# Evaluation of MODIS, MISR, and VIIRS daily level-3 aerosol optical depth products over land

Qi-Xiang Chen, Xin-Lei Han, Yu Gu, Yuan Yuan, Jonathan H. Jiang, Xue-Bo

Yang, Kuo-Nan Liou, He-Ping Tan

► To cite this version:

Qi-Xiang Chen, Xin-Lei Han, Yu Gu, Yuan Yuan, Jonathan H. Jiang, et al.. Evaluation of MODIS, MISR, and VIIRS daily level-3 aerosol optical depth products over land. Atmospheric Research, 2022, 265, 10.1016/j.atmosres.2021.105810. insu-03668297

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# Evaluation of MODIS, MISR, and VIIRS daily Level-3 aerosol optical depth products over land

3

4 Qi-Xiang Chen¹, ², Xin-Lei Han², ³, ⁴, Yu Gu², Yuan Yuan¹, *, Jonathan Jiang⁵, Kuo-Nan Liou²,

5 He-Ping Tan¹

6 ¹ School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China.

8 ² Joint Institute for Regional Earth System Science & Engineering, University of California, Los Angeles, CA, USA.

10 ³ LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.

12 ⁴ College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

14 ⁵ Jet Propulsion Laboratory, California Institute of Technology, USA.

16 Corresponding author: Yuan Yuan (yuanyuan83@hit.edu.cn); Yu Gu (gu@atmos.ucla.edu);

17 Abstract

18 This study presents a comprehensive evaluation of eight aerosol optical depth (AOD) products from the latest MODIS C6.1 DT, DB and DTB, MISR V23, and VIIRS V1 DB dataset
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against the Aerosol Robotic Network (AERONET) measurements on global scale during 2012 to 20
2019. The latest MODIS DB products are found to be superior over DT in most cases. MODIS 21
DTB products generally have a better performance than either DT or DB. VIIRS shows the best 22
overall performance on global and regional scale while MISR has a systematic underestimation 23
in moderate and high AOD conditions. Median AOD bias shows distinct regional dependence 24
with the biggest divergence between satellite AOD products appearing in Asia. All these AOD 25
products tend to have better performance in high latitude region and poorer performance in the 26
tropics likely due to the complex aerosol condition and common high cloud cover in the tropics. 27
Time series stability evaluation shows a distinct temporal pattern of statistics, in which the 28
observation number and median AOD bias tend to be high in summer while low in winter and 29
correlation R has a reversing trend. Among these products, VIIRS V1 DB has the highest time 30
series stability of bias. Influence of aerosol loading, particle size, land use, elevation, and 31
precipitable water and temperature on AOD retrievals is broadly consistent between products. 32
High aerosol loading, small particle size, bright surface, high altitude, and high precipitable 33
water and temperature tend to decrease the retrieval accuracy, e.g. lower expected error (EE) 34
within ratio and larger median bias. The latest AOD products need to be furthermore improved 35
under extreme conditions like very fine aerosols, elevated terrain and high precipitable water and 36
temperature. 37

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39 **Keywords:** MODIS; MISR; VIIRS; AERONET; aerosol optical depth; Evaluation;

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# 1. Introduction

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Aerosols play an important role in the earth-ocean-atmosphere system (Banerjee et al., 2018; Chen et al., 2016; Gu et al., 2016). They can influence the global radiative balance directly by scattering and absorbing solar radiance and indirectly by modifying the cloud and precipitation processes (Kahn et al., 2010; Zhao et al., 2019). As recent IPCC report emphasized, interactions of atmospheric aerosols with clouds and radiation are the largest uncertainty in modulating climate variability (IPCC, 2018). To understand aerosols’ direct and indirect effects on regional and global climate, accurate and long-term aerosol monitoring data is necessary (WMO, 2017).

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The major parameter for understanding the aerosol climate effect is the aerosol optical depth, which is defined as the vertical integral of extinction generated by aerosol particles (Kinne, 2009; Sogacheva et al., 2020). As ground-based observations only have limited spatial coverage, satellite AOD observations are the desired solution (Yu-ling et al., 2009). According to the criteria set by the Global Climate Observing Systems (GCOS), climate change studies require consistent products with more than 30 years record (GCOS, 2016). However, individual satellite/sensor only has limited lifespan of 5-15 years. To obtain a climatologically useful AOD record, a large number of sensors have been launched to continue the global aerosol monitoring, such as the Total Ozone Measurements, Advanced Very High-Resolution Radiometer (AVHRR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Advanced Along Track Scanning Radiometer (AATSR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging Spectroradiometer (MISR) (Kokhanovsky and de Leeuw, 2009; Lenoble et al., 2013). Among these satellites, MODIS and MISR are widely used sensors providing the AOD record in the last two decades. However, most of the above mentioned sensors are out of
service or scheduled to shut down, and Visible Infrared Imaging Radiometer Suite (VIIRS) 64 currently onboard Suomi-National Polar-orbiting Partnership (Suomi-NPP, launched in late 65 2011) and Joint Polar Satellite System-1 (JPSS-1: in operation as NOAA-20, launched in 2017) 66 is designed to continue the data records (Hsu et al., 2019). 67

Since inconsistencies in radiometric calibration, cloud screen, surface reflectance, and 68 aerosol model selection can lead to differences in AOD retrieval, AOD products from different 69 sensors must be carefully evaluated to ensure the consistency of AOD record (Sawyer et al., 70 2020; Sogacheva et al., 2020). Comprehensive evaluation of AOD products can not only help 71 understand retrieval performances on different spatiotemporal scale, but can also provide 72 directions for improving retrieval algorithms and reducing systematic biases (Liu et al., 2014; 73 Mhawish et al., 2017; Peng et al., 2019; Sayer et al., 2019). Many efforts had been made to 74 evaluate satellite AOD performances and find out potential biases and major influencing factors 75 (Gupta et al., 2016; Han et al., 2018; Kahn et al., 2010; Kahn et al., 2009; Sayer et al., 2013; 76 Sayer et al., 2019). For example, Shi et al. (2011) compared the MODIS C5.1 DT products, DB 77 AOD products, and MISR V22 AOD datasets with AERONET AOD observations and found 78 distinctly spatial differences between the MODIS and MISR AOD products. Liu et al. (2014) 79 found that VIIRS AOD differs from MODIS C6 by around -0.01 over ocean and 0.03 over land 80 and it tends to overestimate AOD over vegetated surfaces while underestimate AOD over 81 soil-dominated surfaces. Mhawish et al. (2017) investigated the MODIS C6 AOD products and 82 found the retrieval accuracy was strongly dependent on the estimated surface reflectance and 83 aerosol type. These studies presented a general view of the spatiotemporal performance and 84 systematic bias of different retrieval algorithms as well as major influencing factors on AOD 85 retrievals. Based on these findings, the latest MODIS C6.1, MISR V23 and VIIRS V1 DB AOD 86
datasets were updated and released in late 2017 and early 2018 with major improvements. Some studies have tried to validate some of these latest AOD products against AERONET observations. For example, Sayer et al. (2019) compared VIIRS V1 DB land AODs with MODIS C6.1 DB products and validated their spatial performance and time series stability. Wei et al. (2019) evaluated the MODIS C6.1 DT, DB and DTB AOD products and discussed their uncertainty related to several factors like aerosol type, elevation and land use. However, the linkage between these newly released AOD products and factors like elevation, aerosol type, and land use as well as their time series stability still remain unclear. Moreover the relative difference between these updated products need further investigation. In addition, meteorological factors (e.g. precipitable water and temperature) are closely related to AOD retrieval and aerosol property (Hu et al., 2019; Liu et al., 2018), however, their influence on satellite AOD products is scarcely discussed.

