MODIS Dark Target Research Articles

Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land

ABSTRACT The Medium Resolution Spectral Image (MERSI) is a MODIS-like sensor aboard Fengyun-3 satellite. The first version of MERSI aerosol algorithm has been developed based on MODIS dark target (DT) algorithm, with modified models for estimating surface reflectance and an adjusted inland water masking method to release haze aerosols. This study applies MERSI DT algorithm to the global observations from the upgraded MERSI sensor (MERSI-II) on Fengyun-3D. And then, the Aerosol Optical Depth (AOD) results from the year of 2019–2020 are validated against the Aerosol Robotic Network (AERONET) data. In addition, analyses of the spatial distribution and error characteristics of MODIS and MERSI-II retrievals are presented. The overall validation demonstrates that MERSI-II retrievals perform well globally, with a correlation coefficient of 0.877 and 67.1% of matchups within the Expected Error envelope of ± (0.05 + 0.2τ), which are close to the statistic metrics of MODIS products. In addition, MERSI-II and MODIS AODs exhibit similar error trends and error dependence. Moreover, the similar global distribution characteristics of the two AODs are revealed in the retrieval performance at site and regional scales, as well as in the analysis of monthly averages. These findings indicate the success of the ported MERSI algorithm.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Assimilation of POLDER observations to estimate aerosol emissions

Abstract. We apply a local ensemble transform Kalman smoother (LETKS) in combination with the global aerosol–climate model ECHAM–HAM to estimate aerosol emissions from POLDER-3/PARASOL (POLarization and Directionality of the Earth's Reflectances) observations for the year 2006. We assimilate aerosol optical depth at 550 mnm (AOD550), the Ångström exponent at 550 and 865 nm (AE550–865), and single-scattering albedo at 550 nm (SSA550) in order to improve modeled aerosol mass, size and absorption simultaneously. The new global aerosol emissions increase to 1419 Tg yr−1 (+28 %) for dust, 1850 Tg yr−1 (+75 %) for sea salt, 215 Tg yr−1 (+143 %) for organic aerosol and 13.3 Tg yr−1 (+75 %) for black carbon, while the sulfur dioxide emissions increase to 198 Tg yr−1 (+42 %) and the total deposition of sulfates to 293 Tg yr−1 (+39 %). Organic and black carbon emissions are much higher than their prior values from bottom-up inventories, with a stronger increase in biomass burning sources (+193 % and +90 %) than in anthropogenic sources (115 % and 70 %). The evaluation of the experiments with POLDER (assimilated) and AERONET as well as MODIS Dark Target (independent) observations shows a clear improvement compared with the ECHAM–HAM control run. Specifically based on AERONET, the global mean error in AOD550 improves from −0.094 to −0.006, while absorption aerosol optical depth at 550 nm (AAOD550) improves from −0.009 to −0.004 after the assimilation. A smaller improvement is also observed in the AE550–865 mean absolute error (from 0.428 to 0.393), with a considerably higher improvement over isolated island sites at the ocean. The new dust emissions are closer to the ensemble median of AEROCOM I, AEROCOM III and CMIP5 as well as some of the previous assimilation studies. The new sea salt emissions have become closer to the reported emissions from previous studies. Indications of a missing fraction of coarse dust and sea salt particles are discussed. The biomass burning changes (based on POLDER) can be used as alternative biomass burning scaling factors for the Global Fire Assimilation System (GFAS) inventory distinctively estimated for organic carbon (2.93) and black carbon (1.90) instead of the recommended scaling of 3.4 (Kaiser et al., 2012). The estimated emissions are highly sensitive to the relative humidity due to aerosol water uptake, especially in the case of sulfates. We found that ECHAM–HAM, like most of the global climate models (GCMs) that participated in AEROCOM and CMIP6, overestimated the relative humidity compared with ERA5 and as a result the water uptake by aerosols, assuming the kappa values are not underestimated. If we use the ERA5 relative humidity, sulfate emissions must be further increased, as modeled sulfate AOD is lowered. Specifically, over East Asia, the lower AOD can be attributed to the underestimated precipitation and the lack of simulated nitrates in the model.

An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam

High-resolution aerosol products offer advantages over lower spatial-resolution products for monitoring pollution in urban areas and industrial zones. Therefore, in this study we develop a Landsat 8 algorithm to retrieve aerosol optical depth (AOD) based on the MODIS Dark Target (DT) algorithm. This algorithm uses the direct surface reflectance of Landsat 8 to build a surface reflectance relationship between the visible (BLUE, RED) and short-wave infrared bands (SWIR2). AOD data from AERONET stations in the study area and AOD data from MODIS 3 km were used for validation of the estimated Landsat 8 AOD. The estimated AOD was highly correlated at most AERONET AOD, with =0.98, (RMSE = 0.3), except Bac Lieu, but only had a moderate correlation with MODIS AOD which was attributed to the difference in data retrieval time and associated changes in climate conditions. This is the first high-resolution AOD retrieval in Vietnam, leading to more practical applications for air quality monitoring.

