Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

Abstract

Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, combined with the Earth's curvature, results in a pixel shape distortion known as the "bow-tie effect". Specifically, sensor pixels near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ∼ 9 and, additionally, the overlap of pixels acquired from consecutive scans. The Deep Blue and Dark Target aerosol optical depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bow-tie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30• , corresponding to approximately 50 % of the data, are areally enlarged by a factor of 50 % or more compared to this nominal pixel area and are not spatially independent of each other. This has implications for retrieval uncertainty and aggregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bow-tie distortion. Additionally, the pixel overlap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the overlap between L2 pixels and slows the rate of growth of L2 pixel size vs. VZA. This can be achieved without significant changes to existing MODIS processing algorithms. The second step involves additionally changing the number of sensor pixels aggregated across-track as a function of VZA, which preserves L2 pixel size at around 10 km × 10 km across the whole swath but would require algorithmic quality assurance tests to be re-evaluated. Both of these steps also improve the extent to which the pixel locations a user would infer from the L2 data products represent the actual spatial extent of the L2 pixels.

How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

“…This distortion at the edge of the swath is about a factor of 2 in the along-track and 5 in the across-track direction (i.e. 10-fold increase in pixel area), and overlap is close to 100 %, which has consequences for AOD retrieval characteristics (Sayer et al, 2015). For about half of the swath, however, the areal expansion is less than a factor of 2 compared to the nominal 10 km × 10 km pixel size.…”

“…The C61 DT land algorithm is similar to that of the previous Collection 6 (C6; Levy et al, 2013) but implements an updated surface reflectance model, detailed in Gupta et al (2016), to reduce a systematic positive bias of DT over urban surfaces. The C61 DB data include numerous small updates to surface and aerosol models and cloud and quality assurance (QA) tests to reduce known error sources Sayer et al, 2019). All three algorithms also benefit from sensor calibration updates.…”

“…As with the daily data, the magnitude of the offset is AOD-dependent; the magnitude is, however, less consistent than for the daily results, with the median offset being largest for AERONET. This might in part reflect known tendencies for a high bias in low-AOD conditions and/or low bias in high-AOD conditions in these satellite products (Kahn et al, 2010;Eck et al, 2013;Levy et al, 2013;Sayer et al, 2019), meaning that the difference τ l −τ n is dampened due to diminished spatial and temporal variability within and between days. The asymmetry of the variabilities (vertical lines) in both panels of Fig.…”

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

“…They measure reflected solar and emitted thermal radiation in 36 bands in the spectral range 412 nm to 14.3 μm, with nominal horizontal pixel sizes at the center of swath from 250 m to 1 km (dependent on band). Due to MODIS's scan geometry and the shape of the Earth pixel sizes become increasingly larger, and shapes distorted, for off‐nadir view angles (see Sayer, Hsu, & Bettenhausen, , for the impacts of this so‐called “bow tie effect” on the aerosol products). Several algorithms have been developed and applied by NASA to provide AOD at 550 nm as part of MODIS routine data processing.…”

Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land

“…Away from nadir views, the sensor scan geometries and Earth's curvature cause a “bow‐tie distortion” where pixels become larger, and consecutive scans begin to overlap, at larger view zenith angles (Wolfe et al, ; Xiong et al, ). For MODIS, the distortion at edge of the 2,330‐km swath is about a factor of two in the along‐track and five in the across‐track direction, and overlap is total, which has consequences for AOD retrieval characteristics (Sayer, Hsu, & Bettenhausen, ). For VIIRS, an onboard reaggregation and bow‐tie deletion system means that distortion is closer to a factor of two and overlap is much smaller (Wolfe et al, ), even at the edge of its 3,040‐km swath.…”