The main goal of this study is to comprehensively evaluate the newly released MODIS, MISR, and VIIRS AOD products and investigate potential influencing factors on their performance since MODIS, MISR, and VIIRS are widely used sensors for global scale aerosol monitoring. Compared with previous works, this study not only evaluates more satellite AOD products considering the uncertainty related to factors like aerosol type, elevation, and land use on global scales, but also explores the time series stability of these newly released AOD products as well as the influence of precipitable water and temperature on AOD retrievals. Global AOD products from the Terra- and Aqua-MODIS DT, DB and DTB, MISR V23, and VIIRS V1 DB algorithms between 2012 and 2019 are utilized and AERONET AODs from the latest Version 3 Level 2 collection are involved as the ground "truth" to evaluate their performance. Detailed information about MODIS, MISR, VIIRS, and AERONET AOD products and evaluation
methods are described in section 2. The performance of satellite-derived AOD on varying spatial
scale and the time series stability are presented in section 3. Influence of main factors including
aerosol loading, aerosol particle size, land use, elevation, and precipitable water and temperature
on AOD retrievals is discussed in section 4. And a brief conclusion is conducted in section 5.

## 2. Data and Method

### 2.1 MODIS DT, DB and DTB AOD products

MODIS was launched onboard Terra in 1999 and Aqua in 2002. It has a wide spectral range
from 0.41 µm to 14.5 µm, broad swath of 2330 km, and relatively fine spatial resolution of 250
m to 1 km (Levy et al., 2013). The equator crossing times for MODIS onboard Terra and Aqua
satellites are 10:30 and 13:30 local time, respectively. MODIS has been monitoring a number of
important climate parameters (e.g. AOD) globally for more than twenty years. It has different
strategies for retrieving aerosol properties over land and ocean, for example, the dark target
algorithm (DT) and the deep blue algorithm (DB). MODIS level 2 daily products are retrieved
based on the calibrated reflectance of 1 km resolution (known as the L1B data), and AOD at 3
km and 10 km resolution are major aerosol products (known as MOD04_L2 and MYD04_L2 for
Terra and Aqua, respectively). The level 3 global AOD products (MOD08_D3 and MYD08_D3
of 1×1 degree resolution) are spatiotemporally aggregated from the 10 km resolution level 2
daily products with the best quality (QA=3). In this study, the level 3 AOD products from both
Terra and Aqua satellites between 2012 and 2018 are utilized. Since this study focuses on
evaluating land AOD products, only the land aerosol retrieval algorithms are introduced here.

The DT algorithm is primarily developed for retrieving AOD over visually “dark” areas,
like vegetated land surfaces. Kaufman et al. (1997) found a consistent relationship between the
surface reflectance at 0.47, 0.65 and 2.11 µm in many vegetated regions. Since there are only three wavelength bands available as retrieval input, aerosol types have to be carefully prescribed as a function of season and location. Then a look-up table (LUT) is established for a set of likely conditions to quickly find the best solution (Levy et al., 2007). Compared with C6, the latest C6.1 aerosol products updated the radiometric calibration by adding two new calibration corrections from the NASA Ocean Biology Processing Group (OBPG) and the MODIS Characterization Support Team (MCST) (Gupta et al., 2016). It also degraded the quality assurance of retrieval (QA) to the lowest of 0 when more than 50% coastal or 20% water pixels in a 10×10 km retrieval box are detected. In addition, surface parameterization scheme over urban areas is improved by integrating the MODIS Land Surface Reflectance and Land Cover Type products (Gupta et al., 2016).

The DB algorithm is originally designed for bright-reflecting surfaces that the DT algorithm does not hold, like deserts. A major principle for the DB algorithm is that the reflectance of most surfaces is low at 0.412 µm, which increases the relative contribution from aerosols to the observed TOA reflectance (Hsu et al., 2004; Hsu et al., 2006). With an update in 2013, the second-generation DB algorithm can achieve AOD retrieval over arid and semi-arid, natural vegetated, urban, built-up, and transitional regions (Liu et al., 2014; Sayer et al., 2013). The LUT from the DB algorithm is a function of solar/sensor geometry, AOD, aerosol properties, and surface reflectance. Aerosol properties including phase function and single scattering albedo are prescribed as a function of location and seasons. The surface reflectance is prescribed dynamically from several pre-calculated surface reflectance database. The spectral AOD is quickly retrieved at a nominal 1×1 km resolution by finding the best match between the observed top-of-atmosphere (TOA) reflectance at 0.412, 0.47 and 0.65 µm and the pre-calculated TOA
reflectance stored in the LUT. The 10×10 km resolution DB AOD is similarly retrieved from the aggregated 10×10 km TOA reflectance. Compared with C6, the C6.1 DB algorithm introduces a heavy smoke detection scheme for land aerosol retrieval (Hsu et al., 2019).

The DTB AOD products are the combination of DT and DB AOD datasets with QA=3 and QA≥2, respectively. The combination process depends on the Normalized Difference Vegetation Index (NDVI). To be specific, if NDVI>0.3, the DTB AOD equals to the DT AOD; if NDVI<0.2, the DTB AOD equals to the DB AOD; if 0.2≤NDVI≤0.3, the DTB AOD equals to the average of the DT and DB AOD. The merged DTB AOD products have a broader spatial coverage over land especially for transitional regions (Levy et al., 2013).

## 2.2 VIIRS DB AOD products

VIIRS is the next-generation instrument designed to continue the AOD record after MODIS. VIIRS has 22 spectral bands ranging from 0.412 to 12.05 µm. The swath for VIIRS (3040 km) is 1.5 times than MODIS (2330 km), thus no data gaps occur at equatorial regions and twice or more sampling is available for middle and high latitude regions (Liu et al., 2014). The equator crossing time for S-NPP VIIRS is 13:30 local time. A major difference between VIIRS and MODIS is that the band difference. Specifically, except for the common bands, VIIRS has 0.488, 0.672 and 2.25 µm bands while MODIS has 0.47, 0.65, and 2.12 µm bands, which means the surface reflectance at these bands is generally brighter for VIIRS. Also, VIIRS has a larger zenith angle range of 70° than MODIS of 64°. Thus, the accuracy of VIIRS AOD products tends to be more sensitive to surface and aerosol property assumptions (Hsu et al., 2019). Currently, VIIRS only provide DB aerosol products. The VIIRS V1 DB algorithm is same as the MODIS C6.1 DB algorithm, thus repeated description of the DB algorithm is avoided here. It should be noted that the surface model for elevated and heterogeneous terrain is improved and the
regional/seasonal aerosol model is also updated only in the VIIRS V1 DB algorithm (Sayer et al., 2019). In this study, the level 3 daily AOD products with spatial resolution of 1°×1° from S-NPP satellite are used and the corresponding time period is from Mar 2012 to Dec 2019.

