC), normalized difference vegetation index (NDVI), and temperature
vegetation dryness index (TVDI). From the LUCC dataset, we selected the
median year of the whole study period, 2010, from Resource and Environment
Science and Data Center. The LUCC dataset was obtained by manual visual
interpretations of the Landsat series data as the data source. This dataset
included six categories (farmland, forest, grassland, water body, construction
land, and unused land) and 25 subcategories at a spatial resolution of 30 m.
LUCC data are often likely to indicate the intensity of human activity and
are closely related to aerosol emissions, transport, and dustfall (Fan et
al., 2020; Li et al., 2022). The NDVI data were obtained from the NASA
Global Inventory, Monitoring, and Modelling Studies (GIMMS) NDVI3g v1
product at temporal and spatial resolutions of 15 d and 0.083
The elevation data were collected from the Shuttle Radar Topography Mission (SRTM)
90 m digital elevation model (DEM). DEM is highly correlated with surface
pressure and always used to represent the dispersion condition of aerosols
(Xue et al., 2021; Fan et al., 2020). Based on elevation, geomorphology is
realized under Geographic Resource Analysis Support System extension named
r.geomorphon modular (Jasiewicz and Stepinski, 2013). Using the System for
Automated Geoscientific Analyses software (
Environmental covariates for AOD modeling.
Ensemble machine learning methods can be divided into two main categories according to whether dependency relations exist between learners: boosting and bagging (Fig. S1) (González et al., 2020). If there is a strong dependency between individual weak learners, and a series of individual weak learners needs to be generated serially (this means that the following weak learner is affected by the former weak learner), this is boosting. In contrast, if there is no dependency between individual weak learners, a series of individual learners can be generated in parallel (there is no constraint relationship between each learner), this is bagging. The typical representative and extensively used boosting and bagging algorithms are gradient boosting decision tree (GBDT) and random forest (RF), respectively (Zounemat-Kermani et al., 2021). Compared to boosting, bagging reduces the training difficulty and has a strong generalization ability.
Bagging (namely, bootstrap aggregating), as a simple but powerful ensemble algorithm to obtain an aggregated predictor, is more accurate than any single model (Breiman, 1996). Bagging uses multiple base learners or individual learners (such as decision trees, neural networks, and other basic learning algorithms) to construct a robust learner under certain combined strategies (Li et al., 2018). Generally, bagging algorithms include bootstrap resampling, decision tree growing, and out-of-bag error estimation steps. The main steps of bagging are as follows: (1) bootstrap resampling of a random sample (return sampling) under abundant individual weak learners; (2) model training based on the origin samples to train for abundant individual weak learners in accordance with the self-serving sample set; and (3) outputting the results based on the decision tree and calculating the average of all the regression results to obtain the final regression results. Therefore, bagging reduces the overfitting problem and prediction errors in decision trees and the variance, thereby significantly improving the accuracy of the results. Simultaneously, the influence of noise on the bagging algorithm is comparatively lower than those of other available machine learning algorithms for obtaining AODs (Liang et al., 2021).
In this study, we used 12 environmental covariates (1 km) as the downscaling
method (bagging tree ensemble algorithms) inputs to acquire an
AOD-environmental covariate (AODe) model at a 1 km resolution and utilized
the AODe model and 250 m environmental covariates to acquire the FEC AOD
product. Specifically, the basic idea for downscaling AODs with bagging
trees ensemble machine learning (ML) models is to train the relationships
between the MAIAC AODs and the auxiliary environmental variables at a coarse
resolution (1 km) using ML algorithms. We then applied the trained
relationships to generate a high-resolution FEC AOD product at a fine
resolution (250 m) (Duveiller et al., 2020; Yang et al., 2020; Ma et al.,
2017). In the case of lacking environmental covariates in some periods, we
used the multiyear monthly average to replace the missing values. The reason
why the 250 m target resolution was selected was that existing studies have
shown that the 250–500 m spatial-resolution scales are appropriate for
aerosol RS research and can optimally capture aerosol features (Wang et al.,
2021; Chen et al., 2020). Second, most high-resolution global product data
have a 250 m resolution, especially soil data, as this resolution is most
convenient for peer comparison and further research and application (De
Poggio et al., 2021; Hengl et al., 2017). The model was built monthly from
March 2000 to February 2020 to assure the model's accuracy in the inference
process, and the specific parameter set included 10 cross-validation folds,
the number of learners (
Flow chart of the experiment and model calculation process.
