Articles | Volume 8, issue 12
https://doi.org/10.5194/amt-8-5277-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-5277-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Dec 2015
Research article |
| 17 Dec 2015
Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Goddard Earth Sciences Technology And Research (GESTAR), Universities Space Research Association (USRA), Columbia, Maryland, USA
N. C. Hsu
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
C. Bettenhausen
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Systems and Applications Inc., Lanham, Maryland, USA
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23 citations as recorded by crossref.
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22 citations as recorded by crossref.
- On the Abundance and Common Properties of Continental, Organized Shallow (Green) Clouds T. Dror et al. 10.1109/TGRS.2020.3023085
- Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies A. Sayer et al. 10.1002/2015JD024729
- Development of an operational land water mask for MODIS Collection 6, and influence on downstream data products M. Carroll et al. 10.1080/17538947.2016.1232756
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- Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land A. Sayer et al. 10.1029/2018JD029598
- Exploring the Spatiotemporal Coverage of Terrestrial Snow Mass Using a Suite of Satellite Constellation Configurations L. Wang et al. 10.3390/rs14030633
- Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project A. Sayer et al. 10.1002/2017JD027412
- Enhancement of Nighttime Fire Detection and Combustion Efficiency Characterization Using Suomi-NPP and NOAA-20 VIIRS Instruments M. Zhou et al. 10.1109/TGRS.2023.3261664
- Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites P. Gupta et al. 10.5194/amt-17-5455-2024
- Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016 Y. Wang et al. 10.3390/rs15184462
- Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data A. Sayer et al. 10.5194/amt-12-3595-2019
- Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
- Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019 S. Muhammad & A. Thapa 10.5194/essd-13-767-2021
- An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
- Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenes A. Sayer et al. 10.5194/amt-10-1425-2017
- How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth A. Sayer & K. Knobelspiesse 10.5194/acp-19-15023-2019
- A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area V. Premier et al. 10.1109/JSTARS.2021.3103585
- Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission X. Ye et al. 10.1016/j.scitotenv.2022.161055
- Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. 10.1029/2018JD028313
- Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory N. Borodai et al. 10.1051/epjconf/201919702011
- Ocean Color Hyperspectral Remote Sensing With High Resolution and Low Latency—The HYPSO-1 CubeSat Mission M. Grotte et al. 10.1109/TGRS.2021.3080175
1 citations as recorded by crossref.
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Short summary
MODIS is a satellite sensor widely used in Earth science. Its scanning geometry results in a distortion called the ‘bow-tie effect’, which means that, depending on the location of a pixel relative to the satellite ground track, the size and shape of the pixel may be distorted. This affects data such as aerosol optical depth (AOD) derived from the measurements. This paper illustrates the bow-tie disortion’s effect on AOD and presents techniques to restore AOD data products to a more uniform grid
MODIS is a satellite sensor widely used in Earth science. Its scanning geometry results in a...