## 2.3 MISR AOD products

MISR was launched onboard the Terra satellite in 1999. Its equator crossing time is 10:30 am local time and it is capable of global coverage every nine days at the equator and two days at the poles. MISR has nine push-broom cameras observing the Earth at nine looking-angles ranging from 70.5° through nadir to -70.5°. Each camera has four spectral bands centered at 0.446, 0.558, 0.672, and 0.866 µm. The overlap swath width of the MISR image data is 380 km. Since MISR has a wide range of along-track view angles, it provides sensitive information on aerosol microphysical properties and also allows the retrieval algorithms to distinguish surface from atmospheric contribution to the TOA radiance (Kahn et al., 2009).

The aerosol optical modes used in MISR retrieval algorithm are composed of an Aerosol Physical and Optical Properties file and a tropospheric Aerosol Mixture file (Shi et al., 2011). In addition to the predetermined aerosol models, a LUT for obtaining most of the radiative transfer parameters needed by the algorithms is also established. It should be noted that, different from the DT and DB algorithms, no regional/seasonal constrains are applied to the aerosol types in the MISR retrieval algorithm and in most cases no a priori assumptions are made about the surface bi-directional reflectance factors. In addition, the retrievals are skipped for complex surfaces (e.g. mountain area), shallow water, cloudy and glint-contaminated regions. With the pre-calculated LUT, a principal component retrieval algorithm is performed to retrieve aerosol properties over land using both the corrected TOA radiance data and some ancillary inputs. MISR level 2 (L2) products provide AOD, aerosol composition and size, and other related parameters. The L2 data
are statistically aggregated into global grids with a spatial resolution of 0.5°×0.5° on daily, monthly, and yearly time scales to generate L3 products.

The current aerosol retrieval algorithm for MISR is Version 23 (V23). Compared with V22, V23 AOD products increase the resolution from 17.6 km to 4.4km, decrease the retrieved AOD bias below about 0.02, and store data in a NetCDF-4 format for easier usage (Garay et al., 2019; Lee et al., 2018). In this study, daily AOD from MISR L3 products between Jan 2012 and Dec 2019 are used.

## 2.4 AERONET observations

The Aerosol Robotic Network (AERONET) is a ground-based aerosol monitoring network, which provides long-term, continuous spectral AODs with low uncertainty (~ 0.01 - 0.02) and high temporal resolution (~15 minutes) on a global scale (Dubovik et al., 2002; Schuster et al., 2001). In general, all AERONET sites provide AOD at 0.44, 0.67, 0.87 and 1.02 µm at a minimum. AERONET is often regarded as a standard source for validation and bias correction for satellite AOD products. In this study, the Version 3 level 2.0 AOD products (cloud-screened and quality-assured) are used to evaluate the MODIS, MISR and VIIRS AOD performances (Giles et al., 2019). The altitude of AERONET stations and the precipitable water and temperature measured at the same time as AOD are also included. Only sites with at least 50 matchups in total are used in the evaluation process. Figure 1 shows the regional assignment for the AERONET sites.
<figure><img src="image_2.png" /><figcaption>**Figure 1** Regional assignment for the AERONET sites, total 12 regions are divided: Eastern North America (ENA), Western North America (WNA), South America (SAM), Europe (EUR), Oceania (OCE), Central Asia (CAS), Southern Africa (SAF), Indian subcontinent (IND), Northeastern Asia (NEA), Southeastern Asia (SEA), North Africa/Middle East (NAME), and Boreal (BOR). Note that this is an assignment illustration and not all of these sites are involved in the evaluation.</figcaption></figure>

## 2.5 Matchup and Evaluation method

Since AERONET provides a point observation with a sampling frequency of 5–15 min while satellite provides a regional snapshot at a single time, a spatiotemporal matchup scheme is applied. To be specific, AERONET data are firstly averaged within a time window of ± 30 minutes from the satellite overpass, and then a spatial average is conducted if more than one AERONET observation is available in the 1°×1° grid cell (Peng et al., 2019). As AERONET does not provide AOD at 0.55 µm, data are interpolated to 0.55 µm via the Angstrom Exponential relationship before averaged (Chen et al., 2019; Han et al., 2018)

$$ \alpha = - \frac{\log(\tau_1 / \tau_2)}{\log(\lambda_1 / \lambda_2)} \qquad (1) $$

where $τ_1$ and $τ_2$ are the AOD at wavelength $λ_1$ and $λ_2$. AODs at 0.44 and 0.675 µm are selected to interpolate the Angstrom Exponent ($α$) in this study.
The performance of satellite AOD products is described by several indicators including Pearson's correlation coefficient (R), root mean square error (RMSE), median bias (MB), and expected error (EE) envelope (2013-JGRA-118-7864)

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\tau_{satellite} - \tau_{aeronet})^2} \quad (2)$$

$$MB = \frac{1}{n} \sum_{i=1}^{n} (\tau_{satellite} - \tau_{aeronet}) \quad (3)$$

$$EE = \pm (0.05 + 0.15 \times \tau_{aeronet}) \quad (4)$$

where $\tau_{satellite}$ and $\tau_{aeronet}$ are the satellite-derived and AERONET-derived AOD, and n denotes the number of paired AODs.

## 3. Results

### 3.1 Global scale evaluation

**Figure 2** shows the overall statistics of satellite-derived AODs against ground-based AODs during 2012 to 2019. In general, satellite-derived AODs agree well with AERONET observations. Terra-MODIS DT shows the lowest R of 0.82 while VIIRS DB has the highest value of 0.866. Positive intercepts and less-than-one slopes from linear fittings indicate that all of the satellite products tend to overestimate AOD when AOD is low and underestimate AOD when AOD is high.

MODIS DB AOD products have both higher R and EE within ratio and lower RMSE and MB values than the DT algorithm (Figure 2a-2d). The total matchup number from DB is less than that from DT by 10% ~ 15%, however, this is contradictory with the common knowledge that DB generally has better spatial coverage than DT (Tao et al., 2015; Tao et al., 2017). This
probably because, on the one hand, the spatiotemporal aggregation when generating level 3 AOD products from the 10 km resolution AODs may, to a certain extent, eliminate the difference in the available observation number between DT and DB. On the other hand, we make a spatial average when more than one AERONET observation is available in a grid cell. Although DB is better in retrieving AOD over bright surfaces (Hsu et al., 2019; Hsu et al., 2004; Sayer et al., 2013), only limited AERONET sites are established over bright surfaces. Most of the sites are established over urban and vegetated regions, where DT works quite well (Gupta et al., 2016; Levy et al., 2013; Levy et al., 2007). These two spatial averages may explain the quantitative advantage of the total matchup number between DT and AERONET AODs.