To verify the performance of the FEC AOD product over arid and semi-arid
areas, based on the AERONET AOD data as a reference, some generalized
parameters were chosen to assess the performance of the FEC AOD product,
such as the decision coefficient (
Comparisons of various products with the AERONET AODs:
The multiyear average spatial distributions of the FEC AODs, MAIAC AODs,
MOD04L2 AODs, and MYD04L2 AODs were calculated (Fig. 4). The AOD spatial
patterns exhibited high consistency among these products: high AODs were
located in the Taklimakan Desert and on the Loess Plateau, and low AODs were
distributed in high-elevation areas (such as mountainous zones and Qinghai
province). To further validate the FEC AOD performance, we calculated the
monthly, seasonal, and yearly average AODs from 2000 to 2019 (Figs. S2–S5).
In terms of the monthly scale (Fig. S2), we found that many high AOD
values appeared in March, April, and May, concentrated in and downwind of the
Taklimakan Desert. Generally, the FEC AODs, MAIAC AODs, MOD04L2 AODs, and
MYD04L2 AODs had similar monthly spatial distributions, especially the FEC
AODs and MAIAC AODs. The monthly correlations between the FEC and MAIAC AODs
were all above 0.78 in the study area, most of which were higher than 0.9
(
The multiyear spatial average AODs from 2000 to 2019:
Considering that the ability to capture long-term trends is an important
element for a dataset, we compared the FEC AOD, MAIAC AOD, MOD04L2 AOD, and
MYD04L2 AOD products to further validate the FEC AODs. From January to
December, the multiyear monthly averages of these four AOD products showed
similar change trends, increasing and decreasing alternately, and reaching
their lowest values in November (Fig. S6). Of course, we observed some
differences in the AOD magnitude and fluctuation range; these differences
were due mainly to the difference in AOD retrieval algorithms. To further
analyze the consistency and differences among the products, we also compared
the four products on monthly scale by removing the seasonal cycles (Fig. 5). First, the four AOD data products changed in a highly similar manner,
and the MxD04L2 AOD fluctuation range was significantly higher than those of
the FEC AOD and MAIAC AOD products. Notably, the FEC AOD and MAIAC AOD
products were substantially consistent, with an
The long-term change trends of four AOD products obtained by removing seasonal cycles.
Spatial patterns of AOD trends obtained by removing seasonal
cycles between 2000 and 2019:
As is well established, the effect of scale is a scientific problem in
remote sensing, so we further discussed the ability of the FEC AOD product
to describe relatively fine-spatial-resolution features. First, we created a
10 km
The long-term change trends of four AOD products over five ecological zones obtained by removing seasonal cycles.
Generally, the spatial patterns of the FEC AOD product were consistent among
different years (Fig. S5); the highest AODs were found in the southern
area of Xinjiang Uyghur Autonomous Region of China (hereafter referred to as
Xinjiang) and the center of Shaanxi province, mainly due to the special
meteorological conditions, unique topography, and surface coverage of these
regions. The AODs were low in other areas, especially in southern Qinghai province. The multiyear mean AOD was
Temporal information entropy (TIE) and time-series information entropy (TSIE) of the AOD distribution.
The FEC AOD product, with its high spatial resolution and full coverage over arid and semi-arid areas, provides new possible data sources for further fine-scale research on air pollution in areas with scarce data. Based on the FEC AOD product, we explored the regional distribution characteristics of AODs under different areas and surface coverage types. Figure 9 shows that the AODs in Gansu province were highest in all months, while the AODs in Qinghai province were lowest. From January to December, almost all AODs showed a trend of first increasing and then decreasing, peaking in March and April. Except for Gansu province, where the AODs were bimodal, the other provinces/autonomous regions exhibit unimodal AODs. Figure 10 describes the seasonal AOD distribution under seven different land cover types (forest, grassland, water body, ice and snow, construction land, unused land, and farmland). The AODs over ice and snow were the smallest and continuously decreased from spring to winter. The AODs were at high levels over farmland and construction land, mainly due to human activities. Regardless of the land cover type, the springtime AODs were always highest. Except for ice and snow and unused land, the seasonal AOD distributions were similar among land cover types, first decreasing and then increasing, and autumn had the lowest AOD values.
