scholarly journals Adaption of the MODIS aerosol retrieval algorithm by airborne spectral surface reflectance measurements over urban areas: a case study

2015 ◽  
Vol 8 (7) ◽  
pp. 7335-7371
Author(s):  
E. Jäkel ◽  
B. Mey ◽  
R. Levy ◽  
X. Gu ◽  
T. Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spectral Characteristics ◽  
Mean Difference ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Beijing Area ◽  
Airborne Measurements ◽  
Aerosol Retrieval ◽  
Modis Aod

Abstract. MODIS retrievals of the aerosol optical depth (AOD) are biased over urban areas, where surface reflectance is not well characterized. Since the operational MODIS aerosol retrieval for dark targets assumes fixed spectral slopes to calculate the surface reflectance at 0.47 μm, the algorithm may fail in urban areas with different spectral characteristics of the surface reflectance. To investigate this bias we have implemented variable spectral slopes into the operational MODIS aerosol algorithms of Collection 5 (C5) and C6. The variation of slopes is based on airborne measurements of surface reflectances over the city of Zhongshan, China. AOD retrieval results of the operational and the modified algorithms were compared for a MODIS measurement over Zhongshan. For this case slightly lower AOD values were derived using the modified algorithm. The retrieval methods were additionally applied to MODIS data of the Beijing area for a period between 2010–2014 when also AERONET data were available. A reduction of the differences between the AOD retrieved using the modified C5 algorithm and AERONET was found, whereby the mean difference from 0.31 ± 0.11 for the operational C5 and 0.18 ± 0.12 for the operational C6 where reduced to a mean difference of 0.09 ± 0.18 by using the modified C5 retrieval. Furthermore, the sensitivity of the MODIS AOD retrieval for several surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectances were used as input for the retrieval methods. It is shown that the operational MODIS AOD retrieval over land reproduces the AOD reference input of 0.85 for dark surface types [retrieved AOD = 0.87 (C5)]. An overestimation of AOD = 0.99 is found for urban surfaces, whereby the modified C5 algorithm shows a good performance with a retrieved value of AOD = 0.86.

scholarly journals Adaption of the MODIS aerosol retrieval algorithm using airborne spectral surface reflectance measurements over urban areas: a case study

2015 ◽  
Vol 8 (12) ◽  
pp. 5237-5249 ◽  
Author(s):  
E. Jäkel ◽  
B. Mey ◽  
R. Levy ◽  
X. Gu ◽  
T. Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Airborne Measurements ◽  
Modis Aod ◽  
Urban Sample ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. MODIS (MOderate-resolution Imaging Spectroradiometer) retrievals of aerosol optical depth (AOD) are biased over urban areas, primarily because the reflectance characteristics of urban surfaces are different than that assumed by the retrieval algorithm. Specifically, the operational "dark-target" retrieval is tuned towards vegetated (dark) surfaces and assumes a spectral relationship to estimate the surface reflectance in blue and red wavelengths. From airborne measurements of surface reflectance over the city of Zhongshan, China, were collected that could replace the assumptions within the MODIS retrieval algorithm. The subsequent impact was tested upon two versions of the operational algorithm, Collections 5 and 6 (C5 and C6). AOD retrieval results of the operational and modified algorithms were compared for a specific case study over Zhongshan to show minor differences between them all. However, the Zhongshan-based spectral surface relationship was applied to a much larger urban sample, specifically to the MODIS data taken over Beijing between 2010 and 2014. These results were compared directly to ground-based AERONET (AErosol RObotic NETwork) measurements of AOD. A significant reduction of the differences between the AOD retrieved by the modified algorithms and AERONET was found, whereby the mean difference decreased from 0.27±0.14 for the operational C5 and 0.19±0.12 for the operational C6 to 0.10±0.15 and -0.02±0.17 by using the modified C5 and C6 retrievals. Since the modified algorithms assume a higher contribution by the surface to the total measured reflectance from MODIS, consequently the overestimation of AOD by the operational methods is reduced. Furthermore, the sensitivity of the MODIS AOD retrieval with respect to different surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectance data were used as input for the retrieval methods. It was shown that the operational MODIS AOD retrieval over land reproduces the AOD reference input of 0.85 for dark surface types (retrieved AOD = 0.87 (C5)). An overestimation of AOD = 0.99 is found for urban surfaces, whereas the modified C5 algorithm shows a good performance with a retrieved value of AOD = 0.86.

scholarly journals Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

2018 ◽  
Vol 11 (3) ◽  
pp. 1529-1547 ◽  
Author(s):  
Antti Lipponen ◽  
Tero Mielonen ◽  
Mikko R. A. Pitkänen ◽  
Robert C. Levy ◽  
Virginia R. Sawyer ◽  
...  
Keyword(s):  
Computation Time ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Correlation Models ◽  
Modis Aod ◽  
Uncertainty Estimates ◽  
Average Computation Time ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Parameters

Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

scholarly journals Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

2022 ◽  
Vol 14 (2) ◽  
pp. 373
Author(s):  
Muhammad Bilal ◽  
Alaa Mhawish ◽  
Md. Arfan Ali ◽  
Janet E. Nichol ◽  
Gerrit de Leeuw ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Dust Storms ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Haze Pollution

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and -0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

scholarly journals The GRAPE aerosol retrieval algorithm

2009 ◽  
Vol 2 (2) ◽  
pp. 679-701 ◽  
Author(s):  
G. E. Thomas ◽  
C. A. Poulsen ◽  
A. M. Sayer ◽  
S. H. Marsh ◽  
S. M. Dean ◽  
...  
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Data Set ◽  
Cloud Parameters ◽  
Aerosol Retrieval ◽  
Retrieval Scheme ◽  
Theoretical Performance

Abstract. The aerosol component of the Oxford-Rutherford Aerosol and Cloud (ORAC) combined cloud and aerosol retrieval scheme is described and the theoretical performance of the algorithm is analysed. ORAC is an optimal estimation retrieval scheme for deriving cloud and aerosol properties from measurements made by imaging satellite radiometers and, when applied to cloud free radiances, provides estimates of aerosol optical depth at a wavelength of 550 nm, aerosol effective radius and surface reflectance at 550 nm. The aerosol retrieval component of ORAC has several incarnations – this paper addresses the version which operates in conjunction with the cloud retrieval component of ORAC (described by Watts et al., 1998), as applied in producing the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) data-set. The algorithm is described in detail and its performance examined. This includes a discussion of errors resulting from the formulation of the forward model, sensitivity of the retrieval to the measurements and a priori constraints, and errors resulting from assumptions made about the atmospheric/surface state.

scholarly journals A sea surface reflectance model for (A)ATSR, and application to aerosol retrievals

2010 ◽  
Vol 3 (2) ◽  
pp. 1023-1098 ◽  
Author(s):  
A. M. Sayer ◽  
G. E. Thomas ◽  
R. G. Grainger
Keyword(s):  
A Priori ◽  
Sea Surface ◽  
Retrieval Algorithm ◽  
Bidirectional Reflectance ◽  
Near Ir ◽  
Aerosol Retrieval ◽  
Modis Aod ◽  
Retrieval Scheme ◽  
Along Track ◽  
Bidirectional Reflectance Distribution

Abstract. A model of the sea surface bidirectional reflectance distribution function (BRDF) is presented for the visible and near-IR channels (over the spectral range 550 nm to 1.6 μm) of the dual-viewing Along-Track Scanning Radiometers (ATSRs). The intended application is as part of the Oxford-RAL Aerosols and Clouds (ORAC) retrieval scheme. The model accounts for contributions to the observed reflectance from whitecaps, sun-glint and underlight. Uncertainties in the parametrisations used in the BRDF model are propagated through into the forward model and retrieved state. The new BRDF model offers improved coverage over previous methods, as retrievals are possible into the sun-glint region, through the ATSR dual-viewing system. The new model has been applied in the ORAC aerosol retrieval algorithm to process Advanced ATSR (AATSR) data from September 2004 over the south-eastern Pacific. The assumed error budget is shown to be generally appropriate, meaning the retrieved states are consistent with the measurements and a priori assumptions. The resulting field of aerosol optical depth (AOD) is compared with colocated MODIS-Terra observations, AERONET observations at Tahiti, and cruises over the oceanic region. MODIS and AATSR show similar spatial distributions of AOD, although MODIS reports values which are larger and more variable. It is suggested that assumptions in the MODIS aerosol retrieval algorithm may lead to a positive bias in MODIS AOD of order 0.01 at 550 nm over ocean regions where the wind speed is high.

scholarly journals Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China

2021 ◽  
Vol 13 (18) ◽  
pp. 3752
Author(s):  
Zhendong Sun ◽  
Jing Wei ◽  
Ning Zhang ◽  
Yulong He ◽  
Yu Sun ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Zenith Angle ◽  
Rural Areas ◽  
Urban Areas ◽  
Eastern China ◽  
Practical Significance ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Surface Reflectance

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.

scholarly journals GOCI Yonsei aerosol retrieval version 2 aerosol products: improved algorithm description and error analysis with uncertainty estimation from 5-year validation over East Asia

2017 ◽  
Author(s):  
Myungje Choi ◽  
Jhoon Kim ◽  
Jaehwa Lee ◽  
Mijin Kim ◽  
Young-je Park ◽  
...  
Keyword(s):  
East Asia ◽  
Infrared Imaging ◽  
Observation Time ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Observation Network ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Improved Accuracy

Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed for retrieving hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD showed comparable accuracy compared to ground-based and other satellite-based observations, but still had errors due to uncertainties in surface reflectance and simple cloud masking. Also, it was not capable of near-real-time (NRT) processing because it required a monthly database of each year encompassing the day of retrieval for the determination of surface reflectance. This study describes the improvement of GOCI YAER algorithm to the version 2 (V2) for NRT processing with improved accuracy from the modification of cloud masking, surface reflectance determination using multi-year Rayleigh corrected reflectance and wind speed database, and inversion channels per surface conditions. Therefore, the improved GOCI AOD (τG) is similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD compared to V1 of the YAER algorithm. The τG shows reduced median bias and increased ratio within 0.15τA+0.05 range (i.e. absolute expected error range of MODIS AOD) compared to V1 in the validation results using Aerosol Robotic Network (AERONET) AOD (τA) from 2011 to 2016. The validation using the Sun-Sky Radiometer Observation Network (SONET) over China also shows similar results. The bias of error (τG-τA) is within −0.1 and 0.1 range as a function of AERONET AOD and AE, scattering angle, NDVI, cloud fraction and homogeneity of retrieved AOD, observation time, month, and year. Also, the diagnostic and prognostic expected error (DEE and PEE, respectively) of τG are estimated. The estimated multiple PEE of GOCI V2 AOD is well matched with actual error over East Asia, and the GOCI V2 AOD over Korea shows higher ratio within PEE compared to over China and Japan.

scholarly journals TWO VIEWING THEORY ON ATMOSPHERE CORRECTION IN OCEAN COLOR ALGORITHM

2010 ◽  
Vol 3 (-) ◽  
Author(s):  
Sisir Kumar Dash ◽  
Tasuku Tanaka ◽  
Ryutaro Tateishi
Keyword(s):  
Optical Thickness ◽  
Spectral Characteristics ◽  
Ocean Color ◽  
Simultaneous Equation ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Independent Data ◽  
Atmosphere Correction ◽  
Forward Calculation ◽  
Reflectance Data

A new algorithm for retrieving optical thickness and surface reflectance, data in the visible bands from satellites is developed. The proposed algorithm is to solve the simultaneous equation of two unknown variables, i.e. aerosol optical thickness and surface reflectance (r). In term of difference from the conventional and, one directional retrieval algorithm, we do not need the spectral characteristics of aerosol. We solve the equation by forward calculation using the 6S transfer code. The two observational equations change linearly within the domain where we solve the solution. We estimate the chlorophyll-a concentration from the evaluated r. This method is validated against Global Imager (GLI) data, which has two independent data for one pixel in both tilting and nadir viewing. Keywords, GLI, 6S, Radiative Transfer, Reflectance, Rayleigh, Mie.

A New Aerosol Retrieval Algorithm for Landsat 8 OLI Images over Urban Areas

2021 ◽  
Author(s):  
Yue Yang ◽  
Yunping Chen ◽  
Kangzhuo Yang ◽  
Yan Chen ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Urban Areas ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Aerosol Retrieval

scholarly journals Identifying, Examining, and Planning Areas Protected from Light Pollution. The Case Study of Planning the First National Dark Sky Park in Greece

2019 ◽  
Vol 11 (21) ◽  
pp. 5963 ◽  
Author(s):  
Andreas Papalambrou ◽  
Lambros T. Doulos
Keyword(s):  
Urban Areas ◽  
Spectral Characteristics ◽  
Natura 2000 ◽  
Light Pollution ◽  
Wide Field ◽  
Park Planning ◽  
Complete Study ◽  
Design Selection ◽  
Sparse Population

Light pollution is a type of pollution that climaxes in cities and occurs increasingly away from them, due to the increase of artificial lighting and inappropriate lighting design (selection of luminaires, aiming, illuminance/luminance levels, and spectral characteristics). Increasingly, light pollution also affects the countryside due to local lighting but also distant lighting propagating from urban areas. This has a significant impact on ecosystems and astronomical observing sites. This work analyzes the main facts about light pollution (causes, impact, and solutions) and studies the methods, parameters, and special requirements for planning of light pollution protected areas. This dark sky park planning methodology is implemented as a case study in mount Parnon which has been selected due to its significance as a Natura 2000 protected area and because it is Greece’s most popular astronomical observing site. Mount Parnon is located close to two major cities as well as significant highways, however the site itself remains dark due to its sparse population. Planning a dark sky park involves a complete study of facts regarding the specific site. Existing lighting installations are surveyed in detail by recording types of luminaires and lamps and recording their positions in a map. Lighting illuminance levels are measured by photometers and spectra are analyzed using a spectrometer. Sky brightness levels measurements are performed using specialized photometers and light pollution origins are traced using wide-field photography. Finally, a proposal is made for a dark sky park scheme suited to the specific case of the site.

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