The merged DTB AOD products generally have better performances than individual DT and DB products (Figure 2e and 2f). Compared with DT, the DTB algorithm has much increased R and EE within ratio and at the same time decreased the RMSE and median bias. Compared with DB, the DTB algorithm has increased the correlation R, and more importantly the available observation number has a significant increase by 15% ~ 20%. MISR has the highest EE within ratio of 0.8 (Figure 2e), but its available observation number is only 1/5 ~ 1/4 to that of other sensors due to its narrow swath width. Large intercept values (> 0.05) shown in Figure 2a, 2c, and 2g indicate that when AOD is low (i.e. 0 < AOD < 0.1), MODIS DT and MISR AOD tend to have larger bias than other AOD products in low AOD condition and further modification is needed to the latest MODIS C6.1 DT and MISR V23 algorithms. MISR shows a severe underestimation of AOD when AOD is greater than 0.5, thus attention is required when using MISR AOD in high aerosol loading conditions (Tao et al., 2020). VIIRS DB shows the best overall performance. High correlation R (0.866) and EE within ratio (0.753) as well as low
RMSE (0.117), intercept (0.026), and MB (0.004) for VIIRS indicate that the new generation
sensor has a better capability to continue the AOD record after EOS-era observations.

<figure><img src="image_3.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(f)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(g)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(h)</figcaption></figure>

<caption>Figure 2 Global statistics for the comparison of satellite AOD versus AERONET AOD during 2012 to 2019.</caption>

3.2 *Regional scale evaluation*

**Figure 3** shows the regional statistics of satellite-derived AOD against ground-based AOD during 2012 to 2019. Europe (EUR), Western North America (WNA), and Eastern North America (ENA) account for over half of theIRRbacks. Correlation R over these regions is generally lower than the global average but EE within ratio is higher for all products. MODIS DT performs fairly poor over WNA, which has high RMSE and median bias while low R and EE within ratio. Generally, DT performs well over vegetated and suburban regions like EUR, however, AERONET sites over ENA tend to be over more arid, elevated, and mountainous, and thus depress DT’s performance (Levy et al., 2013; Sayer et al., 2019; Wei et al., 2019). A relative good performance is broadly consistent between MODIS DB and DTB, MISR, and VIIRS DB products over WNA, ENA, and EUR. VIIRS DB tends to have the best over performance with both R and EE within ratio over 0.8 and absolute MB below 0.01. Similar to
WNA, ENA, and EUR, aerosol loading in South America (SAM) is low and correlation R for all products is lower than global average while EE within ratio is higher. Significantly positive MB over WNA, ENA, EUR and SAM in **Figure 3f** indicate that the latest DT algorithm tends to overestimate AOD by 0.05 ~ 0.1 over these predominantly vegetated regions and the use of DT AOD products over these regions needs more care.

North Africa/Middle East (NAME) is the world's largest mineral dust aerosol source (Banks et al., 2013; Hsu et al., 2006). Since DB was originally designed for bright surface like desert (Hsu et al., 2004), the total matchups of DB is significantly larger than that of DT, and correlation R and EE within ratio of DB are also higher than those of DT as expected. However, both MODIS and VIIRS DB generate a distinct positive MB of ~0.05 and ~0.1 over NAME while DT shows much less bias. This is mostly because DT tends to underestimate AOD in the southwest part of NAME while overestimating AOD in the northeast part and thus shows a superficially smaller MB than DB (**Figure 4**). It is expected that additional AERONET sites over NAME can be developed to help characterize the varying aerosol properties and promote the regional aerosol retrieval. DTB generally has better statistics than DB and DT, including higher correlation R and EE within ratio, larger matchups, and lower median bias. South Africa (SAF) has various aerosol sources, like the transported dust from NAME in the northern part and locally biomass burning aerosols in the southern part (Banks et al., 2013; Hansell et al., 2007). Correlation R for all products over SAF is higher than the global average while EE within ratio is slightly lower. All the products tend to underestimate AOD over SAF, and this is possibly due to the complex aerosol condition and that the aerosol model over SAF needs further improvement.

Central Asia (CAS) is characterized by mountainous arid surfaces and therefore available AERONET sites are limited. Aerosols over CAS are clean continental and local urban aerosols
with occasional transported mineral dust aerosols (Gu et al., 2016; Hsu et al., 2006). Aerosol loading over CAS is lowest in Asia with a regional mean AOD of 0.18, which is close to SAF and higher than ENA and EUR. Correlation R and EE within ratio over CAS are generally comparative to the global average. DT tends to overestimate AOD by near 0.1 over CAS while DB tends to slightly underestimate AOD by 0.04. Indian subcontinent (IND), Northeast Asia (NEA), and Southeast Asia (SEA) are characterized by highly complex aerosols with spatiotemporal varying contributions from mineral dust, smoke, and urban/industrial emissions and aerosol loading there is much higher than other regions (Choi et al., 2019; Gu et al., 2016). Although all the products show relatively high correlation R, their performances vary. MODIS DT has a positive bias over IND, and the bias tends to be larger over NEA and SEA. MODIS DB has a positive bias over NEA, a negative bias over SEA, and a larger negative bias over IND. DTB products generate much smaller bias over IND and SEA but still largely overestimates AOD over NEA. VIIRS DB shows somewhat better performance than MODIS DB, and this is due to the adjustments to surface and aerosol models over IND, NEA, and SEA, which is not implemented in MODIS C6.1 (Hsu et al., 2019; Sayer et al., 2019). MISR generally underestimates AOD over NEA, IND, and especially SEA by 0.1. High AOD and RMSE over IND, NEA, and SEA indicate a strong variation of aerosol species and the corresponding aerosol characterization (aerosol optical model determination) is probably the largest challenge on promoting the retrieval performance. The complex terrain and monsoon also increase the difficulty in aerosol retrieval over these regions (Sayer et al., 2019). Unfortunately, AERONET sites over IND and NEA are too sparse to catch the aerosol properties and their spatiotemporal variation. More ground-based sites are encouraged to enhance our understanding of the aerosol properties over IND and NEA and to further improve the retrieval performance.
Oceania (OCE) has the lowest regional mean AOD among the twelve regions, and rarely high aerosol loading events are closely related to wildfire smokes(Mukkavilli et al., 2019). Similar to ENA and EUR, EE within ratio over OCE is higher than global average and correlation R is lower. A positive bias (~ 0.02) is found in VIIRS DB products over OCE while a negative bias (~ -0.02) in MODIS DB products. This difference mainly comes from the amplification of small differences in sensor calibration and/or how spectral response functions in low aerosol loading and bright surface conditions (Sayer et al., 2019). Boreal (BOR) is also a low aerosol loading region and performance in BOR is fairly similar to that in ENA and NEA, which has familiar surface and aerosol conditions.

<figure><img src="image_11.png" /><figcaption>**Figure 3** Regional statistics for the comparison of satellite AOD versus AERONET AOD during 2012 to 2019. The size of the square represents the absolute value of variables. Median Bias: median bias of AOD at 550 nm, R:</figcaption></figure>
Pearson's correlation between AERONET and satellite AOD, AOD: regional mean AOD at 550 nm, EE: the ratio of EE within, and RMSE: root mean square error. ENA: Eastern North America, WNA: Western North America, SAM: South America, EUR: Europe, OCE: Oceania, CAS: Central Asia, SAF: Southern Africa, IND: Indian subcontinent, NEA: Northeastern Asia, SEA: Southeastern Asia, NAME: North Africa/Middle East, and BOR: Boreal.

## 3.3 Site scale evaluation

The global and regional statistics (Figure 2 and 3) provide a primary view of the AOD products performance from different sensors/algorithms, however, the uneven AERONET site distribution and inconsistent sampling period among sites will lead to a certain uncertainty to the statistics. Therefore, a site scale evaluation is conducted to understand the uncertainty. Figure 4 presents the site scale statistics of Aqua-MODIS DT and DB, MISR, and VIIRS DB AOD against ground-based AOD during 2012 to 2019.

