Aerosol Retrieval and Atmospheric Correction

1996 ◽  
pp. 93-124 ◽  
Author(s):  
E. Vermonte ◽  
Nazmi Saleous ◽  
B. N. Holben
Keyword(s):  
Atmospheric Correction ◽  
Aerosol Retrieval

Aerosol retrieval over land from AVHRR data-application for atmospheric correction

1992 ◽  
Vol 30 (2) ◽  
pp. 212-222 ◽  
Author(s):  
B. Holben ◽  
E. Vermote ◽  
Y.J. Kaufman ◽  
D. Tanre ◽  
V. Kalb
Keyword(s):  
Atmospheric Correction ◽  
Aerosol Retrieval ◽  
Data Application ◽  
Avhrr Data

Improvements in Aerosol Retrieval for Atmospheric Correction

2008 ◽  
Author(s):  
S. M. Adler-Golden ◽  
M. W. Matthew ◽  
A. Berk ◽  
M. J. Fox ◽  
J. Lee ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Aerosol Retrieval

scholarly journals Impact of cumulus cloud spacing on Landsat atmospheric correction and aerosol retrieval

2001 ◽  
Vol 106 (D11) ◽  
pp. 12129-12138 ◽  
Author(s):  
Guoyong Wen ◽  
Robert F. Cahalan ◽  
Si-Chee Tsay ◽  
Lazaros Oreopoulos
Keyword(s):  
Atmospheric Correction ◽  
Cumulus Cloud ◽  
Aerosol Retrieval

scholarly journals Evaluation and comparison of MAIAC, DT and DB aerosol products over China

2019 ◽  
Author(s):  
Ning Liu ◽  
Bin Zou ◽  
Huihui Feng ◽  
Yuqi Tang ◽  
Yu Liang
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Coefficient Of Determination ◽  
Land Type ◽  
Deep Blue ◽  
Aerosol Retrieval ◽  
Geometry Dependence ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. A new Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm has been applied in Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and recently provides globally high spatial resolution Aerosol Optical Depth (AOD) products at 1 km. Meanwhile, several improvements are modified in classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS collection 6.1 products. However, validation and comparison for MAIAC, DT and DB algorithms is still lacking in China. In this paper, a comprehensive assessment and comparison of AOD products at 550 nm wavelength based three aerosol retrieval algorithms in MODIS sensor using ground-truth measurements from Aerosol Robotic Network (AERONET) sites over China during 2000 to 2017 is presented. In general, after quality assurance (QA) filter, the coefficient of determination (R2=0.854), correlation coefficient (R=0.929), root-mean-square error (RMSE=0.178), mean bias (Bias=0.019) and the fraction fall within expected error (Within_EE=67.10 %, EE=±(0.05+0.15×AOD)) results for MAIAC algorithm show better accuracy than those from DT and DB algorithms. While the R2, R, RMSE, Bias and Within_EE of DT algorithm are 0.817, 0.930, 0.192, 0.077, 55.36 %, respectively, those corresponding statistics for DB algorithm are 0.827, 0.921, 0.190, 0.018, 63.32 %. Moreover, the spatiotemporal completeness for MAIAC (29.69 %) product is also better than DT (8.00 %) and DB (19.50 %) products after QA filter. In addition, the land type dependence characteristic, view geometry dependence, spatiotemporal retrieval accuracy and spatial variation pattern difference for three products are also analyzed in details.

scholarly journals Updated MISR Over-Water Research Aerosol Retrieval Algorithm Part 2: A Multi- Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

2018 ◽  
Author(s):  
James A. Limbacher ◽  
Ralph A. Kahn
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Surface ◽  
Retrieval Algorithm ◽  
Biological Productivity ◽  
Single View ◽  
Aerosol Retrieval ◽  
Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources; they are also some of the most biologically productive on the planet. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., Chlorophyll-a concentration) to in-situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity/turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to NDVI over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AERosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD  0.20, the ANG RMSE is 0.25 and r = 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, MISR RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sun glint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as MODIS. This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view angle retrieval algorithms struggle to separate atmospheric from oceanic features.

scholarly journals Updated MISR over-water research aerosol retrieval algorithm – Part 2: A multi-angle aerosol retrieval algorithm for shallow, turbid, oligotrophic, and eutrophic waters

2019 ◽  
Vol 12 (1) ◽  
pp. 675-689 ◽  
Author(s):  
James A. Limbacher ◽  
Ralph A. Kahn
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Surface ◽  
Retrieval Algorithm ◽  
Biological Productivity ◽  
Single View ◽  
Aerosol Retrieval ◽  
Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient-rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll a concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Ångström exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD < 1.0, the root-mean-square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD > 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Ångström exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

scholarly journals Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

2019 ◽  
Vol 19 (12) ◽  
pp. 8243-8268 ◽  
Author(s):  
Ning Liu ◽  
Bin Zou ◽  
Huihui Feng ◽  
Wei Wang ◽  
Yuqi Tang ◽  
...  
Keyword(s):  
Zenith Angle ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
The North ◽  
Deep Blue ◽  
Aerosol Retrieval ◽  
Angle Scattering ◽  
Retrieval Algorithms ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Types

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study

2008 ◽  
Vol 47 (13) ◽  
pp. 2215 ◽  
Author(s):  
Svetlana Y. Kotchenova ◽  
Eric F. Vermote ◽  
Robert Levy ◽  
Alexei Lyapustin
Keyword(s):  
Radiative Transfer ◽  
Atmospheric Correction ◽  
Aerosol Retrieval ◽  
Intercomparison Study

scholarly journals Improved cloud and snow screening in MAIAC aerosol retrievals using spectral and spatial analysis

2012 ◽  
Vol 5 (4) ◽  
pp. 843-850 ◽  
Author(s):  
A. Lyapustin ◽  
Y. Wang ◽  
I. Laszlo ◽  
S. Korkin
Keyword(s):  
Data Analysis ◽  
Spatial Analysis ◽  
Spatial Resolution ◽  
Optical Thickness ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Atmospheric Correction ◽  
Strong Suppression ◽  
Aerosol Retrieval ◽  
Resolution Data

Abstract. An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.

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