The monthly AOD distribution characteristics in different provinces/autonomous regions. The error bars represent the standard errors.
The seasonal AOD distributions over different land cover types. The error bars represent the standard errors.
To examine the contributions of environmental covariates to the FEC AOD
dynamics, redundancy analysis (RDA) was used to explore the association
between different seasons in the FEC AOD product and environmental
covariates. The 12 environmental covariates were divided into three
groups: meteorological parameters, surface properties, and terrain factors.
The variance proportions driving the FEC AOD variations on different
temporal scales were tested from the environmental covariate groups. The
variation in the FEC AODs can be interpreted by every group of environmental
covariates individually or using the combined variation owing to a set of
two or more covariates, and the residual represents the unexplained
proportion. The variance partitioning results can be described as Venn
diagrams constructing in the R language (Waits et al., 2018). From Table 2
and Fig. 11, the variation partitioning analysis reveals that the
meteorological factors still explain a maximal proportion of FEC AOD
variance on different temporal scales, followed by the terrain factors, and
the surface properties explain the smallest variation; the average
contributions of these categories were 77.1 %, 59.1 %, and 50.4 %,
respectively. In different seasons, the environmental covariates have
different abilities to explain the FEC AODs, and the following order was
obtained: winter (86.6 %)
Three groups of environmental covariates for AOD variation partitioning.
Seasonal variation partitioning and average AODs explained in the
following seasons:
This study, based on the MAIAC AOD product and 12 environmental covariates
data, adopts the bagging tree ensemble approach to produce a monthly
advanced-performance, full-coverage, and high-resolution FEC AOD product in
northwest China. The bagging tree ensemble approach has a strong advantage
in feature modeling and prediction, but some problems also exist; for
example, most base learners are black boxes, meaning that their explanation
capacities are limited (Zounemat-Kermani et al., 2021). Concurrently, the
model uncertainty is also an issue to be considered, possibly arising from
the setting of hyperparameters and base learners and the sample number
selection (Khaledian and Miller, 2020). Therefore, the model robustness is
critical for modeling and predicting. Simultaneously, providing mapping
uncertainty information helps users better understand the quality of the FEC
AOD product in different regions, thus further promoting users' reasonable
use of the AOD product. To check the reliability and stability of the
simulated AOD model and consider the computing efficiency simultaneously,
data representing 1 month were randomly selected (August 2010), and we
conducted a 100-iteration, 10-fold cross-validation; that is, we obtained
100 predictions for each pixel and averaged these 100 predictions to obtain
the final prediction result (Rodriguez et al., 2010; Wei et al., 2021; Zhang
et al., 2021; Ma et al., 2022). Then, we calculated the model uncertainty,
specifically by using the standard deviation and upper and lower limits of
the 95 % confidence interval (Sect. S1). Following the 100 experiments, the
validated
Distributions of the mean values and uncertainties in the AOD prediction model.
The average uncertainties corresponding to different AOD levels. The light-colored area surrounded by the black line denotes the AOD percentage and the histogram bars reflect the uncertainty.
The spatial patterns of four AOD products in April and October 2010:
The spatial patterns between the FEC AOD product and other AOD products in April 2010 over Urumqi.