In general, site scale performance is broadly consistent between different satellite AOD products. A distinctly spatial inconsistency of correlation R, EE within ratio and MB is observed in Figure 4. Regional mean correlation R is relatively low (< 0.5) over WNA, SAM, and OCE. In WNA, sites with low correlation R are mainly located around rugged/elevated terrain (Sierra Nevada) and coastal regions. This tendency is even clearer in SAM, that correlation R is low in the west part where there are mainly coastal and mountainous sites (Andes Mountains) while high in the east plain. A similarly low correlation R is also observed in OCE. Although correlation R is low over WNA and SAM, MODIS DB, MISR, and VIIRS DB show high EE within ratio and small MB, indicating a good performance over these low aerosol loading regions. However, DT has large positive MB and low EE within ratio over WNA and SAM, indicating DT retrievals over rugged/elevated terrain and coastal regions are inferior to DB at present and an update of surface model for elevated and heterogeneous terrain to DT is expected in the next DT algorithms to promote the performance over these regions. DB tends to have the most sites with high EE within ratio (> 0.8) and low MB (< ±0.04) over WNA than MISR. And this is
likely due to that DB algorithm has surface model for elevated and heterogeneous terrain to correct AOD retrievals over WNA while MISR skips the retrievals over complex terrain like mountains (Garay et al., 2019; Kahn et al., 2009).

All the products show a distinctly poor performance in the tropics. EE within ratio over NAME, IND, and SEA is significantly lower than other regions. A similar spatial pattern is also observed for MB. MISR tends to overestimate AOD in the middle of NAME by > 0.1 and underestimate AOD in the north part of SAF and IND by < -0.1. For MODIS and VIIRS, large positive bias (> 0.1) appears in NAME, south part of SEA, and west part of NEA while negative bias (< -0.1) appears in the north part of SAF and IND. Such poor performance in the tropics is likely due to that the relatively sparse ground sites are not able to summarize reasonable aerosol models to characterize the erratic aerosol conditions in the tropics (Sayer et al., 2019). And common high cloud cover (especially the optical thin cirrus) in the tropics is also a great challenge to achieve successful AOD retrievals (Chew et al., 2011; Reid et al., 2013).

<figure><img src="image_12.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(f)</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure 4** Site scale statistics for the comparison of satellite AOD versus AERONET AOD during 2012 to 2019.</figcaption></figure>

3.4 *Time series stability*

Time series stability and drift characterization are important for the evaluation of satellite AOD performance, because the accuracy of AOD trend analysis is closely related to the time series stability and potential drift. **Figure 5** shows the time series statistics of the comparison between satellite-derived AOD and ground-based AOD. The variation ofIRRPS from all AOD products shows a similar pattern. The number of globalIRRPS reaches peak in July and August while the bottom in December and January, which is probably due to the higher cloud cover ratio in winter than that in summer. A significant decrease of MISR and VIIRSIRRPS is observed between 2019 and 2020. It is likely that much of the AERONET data collected in 2019 have not been fully raised to L2 at present. Although there is a decrease trend of monthlyIRRPS between 2019 and 2020, the annualIRRPS between 2019 and 2020 is very close to that between 2013 and 2014, which exceeds 18,000 pairs of observations for VIIRS and MODIS
while 5,000 for MISR (Figure 6). Such large number of AOD pairs is sufficient enough to conduct global evaluations between 2019 and 2020.

<figure><img src="image_19.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(f)</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Figure 5** Time series of global matchups per day, median bias, and correlation for the comparisons between different satellite-derived AOD with AERONET AOD at 550 nm during 2012 to 2019. The light tones represent daily statistics while dark tones represent monthly statistics.</figcaption></figure>

<figure><img src="image_26.png" /></figure>

Daily correlation R of all the AOD products especially MISR shows a violent fluctuation between 0 and 1, indicating that the accuracy of daily AOD products is unstable and its usage should be taken care. Compared with MODIS DT and DB, MODIS DTB generally shows both smaller fluctuations and higher values of R, which means the merging process in DTB algorithm can improve the retrieval stability. On a monthly scale, correlation R generally ranges from 0.7 and 0.9. A less obvious seasonal pattern is observed, showing that correlation R tends to be high in winter while low in summer, which is contrary to the trend of global matchups. The time series of median bias show a similar pattern to the number of global matchups especially for MODIS DT and MISR (Sayer et al., 2019). The higher correlation R and lower median bias in winter indicate that satellite AOD products tends to perform better in winter time. MISR shows the most violent fluctuation on a daily scale while VIIRS fluctuates the least. MODIS DT tends to have the largest range of daily median bias between -0.08 and 0.2. On a monthly scale, MODIS DT generally shows a large positive median bias, which is up to 0.1 in the middle of year. The median bias is much smaller for MODIS DB. VIIRS DB has the smallest MB fluctuation among these products, indicating that VIIRS DB products have the best time series stability and also the smallest AOD drift on a daily and monthly scale.
<figure><img src="image_27.png" /><figcaption>**Figure 6.** Annual variation of total matchups for VIIRS_DB, MISR_L3, Aqua- and Terra-MODIS AOD products.</figcaption></figure>

Since median bias is large over heavily polluted regions (Figure 4), we further explore the time series stability on a monthly scale at selected AERONET sites with long-term observations, which include Beijing, Kanpur, Banizoumbou, and Alta Floresta (Figure 7). Beijing, the capital of China, is characterized by a complex mixture of aerosol types; Kanpur is a rapidly industrializing city in India; Banizoumbou is located in Niger, which is influenced by smoke and dust aerosols; Alta Floresta is at Brazil with vegetated terrain and is often influenced by intense biomass burning smoke during August-November. From Figure 7a-7e, the similarity between AERONET and satellite AOD time series seems to be the best in Alta Floresta while the worst in Beijing. MODIS DT tends to overestimate AOD over Kanpur and Alta Floresta while it underestimates AOD in Banizoumbou. From Figure 7e and 7i, time series variation of R is mild for VIIRS DB, MODIS DB, and DT AODs in Beijing and their EE within ratios are mostly between 0.25 and 0.75. Although time series variation of R is violent in Kanpur, Banizoumbou, and Alta Floresta, their EE within ratios from VIIRS and MODIS DB AODs are generally between 0.5 and 1.0 (Figure 7e-7l). However, EE within ratio from MODIS DT is generally lower than those from VIIRS and MODIS DB. In general, VIIRS and MODIS DB AOD products show a stable performance over polluted regions, however, the performance of MODIS
423

DT AOD tends to have larger variations with locations and more attention should be paid when
DT AOD is used. The relatively large difference between AERONET and satellite AODs in
Beijing indicates that further study on AOD retrieval over eastern China with complex aerosol
types would be worthwhile for the future. It should also be noted that the comparisons in this
study are not strictly consistent in both spatial and temporal scales due to their difference in the
number of matchups, and the results may vary with the number of matchups as well as algorithm
iteration.

