Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

Sun, Lin; Wei, Jing; Bilal, Muhammad; Tian, Xinpeng; Chen, Jia; Guo, Yamin; Mi, Xueting

doi:10.3390/rs8010023

Cited by 99 publications

(60 citation statements)

References 46 publications

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“…However, it is important to note that aerosols can influence the NDVI, with NDVI values typically decreasing as AOD increases, thus limiting its potential to evaluate surface conditions [1,22]. To overcome this drawback, other vegetation indices that are not sensitive to atmospheric aerosols could replace the NDVI.…”

Section: Aeronet Aod Datamentioning

confidence: 99%

“…Compared with using the linear relationship of 1.6 vs. 2.1 directly, as shown in Figure 1a, it is possible to improve the accuracy of surface reflectance estimation through an NDVI-based correlation of 1.6 vs. 2.1. However, it is important to note that aerosols can influence the NDVI, with NDVI values typically decreasing as AOD increases, thus limiting its potential to evaluate surface conditions [1,22]. To overcome this drawback, other vegetation indices that are not sensitive to atmospheric aerosols could replace the NDVI.…”

Section: Aeronet Aod Datamentioning

confidence: 99%

“…In the MODIS DT algorithm, the influence of aerosols on the SWIR 2.1 µm channel is negligible (except for heavy aerosol or dusts), so the correlations of reflectance in the 0.47, 0.65, and 2.1 µm channels are used to estimate the surface reflectance. Although highly accurate estimations can be made over low reflectance areas, the DT algorithm is limited by surface conditions and cannot be used over bright-reflection regions, such as cities, deserts, or semi-desert regions; this is because the surface reflectance signals over these regions are too large, which makes it difficult to discriminate aerosol contributions from satellite level TOA signals [1,26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Atmospheric aerosols are solid particles or liquid droplets suspended in the atmosphere with diameters ranging from 10 −3 to 10 2 micrometers [1]. Aerosol is a major component of the atmosphere and plays an important role in global environmental and climate change [2].…”

Section: Introductionmentioning

confidence: 99%

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A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

et al. 2017

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Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 µm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI 2.1 )-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI 2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AOD AERONET ).

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Section: Aeronet Aod Datamentioning

confidence: 99%

Section: Aeronet Aod Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

et al. 2017

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“…However, detection of thin cloud or subpixel cloud are difficult, and there is also uncertainly in the aerosol effect correction [31]. Following the approach in the paper by Sun et al [32], we adopted the criterion of minimum land surface reflectance (MLSR) for database synthesis because it is the most efficient way to avoid cloud contamination. In each 500 m pixel, the lowest surface reflectance value in the twenty images time series, i.e., four images per month for 5 successive years, was identified as the clearest observation.…”

Section: Construction Of Surface Databasementioning

confidence: 99%

Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

et al. 2018

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Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of~0.928, root mean square error (RMSE) of~0.042, mean absolute error (MAE) of~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.

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A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database

et al. 2016

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Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long-time-sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear-sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long-term, large-scale cloud detection operations.

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Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

Cited by 99 publications

References 46 publications

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database

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