Continuing the MODIS Dark Target Aerosol Time Series with VIIRS

Abstract

For reflected sunlight observed from space at visible and near-infrared wavelengths, particles suspended in Earth’s atmosphere provide contrast with vegetation or dark water at the surface. This is the physical motivation for the Dark Target (DT) aerosol retrieval algorithm developed for the Moderate Resolution Imaging Spectrometer (MODIS). To extend the data record of aerosol optical depth (AOD) beyond the expected 20-year lifespan of the MODIS sensors, DT must be adapted for other sensors. A version of the DT AOD retrieval for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership (SNPP) is now mature enough to be released as a standard data product, and includes some upgraded features from the MODIS version. Differences between MODIS Aqua and VIIRS SNPP lead to some inevitable disagreement between their respective AOD measurements, but the offset between the VIIRS SNPP and MODIS Aqua records is smaller than the offset between those of MODIS Aqua and MODIS Terra. The VIIRS SNPP retrieval shows good agreement with ground-based measurements. For most purposes, DT for VIIRS SNPP is consistent enough and in close enough agreement with MODIS to continue the record of satellite AOD. The reasons for the offset from MODIS Aqua, and its spatial and temporal variability, are investigated in this study.

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

“…After 30 years, the DT algorithm is well on its path forward, expanding to other sensors beyond MODIS. Currently a version of DT is running operationally using VIIRS observations as inputs [24,148] and a prototype DT algorithm has been applied to the Advanced Himawari Imager (AHI) [149] and the two Advanced Baseline Imagers (ABIs) currently in geosynchronous orbit on the Himawari, GOES-E, and GOES-W satellites [271]. The goal is to provide aerosol data users with a comprehensive view of the global aerosol system, combining the global coverage of polar-orbiting satellites in low Earth orbit (LEO) with the high temporal coverage of sensors in geosynchronous orbit (GEO).…”

“…It also included looking ahead to when the MODIS sensors would finish their missions and the community would need to switch to alternative sensors. In light of that need for continuity, the DT algorithm has been successfully ported to the VIIRS sensor [24] and is in the process of being adapted for the current fleet of geosynchronous sensors. Throughout this decade, as was the case for the 20 years previously, AERONET has been an essential partner in the continuous development, validation, and maintenance of the DT algorithm.…”

“…The original algorithm, termed Dark Target (DT) [15] for its use of dark surfaces as targets of opportunity to measure the contrast of the bright aerosol above, has undergone continuous modifications and improvements over the two decades it has been in operation [15][16][17][18][19][20][21][22][23][24]. These changes were implemented as remote sensing science progressed, as validation identified areas of concern that could be remedied, and as the instruments themselves aged.…”

Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

“…AOD from polar-orbiting satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), is retrieved using multi-channel algorithms (Levy et al, 2007(Levy et al, , 2010Sayer et al, 2014;Jackson et al, 2013;Liu et al, 2014;Laszlo and Liu, 2016). As a result, AOD from MODIS and VIIRS has high accuracy, e.g., MODIS dark target AOD has an expected error of ± (0.05 + 15 %) over land (Levy et al, 2013) and VIIRS AOD developed at the National Oceanic and Atmospheric Administration (NOAA) has a bias of 0.02 and standard deviation of H. Zhang et al: ABI AOD bias correction algorithm error of 0.11 (Laszlo and Liu, 2016), but the low temporal resolution of polar-orbiting satellites limits the availability of observations for a given location.…”

“…The accuracy and precision of VIIRS and MODIS AOD is well documented for use in various decision support systems (Laszlo and Liu, 2016;Sawyer et al, 2020;Levy et al, 2013;Sayer et al, 2014). The geometries of observations from a geostationary satellite are quite different from a polar-orbiting satellite; this can lead to differences in the quality of retrieved AOD despite the similarity of the AOD retrieval algorithms.…”