424

425

426

427

428

429

<figure><img src="image_28.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(f)</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(g)</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(h)</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(i)</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>(j)</figcaption></figure>
<figure><img src="image_38.png" /><figcaption>**Figure 7** Time series of monthly mean AOD<sub>550</sub>, correlation R and EE within ratio for the comparisons between satellite-derived AOD<sub>550</sub> with AERONET AOD<sub>550</sub> at Beijing, Kanpur, Banizoumbou, and Alta Floresta during 2012 to 2019.</figcaption></figure>

**4. Discussion**

The performance and uncertainty of satellite-derived AOD is closely associated with several main factors including aerosol loading, aerosol particle size, land use, elevation, and precipitable water and temperature (Liu et al., 2018; Sayer et al., 2013; Sayer et al., 2019; Wei et al., 2019). In this section, overall matchups are divided into several groups according the above factors and the performance and uncertainty of satellite-derived AOD in each group is discussed.

**4.1 Influence of aerosol loading and particle size on retrievals**

**Figures 8** show the performance of Aqua-MODIS DT and DB, MISR, and VIIRS DB AOD products on different aerosol loading ranges. A general tendency toward negative bias is observed for all AOD products. Among all the satellite AOD products, MISR shows the largest median bias when AOD is greater than 0.5 (Tao et al., 2020). Correlation R shows decreasing trends from 0.4 to 0 with AOD increasing for all products and EE within ratio show a similar decreasing trend from over 0.8 to less than 0.25. MISR has the best performance with AOD ranging from 0 to 0.4 and VIIRS DB tends to perform the best when AOD is greater than 0.4. The decrease of R and EE within ratio and the increase of median bias with AOD indicates that the performance of latest satellite AOD products from MODIS, MISR, and VIIRS gets worse as AOD increases and corresponding corrections for retrieving AOD in high aerosol loading condition are necessary in the next version of products.
<figure><img src="image_39.png" /><figcaption>**Figure 8** Global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of AOD. The size of the square represents the absolute value of variables.</figcaption></figure>

**Figures 9** show the performances of Aqua-MODIS DT and DB, MISR, and VIIRS DB AOD products on different aerosol loading range at Beijing, Kanpur, Banzioumbou, and Alta Floresta. R and EE within ratio from MISR and VIIRS DB products are generally higher than MODIS AOD products in different AOD bins. In Beijing, the EE within ratio from MISR and VIIRS DB is higher than MODIS DT and DB products. MODIS DT and DB tend to overestimate AOD by 0.2 when AOD is between 0.3 and 1.5. VIIRS DB tends to overestimate AOD by 0.1 when AOD is between 0 and 0.6 while underestimating it when AOD is larger than 1.0. In Kanpur, MODIS DT overestimates AOD while MODIS DB, VIIRS DB, and MISR underestimates AOD, and the trend gradually becomes apparent with AOD increasing. In Banizoumbou, MODIS DB and DTB, VIIRS DB and MISR overestimate AOD when AOD is less than 0.3 while underestimating AOD when AOD is larger than 0.5. However, MODIS DT underestimates AOD by >0.1 when AOD is larger than 0.2 because DT algorithm is not suitable for desert regions(Gupta et al., 2016). In Alta Floresta, median bias is low when AOD is less than 0.5 and MODIS DT shows the largest bias among all products. In general, the performance of satellite AODs varies with location and aerosol loading, and extra attention is needed when choosing satellite AOD products under high aerosol loading conditions. Further efforts are still
needed to reduce errors under high aerosol loading conditions for MODIS, VIIRS, and especially
MISR AOD products.

<figure><img src="image_40.png" /><figcaption>**Figure 9** Statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of AOD at Beijing, Kanpur, Banizoumbou, and Alta Floresta. The size of the square represents the absolute value of variables.</figcaption></figure>

**Figures 10** shows the performance of satellite AOD products as a function of AE. AE is an indicator used to describe the size of bulk aerosol particles (Chen et al., 2019). The higher AE is, the smaller the aerosol particle is. Generally, correlation R stabilizes at around 0.8 when AE is lower than 1.5 and then shows a decreasing trend. The median bias tends to grow with AE for all the products, and median bias reaches the maximum value when AE is between 2.5 and 3. The growing bias with AE indicates that the current retrieval algorithm performs relatively poor when fine mode aerosols dominate and a proper modification of the fine mode aerosol model
could be a possible solution to reduce the bias in future algorithms. MODIS and MISR DB and MISR tends to slightly underestimate AOD by ~0.1 when AE is low. Extra caution is needed for DT AOD products, because DT generally overestimates AOD by 0.1 for most AE clusters except the bin with AE between 0 and 0.5.

<figure><img src="image_41.png" /><figcaption>**Figure 10** Global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of AE. The size of the square represents the absolute value of variables.</figcaption></figure>

**Figures 11** show the performance of satellite AOD products as a function of AE at Beijing, Kanpur, Banzioumbou, and Alta Floresta. In Beijing, VIIRS DB shows the lowest median bias when AE ranges from 0.5 and 2.0. Median bias from VIIRS DB decreases with AE while those from MODIS DT and DB increase with AE. MODIS DT and DB tend to overestimate AOD by >0.1 when AE is larger than 1.0. In Kanpur, large underestimation of AOD (i.e. >0.1) is observed in VIIRS DB, MISR, and Aqua-MODIS DB products while large overestimation of AOD (i.e. >0.1) is observed in Terra-MODIS AOD as well as Aqua-MODIS DB AOD when AE is larger than 1.0. In Banizoumbou, MODIS DT shows the poorest performance because DT algorithm is not good at retrieving AOD over bright surfaces thus leading to the severe underestimation of AOD(Gupta et al., 2016; Tao et al., 2015). MISR AOD underestimates AOD by ~0.1 when AE is between 0 and 0.5 and VIIRS DB AOD underestimates AOD by ~0.1 when
AE is between 1.0 and 1.5. In Alta Floresta, MODIS DT and DTB tends to overestimate AOD by ~0.1 when AE is greater than 1.5. In general, the performance of satellite AODs varies with location and AE, and more attention is needed when using AOD products under too high or too low AE conditions. Further efforts are needed for MISR and VIIRS DB to reduce errors at high polluted regions like Kanpur and Banizoumbou when AE is very high and very low.

<figure><img src="image_42.png" /><figcaption>**Figure 11** Global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of AE at Beijing, Kanpur, Banizoumbou, and Alta Floresta. The size of the square represents the absolute value of variables.</figcaption></figure>

## 4.2 Influence of land use and elevation on retrievals

**Figure 12** shows the influence of land use and elevation on satellite AOD retrievals. The land use information is extracted from the MODIS MCD12C1 dataset, which divides earth surface into 17 types (Abercrombie and Friedl, 2016). In this study, land use is regrouped into 6
major clusters including water, forest, savannas, cropland, urban and barren. Forest denotes forest and shrubland, savannas refers to grassland, savannas, and wetland, urban represents urban/built-up and mosaic, cropland refers to cultivated land, and barren denotes desert/sparsely vegetated region and snow/ice. Regrouping land use into 6 major clusters is to assure there are sufficient matchups in each group for statistical analysis especially for MISR AOD which has only 1/5 ~ 1/4 available observation number to that of other sensors.