The bagging tree ensemble method performance is generally affected by the selection of environmental covariates (Khaledian and Miller, 2020). The prediction accuracy is dependent on input variables, with underpinning static variables, and meteorological factors (dynamic variables) explain most of the AOD variation (Yan et al., 2022). Despite our selection of 12 environmental covariates that could explain most of the AOD variations, approximately 13.4 %–27.5 % of the results could not be well explained, and differences in the interpretation of the environmental covariates existed. Therefore, there is much room for improvement in the optimization of environmental covariates. There is no doubt that the meteorological parameters are the most significant contributors, as the temperature, precipitation, evapotranspiration, and wind speed effectively influence the AOD through direct or indirect interactions in the air (Chen et al., 2020). At the same time, the effect of terrain factors cannot be ignored, as these factors affect the propagation, diffusion, and settlement of the AOD. The surface factors involving the surface cover and soil wetness affect the dust generation and reduction processes. Additionally, some other questions also warrant further research, such as surface properties performance to explain AOD in summer lower spring and examining why the terrain factors have a higher AOD variance analytical power in autumn and winter compared to in spring and summer. We preliminarily speculate that this may be related to multifactor interactions, but this topic needs further analysis. In the following research, we consider introducing more related environmental covariates to try to improve the prediction accuracy. In addition, we plan to further explore the internal correlations among various covariates and the relative contributions of individual covariates to the AOD. Of course, the high spatial resolution and accuracy of the environmental covariates must also be taken into consideration (by adding or replacing data as necessary).
Spatial heterogeneity, as the second law of geography, is the source of the
scale effects. As a result, the richness of feature information varies in
accordance with spatial scales in remote sensing data; in most cases,
certain patterns are found only at specific scales (Miller et al., 2015).
Complex underlying surfaces are often accompanied by strong spatial
heterogeneity and scale effects, which bring great challenges to
high-spatial-resolution remote sensing observations and product generation.
In this research, the FEC AOD product, which is generated by the way in
which MAIAC AOD is constrained by combining dynamic and static variables,
was consistent with the MAAC AOD product overall. Specifically, the monthly
correlations were all above 0.78 in the study area, and most were higher
than 0.9 (
This monthly advanced-performance, full-coverage, high-resolution AOD
dataset (FEC AOD) constructed over northwest China in this study is freely
available via
In this paper, a monthly advanced-performance, full-coverage, high-resolution AOD dataset was produced based on the MAIAC AOD product and multiple environmental covariates and utilizing a machine learning method from 2000 to 2019 in the northwest region of China. AERONET and MODIS AOD data were collected to verify the applicability of the FEC AOD product. Then, the spatiotemporal changes reflected in FEC AOD product are analyzed, and an interpretation of the contributions of environmental covariates to the FEC AOD product is explored. The results show that the FEC AOD effectively compensates for the deficiency and constraints of in situ observation and satellite AOD products. Moreover, the FEC AOD product demonstrates a reliable performance and ability to capture local information and long-term trends, even superior to the abilities of the MAIAC and MxD04L2 AOD products; these findings also indicate the necessity of high-spatial-resolution AOD data. The spatial patterns are consistent among different years and greatly differ at the seasonal level. The higher the AOD is, the stronger the temporal variability is. The AODs exhibit a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert between 2000 and 2019. Farmland and construction land have high AOD levels compared to other land cover types. The meteorological factors demonstrated the maximum interpretation ability of AODs on all analyzed temporal scales, while the capability of environmental covariates to explain AODs varies seasonally.
The supplement related to this article is available online at:
XCh designed and developed the methodology and software, conducted the analysis and validation, and wrote the paper. HZ supported and supervised the study. ZiZ developed the methodology and reviewed the paper. XCa and JD investigated and developed the methodology. CZ and ZhZ performed the conceptualization of and investigations in this study. JW supported and supervised the study and reviewed the paper.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors are grateful to the Atmosphere Archive and Distribution System (
This research has been supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP; grant no. 2019QZKK0103), Basic Research Program of Shenzhen (grant no. 20220811173316001), Guangdong Basic and Applied Basic Research Foundation (grant no. 2020A1515111142), Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University (grant no. 2022LSDMIS05), and the Open Research Fund of Key Laboratory of Digital Earth Science, Aerospace Information Research Institute Chinese Academy of Sciences, Chinese Academy of Sciences (grant no. 2022LDE007).
This paper was edited by David Carlson and reviewed by two anonymous referees.