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

The techniques and analyses employed by remote sensing provide key information about atmospheric particle properties at regional and global scales. However, limitations in optical spectral models used to represent the different types of aerosols in the atmosphere and their effects (direct and indirect) are still one of the major causes of sources of uncertainties and substantial impacts in climate prediction. There are no studies yet in South America, especially in the Amazon Basin, that have evaluated the advantages, disadvantages, inconsistencies, applicability, and suitability of the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) destined for monitoring the ambient aerosol optical thickness over rivers and continents. In this study, the results of the DT (Dark Target) algorithm for products with 3 km and 10 km resolutions were systematically evaluated for six sites in the Amazon rainforest. The comparisons between the products were carried out with the AERONET (Aerosol Robotic Network) measurements, which were used as reference. Statistical parameters between AERONET vs. MODIS were also evaluated based on biomass burning records in the site regions. Here, the DT 10 km product showed satisfactory performance for the Amazon region, with observations between the expected error (EE) limits above 66%, in addition to R > 0.8 and RMSE < 0.3. However, the regional analysis for the two sites in the central and southern regions of the Amazon basin did not have the same performance, where the results showed an EE of 24 and 47%, respectively. The DT 3 km product did not perform well in any site, with an EE below 50%. Both products overestimated the AOD, but the 3 km product overestimated it approximately four times more due to its algorithm setup. Thus, we recommend the 10 km product for general analysis in Amazonia. Regional biomass burning records showed a direct relationship with the AERONET vs. MODIS DT with overestimation of both products. All variations between products and sites were justified based on the difficulty of retrieving surface reflectance and the model selected for local aerosols. Improvements in the optical spectral model currently implemented in the algorithms, with more realistic representations of the main types of the aerosol present in the Amazon Basin, may contribute to better performance among the evaluated products.

Global validation and comparison of MODIS DT and POLDER-3 GRASP aerosol products over ocean

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the third generation of Polarization and Directionality of Earth's Reflectances (POLDER-3) are two types of sensors, which have their own advantages in sensor design. MODIS is a conventional multi-spectral imager with single-angle observations and a wide spectral range of surface reflection bands from blue to shortwave infrared, while POLDER-3 is a multi-angle polarization instrument with narrower spectral range from blue to shortwave near-infrared. It is highly anticipated to understand the quantitative capability of these two sensors in aerosol retrievals. For this purpose, this study conducts a long-time series comparison and evaluation of over-ocean aerosol products from these two types of sensors during the period 2005–2013, corresponding to the entire lifetime of the POLDER-3 products. The MODIS aerosol products based on Dark Target (DT) algorithm and two POLDER-3 products based on General Retrieval of Atmosphere and Surface Properties (GRASP) “High Precision” (GRASP/HP) and “Models” (GRASP/Models) algorithm are adopted in this study. Two types of ground-based measurements from automatic Aerosol Robotic Network (AERONET) data and shipborne Maritime Aerosol Network (MAN) data are employed to validate four parameters common to both satellite products, including total Aerosol Optical Depth (AOD), Ångström Exponent (AE), fine-mode AOD (AODF) and coarse-mode AOD (AODC). The overall validation results show GRASP/Models has similar performance with MODIS DT in total AOD products. This indicates that the multi-angle polarization observations of POLDER-3 can have the same advantages as the multi-spectral observations of MODIS in total AOD retrievals. Furthermore, the statistical parameters of the validation results based on AERONET data show that GRASP/Models is slightly better than MODIS DT. However, for the validation results using MAN data, it is interesting to find that MODIS DT is slightly better than GRASP/Models in all aspects. This may be due that the manual measurements of MAN can consciously avoid more cloud contamination. Meanwhile, it suggests that MODIS DT algorithm over ocean needs further improvements in cloud mask and quality control. In addition, from the validation results of both types of ground-based data, it shows that GRASP/HP significantly overestimates AOD, but has better validation accuracy than GRASP/Models and MODIS DT in AE products, which implies that multi-angle polarization sensor has great potential in aerosol properties retrieval. For AODF and AODC, the statistical parameters of validation results for all three products show little difference. GRASP/HP is slightly better than GRASP/Models and MODIS DT in AODF products, while the AODC products of GRASP/Models and MODIS DT show slightly superior to that of GRASP/HP. This may suggest that although the higher AE validation accuracy of GRASP/HP, the higher total AOD accuracy of GRASP/Models and MODIS DT still dominates the validation results of AODF and AODC. The results could contribute to the application of over-ocean aerosol products of these two satellite in climate research, as well as to the improvement of aerosol retrieval algorithms over ocean for instruments such as single viewing spectrometer and multi-angle polarimeter.

Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

Model-enforced post-process correction of satellite aerosol retrievals

Abstract. Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China

Himawari-8 (H8), as a new generation geostationary meteorological satellite, has great potential for monitoring the spatial–temporal variation of aerosol properties. However, the large amount of spectral data with differing observation geometries require re-formulation of the surface reflectance correction to utilize this new satellite data. This is achieved by using an improved version of the time series (TS) technique proposed by Mei et al., (2012) based on the assumption that the ratio of the surface reflectance in different spectral bands does not change between any two scan times within an hour. In addition, more suitable aerosol models were adopted, based on cluster analysis of local Aerosol Robotic Network (AERONET) data. The improved TS algorithm (ITS) was applied to retrieve the Aerosol Optical Depth (AOD) over eastern China and the results compare favorably with collocated reference AOD data at eleven sun photometer sites (R > 0.8, Root Mean Square Error (RMSE) < 0.2). Comparison with the H8 official AOD product and with MODIS Dark Target (DT)–Deep Blue (DB) combined AOD data shows the good performance of the ITS method for AOD retrieval with different observation angles.

Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation

The MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) algorithm enables simultaneous retrieval of aerosol and bidirectional surface reflectance at high resolution of 1 km. Taking advantage of multi-angle and image-based information, the MAIAC algorithm has great potential for improving retrieval of aerosols over both dark and bright surfaces. Here, by comparing MAIAC aerosol products with the ground-based observations at 9 typical sites spread out in China, we gain the insights regarding the performance of MAIAC algorithm, for the first time, over Asia that has complicated surface types, diverse aerosol sources, and heavy loading of aerosols in the atmosphere. While aerosol products from MAIAC show similar spatial distribution as that from MODIS Dark-Target (DT) and Deep-Blue (DB) algorithms, they are superior to reveal numerous hotspots of high AOD values in fine scales due to their higher resolution at 1 km. Moreover, since MAIAC algorithm for cloud screening uses time series of observations, it shows higher effectiveness to mask cloudy pixels as well as the pixels of the melting and aging ice/snow surfaces. While MAIAC and ground-observed AOD values show high correlation coefficient of ∼0.94 in two AERONET sites of Beijing and Xianghe, considerable bias is prevalent in other regions of China. Systematic underestimation is found over the deserts in western China likely due to the high bias of single scattering properties of aerosol model prescribed in MAIAC algorithm. In eastern China, the distinct positive bias is found in conditions with low-moderate AOD values and likely results from errors in regression coefficients in the surface reflectance model. Given its advantages in cloud and snow/ice screening and retrieval in fine spatial resolution, MAIAC algorithm can be improved by further refinement of regional aerosol and surface properties.

MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Much attention has been paid to develop methods to estimate particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) from satellite aerosol optical depth (AOD). One of fundamental limitation of these methods is lack of AOD and thereby PM2.5 cannot be derived from satellites when clouds are present or when surface conditions are not favorable. This would probably result in an inherent clear-sky biased estimate of PM2.5 for air quality assessment that requires continuous 24-h measurements at all-sky conditions. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 data in North China, a highly polluted area with large spatiotemporal variabilities of AOD and PM2.5 values, missing MODIS AOD retrievals and its potential effect on PM2.5 estimation are studied. The MODIS dark target (DT) algorithm produces very few AODs in winter, with a regional observation rate of 4%, which limits its statistical significance for PM2.5 air quality monitoring. This limitation applies to MODIS DT AOD products at 10-km and 3-km resolutions since they are derived from the same retrieval core (Remer et al., 2013). In contrast, The MODIS deep blue (DB) AOD product complements the MODIS DT AOD coverage, which is remarkable in winter. The MODIS DT and DB merged product has comparable accuracy to that of the DT and DB products but shows a larger sampling rate, therefore, it is more suitable for estimating surface PM2.5. While the regional mean PM2.5 values in the presence and absence of AOD retrievals in spring and summer are comparable, but the former is substantially lower than the latter in autumn by 11.2 μgm−3 and winter by 8.5 μgm−3 on average. The difference in some stations even exceeds 20 μg m−3. Methods to fill missing AOD values in North China are crucial to provide an unbiased sampling and estimate of PM2.5 concentration in all-sky conditions, likely by integrating satellite, surface and modeling data.

Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. CapsuleIn comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage.

Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals

Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-global-averaged uncertainties in the DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval, we created a research algorithm and applied it to 80 appropriate MODIS granules during the event. Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5∘ grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm.

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up-Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data.

Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters

In this study, the Simplified Aerosol Retrieval Algorithm (SARA) was applied to both MODIS and Visible Infrared Imaging Radiometer Suits (VIIRS) images to retrieve aerosol optical depth (AOD) over water for blue, green and red wavelengths at 500m (MODISSARA) and 750m (VIIRSSARA) resolutions, respectively. Retrievals were compared with the Terra–MODIS Dark Target (DT) AOD at 3km resolution (MODIS3K) and the VIIRS Environmental Data Record (VIIRSEDR) AOD at 6km resolution. Validation was conducted using 86 Microtops II Sun photometer AOD measurements collected over different classes of water quality (low to high sediment levels) for seven days (6, 8, 9, 13, and 15, 16 and 17 October 2014) between 10:00 to 15:00 local time. Thirty-eight to fifty percent and 44–54% of the MODIS3K and the VIIRSEDR AOD retrievals respectively, fall within the expected error (EE) with root mean square error from 0.12 to 0.14 and mean absolute error from 0.10 to 0.11. The MODISSARA and the VIIRSSARA AOD retrievals are well correlated with the ground–based measurements (R: MODISSARA=0.86–0.89 and VIIRSSARA=0.85–0.94), with a larger number of retrievals falling within the EE MODISSARA=73–77% and VIIRSSARA=71–82%) than MODIS3K and VIIRSEDR. The results indicate that the SARA algorithm is more robust than MODIS3K and VIIRSEDR global AOD products, and can retrieve accurate AOD over low to highly sedimented water surfaces, similar to good AOD retrievals over land.

Landsat 8-based inversion methods for aerosol optical depths in the Beijing area

As an essential component of the Earth-atmosphere system, aerosols have important impacts on the atmospheric environment and human health. Based on the data sourced from Landsat 8 satellite images, the goal of this paper is to retrieve aerosol optical depth (AOD) in the Beijing area by means of the MODIS Dark Target (DT) Method and the visible near-infrared (VNIR) atmospheric correction method (ACM), of which the accuracy is verified by observation data from AERONET. Furthermore, analysis was conducted to assess the effects of the two specific inversion methods on AOD values and AOD distribution characteristics in Beijing. The results indicate the following: 1) both the DT method and the VNIR method can be used successfully in the inversion of AOD in Beijing with Landsat 8 satellite data, while the DT method generates a slightly higher accuracy than that of the VNIR method, in which the root mean squared error (RMSE) values are 0.195 and 0.282, respectively; 2) AOD distribution in Beijing is presented with significant regional features, in which the areas with high AOD values were mainly concentrated in six districts (Dongcheng, Xicheng, Chaoyang, Fengtai, Haidian, and Shijingshan) and their surrounding areas. In addition, the AOD values are relatively low in the southwestern and northern regions of Beijing, which was mainly due to minor impacts of human activity and transportation.

A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm

Abstract. The MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aboard the two Earth Observing System (EOS) satellites Terra and Aqua, provide aerosol information with nearly daily global coverage at moderate spatial resolution (10 and 3 km). Almost 15 years of aerosol data records are now available from MODIS that can be used for various climate and air-quality applications. However, the application of MODIS aerosol products for air-quality concerns is limited by a reduction in retrieval accuracy over urban surfaces. This is largely because the urban surface reflectance behaves differently than that assumed for natural surfaces. In this study, we address the inaccuracies produced by the MODIS Dark Target (MDT) algorithm aerosol optical depth (AOD) retrievals over urban areas and suggest improvements by modifying the surface reflectance scheme in the algorithm. By integrating MODIS Land Surface Reflectance and Land Cover Type information into the aerosol surface parameterization scheme for urban areas, much of the issues associated with the standard algorithm have been mitigated for our test region, the continental United States (CONUS). The new surface scheme takes into account the change in underlying surface type and is only applied for MODIS pixels with urban percentage (UP) larger than 20 %. Over the urban areas where the new scheme has been applied (UP > 20 %), the number of AOD retrievals falling within expected error (EE %) has increased by 20 %, and the strong positive bias against ground-based sun photometry has been eliminated. However, we note that the new retrieval introduces a small negative bias for AOD values less than 0.1 due to the ultra-sensitivity of the AOD retrieval to the surface parameterization under low atmospheric aerosol loadings. Global application of the new urban surface parameterization appears promising, but further research and analysis are required before global implementation.