In general, performance on different land use is broadly consistent between these AOD products. Better performance is observed over forest, savannas, cropland, and urban while relatively poorer statistics (lower R and EE within ratio) are observed over barren. A relatively large positive bias is also observed over barren for all products, indicating that further modification on surface radiance correction and aerosol model is necessary to fill the gap (Banks et al., 2013; Sayer et al., 2019; Wei et al., 2019). DT shows the poorest performance over barren with the lowest correlation R and EE within ratio (<0.4) as expected, but it is our expectation that DB shows better performance than DT over 'dark' surfaces like forest and savannas, which agrees well with the comparison work from Wei et al. (2019) that MODIS C6.1 DB retrievals show similar good or even better performance than the DT and DTB retrievals for densely vegetated areas. MISR shows the best overall performances with both high correlation R and EE within ratio than other products due in to its multi-angle observations, which are less sensitive to surface reflectance variation (Garay et al., 2019; Kahn et al., 2009).

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>
<figure><img src="image_46.png" /><figcaption>**Figure 12** Global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of land use type. The size of the square represents the absolute value of variables.</figcaption></figure>

The results shown in **Figures 13** indicate that the retrieval performance is closely related to elevation. For MODIS DT and MISR, there is a decreasing tendency for both correlation R and EE within ratio with elevation and an increasing tendency for median bias. For MODIS DB and VIIRS DB, EE within ratio shows an increasing trend with elevation and correlation R and median bias are relatively stable. When elevation ranges between 2400 and 2800 m, AOD is very low (< 0.1) and the high EE within ratio shows a good performance for DB retrievals despite a very low correlation R. VIIRS DB shows a generally better performance than MODIS DB especially for elevation larger than 1600 m and this is mainly because VIIRS DB has updated the surface model for elevated and heterogeneous terrain while MODIS DB uses the old surface model from C6 (Sayer et al., 2019). It is suggested that DT and MISR should introduce a similar surface model to DB in the future to correct its retrieval over elevated and heterogeneous terrain.

<figure><img src="image_47.png" /><figcaption>**Figure 13** Global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of</figcaption></figure>
elevation. The size of the square represents the absolute value of variables.

## 4.3 Influence of precipitable water and temperature on retrievals

The key aspects for satellite sensors retrieving AOD mainly consists of five parts: observing geometry, surface albedo, cloud screening, aerosol model, and radiative transfer code. Surface albedo and aerosol model have close relationship with not only factors like aerosol loading, aerosol particle size, and elevation, but also precipitable water and temperature.

Precipitable water is supposed to be related to hygroscopic growth of anthropogenic aerosols, since anthropogenic aerosols are mainly fine-mode particles which contain a lot of hygroscopic components like sulfates and nitrates. **Figure 14a** shows the variation of fine-mode effective radius in different precipitable water ranges from AERONET dataset. It is observed that fine-mode effective radius of aerosol particle is generally small when the precipitable water is under 3 cm, and it meets a drastic increase when the precipitable water is higher than 4 cm. **Figures 14b-14f** show the influence of precipitable water on satellite AOD retrievals. When precipitable water is low (0 - 2 cm), MODIS DT performs generally poorer than MODIS DB, MISR, and VIIRS DB for MODIS DT has a low EE within ratio (< 0.65) when AOD is low. For all products, the correlation R stabilized around 0.8 and the EE within ratio with precipitable water presented a decreasing trend. The median bias for DT shows an increasing trend with precipitable water over 4 cm, indicating that high precipitable water has a huge impact on DT retrieval accuracy. MISR is the least affected. Although no distinct bias is observed for MISR and DB with precipitable water, the decrease of EE within ratio also indicates that precipitable water is likely a potential influencing factor to satellite AOD retrievals. And such a performance decrease is probably related to, one the one hand, the increasing thermal contrast with precipitable water, which will cause large uncertainty in AOD retrievals (Liu et al., 2018; Sayer
et al., 2019), and on the other hand, the hygroscopic growth of aerosol particle in high precipitable water condition is not fully considered in the aerosol models in current MODIS, MISR and VIIRS inversion algorithms.

<figure><img src="image_48.png" /><figcaption>**Figure 14** (a) variation of fine-mode effective radius in different precipitable water ranges from AERONET dataset, and (b-f) global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of precipitable water. The size of the square represents the absolute value of variables.</figcaption></figure>

**Figures 15** show the influence of precipitable water on satellite AOD retrievals at Beijing, Kanpur, Banizoumbou, and Alta Floresta. In Beijing, EE within ratios from Terra-MODIS DT and DTB are mostly lower than 0.4, and their corresponding median biases are higher than 0.1 when precipitable water is less than 3 cm. MISR and VIIRS DB tend to underestimate AOD when precipitable water increases. In Kanpur, MODIS DT products tend to overestimate AOD by 0.2 when precipitable water is less than 0.6 cm or larger than 3.6 cm. MISR and VIIRS DB tend to underestimate AOD by >0.15 when precipitable water is greater than 1.0 cm. In Banizoumbou, MODIS DB, DTB, and VIIRS DB products tend to underestimate AOD when precipitable water is between 1.0 cm and 4.0 cm. In Alta Floresta, satellite products tend to underestimate AOD and then overestimate it with the increase of precipitable water. In general, the MISR and VIIRS DB tend to underestimate AOD when precipitable water is high while
MODIS DT and DTB tend to underestimate it. Thus, caution is needed when satellite AOD products are used under different precipitable water conditions in different regions.

<figure><img src="image_49.png" /><figcaption>**Figure 15** Statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of precipitable water at Beijing, Kanpur, Banizoumbou, and Alta Floresta. The size of the square represents the absolute value of variables.</figcaption></figure>

Temperature will influence snowfall and vegetation. When temperature is low (i.e. < 0 °C), land surface would be covered by snow, some vegetated surface (e.g. savannas and cropland) would be covered by withered grass or even turn to bare ground, and these would lead to possible increase of surface albedo in most land surfaces. **Figure 16a** shows the variation of surface albedo in different temperature ranges. When temperature is lower than 0 °C, surface albedo over forest, savannas, cropland and urban regions is around 0.1, which is close to that over barren region (mostly desert and bare ground). And when the temperature rises, it decreases
drastically to around 0.5. Thus, snow cover and vegetation withering are probably the reasons of albedo increase in low temperature condition (i.e. < 0 °C) over forest, savannas, cropland and urban regions. **Figures 16b-16f** present the influence of temperature on satellite AOD retrievals. When AOD is low (< 0 °C), products performance like R, RMSE and median bias is inferior to that with temperature between 10 °C and 30 °C. This indicates that current satellite inversion algorithms still have some shortcomings in setting a proper surface albedo value under low temperature conditions. VIIRS and MISR have a relatively better performance than MODIS in low temperature condition, because VIIRS AOD tends to be more sensitive to surface albedo due to its usage of brighter surface reflectance bands and a larger zenith angle range than MODIS, while MISR has nine push-broom cameras observing the Earth at nine looking-angles, which allows it to better distinguish surface albedo from atmospheric contribution to the TOA radiance. Thus, it is suggested to use VIIRS and MISR AODs in low temperature conditions (e.g. < 0 °C).

<figure><img src="image_50.png" /><figcaption>**Figure 16** (a) variation of surface albedo in different temperature ranges from AERONET dataset, and (b-f) global statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of temperature. The size of the square represents the absolute value of variables.</figcaption></figure>

When temperature increases, correlation R stabilizes at global average but EE within ratio decreases below the global average. This indicates a negative influence of high temperature on
satellite AOD retrievals. **Figure 17** shows the monthly variation of precipitable water and temperature in north and south hemisphere. Monthly variation of precipitable water shows an identical trend to that of temperature, where the precipitable water and temperature are high in summer and low in winter. The precipitable water is positively correlated to temperature in south (R=0.589) and north (R=0.675) hemisphere, since temperature increase will enhance the solubility of water vapor, which leads to a higher content of precipitable water in the column atmosphere. Thus, it is likely that many of the matchups with high temperature are influenced by the hygroscopic growth effect of aerosol particles. Another possibility is the aerosol photochemical reaction. Aerosol photochemical reaction increases the loading of anthropogenic and biomass burning aerosols largely under high temperature conditions (Weitkamp et al., 2007), and such an increase may somewhat change the aerosol optical properties and cause performance decrease (Hu et al., 2019; Mallet et al., 2005).

<figure><img src="image_51.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>(b)</figcaption></figure>

<caption>**Figure 17** Monthly variation of precipitable water and temperature from global AERONET dataset.</caption>

**Figures 18** present the influence of temperature on satellite AOD retrievals at Beijing, Kanpur, Banizoumbou, and Alta Floresta. In Beijing, median biases from MODIS DT show an increasing trend with temperature, which is different from its global average. VIIRS DB overestimates AOD when the temperature is between 2 °C and 18 °C and then underestimates it when the temperature is greater than 20 °C. In Kanpur, MISR and VIIRS DB underestimate
AOD by >0.1. Similar to Beijing, VIIRS DB in Banzioumbou overestimates AOD when the temperature is between 26 °C and 32 °C and then underestimates it when the temperature is greater than 36 °C. In Alta Floresta, MODIS DT and DTB show higher median bias than other products.

In summary, the performance of satellite AOD products varies with precipitable water and temperature. The global- and site-scale analysis show that the satellite AODs generally have good performances under median precipitable water (2 ~ 4 cm) and temperature conditions (10 ~ 30 °C), but relatively bad performances under high precipitable water (> 4 cm) and extreme temperature conditions (< 0 °C and >30 °C). The bad performance is likely caused by the improper setting of aerosol models and surface albedo. Further efforts may be made on two aspects, one is to account the hygroscopic growth effect and aerosol photochemical reaction in current aerosol models to better depict the aerosol status under high precipitable water and temperature conditions, and the other is to quantify the effects of sub-zero temperature on surface albedo (i.e. snow cover or vegetation withering) and provide a more accurate surface albedo value into the satellite AOD retrieval algorithms.

<figure><img src="image_53.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>(f)</figcaption></figure>
<figure><img src="image_59.png" /><figcaption>**Figure 18** Statistics for the comparison of satellite-derived AOD against AERONET AOD as a function of temperature at Beijing, Kanpur, Banizoumbou, and Alta Floresta. The size of the square represents the absolute value of variables.</figcaption></figure>

## 5. Conclusion

Eight aerosol optical depth products over land from the latest MODIS C6.1 DT, DB, and DTB, MISR V23, and VIIRS V1 DB algorithms are comprehensively evaluated against AEORNET V3 level 2 data during 2012 to 2019. In general, VIIRS DB products show the highest correlation R of 0.866. MISR has the largest EE within ratio but it also has the most severe underestimation of AOD in moderate and high AOD conditions. Aqua-MODIS tends to have better performances than Terra-MODIS, which is due in part to the severe aging of Terra-MODIS and corresponding correction difficulty. Fusion DTB products show a better overall performance than individual DT and DB products with both larger observation number and correlation R and lower RMSE.

Regional scale evaluation results show that all the products tend to overestimate AOD over NAME while underestimate AOD over SAF. MODIS, VIIRS DB, and MISR also show large bias over Asia and DT tends to overestimate AOD by > 0.05 over most regions like America, Europe and Asia. From site scale comparison, a distinctly poor performance is observed in the tropics for all products. It is probably due to the changeable aerosol condition as well as the
presence of high cloud cover (especially the optical thin cirrus) that depress the retrieval accuracy in the tropics. Time series evaluations show that correlation R tends to be high in winter while low in summer and overalls and global median biases show a reversing trend. Among all the products, VIIRS DB shows the best time series stability and smallest AOD drift on the whole, while DT shows the worst time series stability.

The aerosol loading analysis shows that performance falls sharply as AOD increases for all products and MISR tends to have the best performance with AOD ranging from 0 to 0.4 while VIIRS DB performs the best when AOD is greater than 0.4. Poorer performance and larger bias appear in high AE bins (>2), indicating a potential deficiency that the current aerosol model may not work well for very fine aerosols. DB and MISR performs generally better than DT over all land uses especially over barren. The increase in elevation generally depresses the retrieval accuracy of DT and MISR while it influences little on DB retrievals due to its surface model correction for elevated and heterogeneous terrain. A better performance of VIIRS DB than MODIS DB suggests that the updated surface model in the latest DB algorithm is able to promote AOD retrieval than previous ones. The meteorological analysis shows that performances get worse with high precipitable water and temperature. MISR tends to be the least influenced while DT is the most influenced. Similar precipitable water and temperature corrections to DB's surface model for elevation and heterogeneous terrain are expected to be introduced into current retrieval algorithms to achieve better retrievals in the future.

The site-scale analysis at Beijing, Kanpur, Banizoumbou, and Alta Floresta shows that performance at different sites would vary with factors including AOD, AE, precipitable water, and temperature. Their variations can be different from the corresponding global average and might be opposite between sites. Thus, further efforts are needed for MODIS, MISR, and VIIRS
algorithms to reduce these errors in different regions and conditions. It is also suggested that DT and MISR should introduce DB's surface model into their algorithm to promote their performance over elevated/rugged regions. More AERONET sites are encouraged to be installed to North Africa and Asia since they are the largest aerosol emission sources around the world and satellite AOD retrievals in those regions are less accurate, which may cause large uncertainty in climate studies.

## Acknowledgement

This study is supported by the National Natural Science Foundation of China (51776051 and 52041601). The authors would like to thank the VIIRS, MISR, and MODIS team and AERONET principal investigators and site managers for kindly providing the data. VIIRS and MODIS data can be freely downloaded from NASA's Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center (https://ladsweb.modaps.eosdis.nasa.gov/). MISR data can be found at the Atmospheric Science Data Center (https://eosweb.larc.nasa.gov/project/misr/misr_table). AERONET data are publicly accessible at their official website (http://aeronet.gsfc.nasa.gov/).

The authors declare no conflict of interest.

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800
Evaluation of MODIS, MISR and VIIRS aerosol optical depth
products over land

Qi-Xiang Chen¹, ², Xin-Lei Han², ³, ⁴, Yu Gu², Yuan Yuan¹, *, Jonathan Jiang⁵, Kuo-Nan Liou²,

He-Ping Tan¹

¹ School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, PR China.

² Joint Institute for Regional Earth System Science & Engineering, University of California, Los Angeles, CA, USA.

³ LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.

⁴ College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

⁵ Jet Propulsion Laboratory, California Institute of Technology, USA.

<figure><img src="image_60.png" /><figcaption>Graphical Abstract:</figcaption></figure>