Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

Abstract

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 µm AOD and 0.55/0.86 µm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODISlike (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products.Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1 • × 1 • AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ∼ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

“…The first step applies the standard procedures designed for Yanishevski-type actinometric instruments from the former Soviet Hydrometeorological Service (Kirilov et al, 1957). Data that pass this quality procedure are then evaluated following the standards set by the Baseline Solar Radiation Network, BSRN (Ohmura, 1998;Long andShi, 2006, 2008;Estevan et al, 2012).…”

“…However, compared to other areas of the world, no studies have been reported in the Caribbean region and in Cuba in particular (Papadimas et al, 2009;Mishchenko et al, 2010;Kahn et al, 2011;Bennouna et al, 2011Bennouna et al, , 2013Witte et al, 2011;Gkikas et al, 2013Gkikas et al, , 2016Levy et al, 2015).…”

“…This comparative analysis does not aim to be a validation study of the MODIS sensor since many works during the long history of the MODIS sensor on the Terra and Aqua platforms have sought to improve its features (these include Kaufman et al, 1997a, b;Tanré et al, 1997;Remer et al, 2002Remer et al, , 2005Remer et al, , 2006Hsu, et al, 2004Hsu, et al, , 2006Hsu, et al, , 2013Levy et al, 2007Levy et al, , 2009Levy et al, , 2010Levy et al, , 2013Levy et al, , 2015Sayer et al, 2013Sayer et al, , 2014 https://darktarget.gsfc.nasa.gov/atbd/overview, last access: 9 April 2018). However, compared to other areas of the world, no studies have been reported in the Caribbean region and in Cuba in particular (Papadimas et al, 2009;Mishchenko et al, 2010;Kahn et al, 2011;Bennouna et al, 2011Bennouna et al, , 2013Witte et al, 2011;Gkikas et al, 2013Gkikas et al, , 2016Levy et al, 2015).…”

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

“…The Polarization and Directionality of the Earth's Reflectance (POLDER) instrumentation on the Advanced Earth Observing Satellite 1 (ADEOS-1) can achieve polarization measurement, which allows ground contributions to be determined based on the fact that atmospheric scattering is much more polarized than surface reflection [11,17]. Dark Target (DT) is a classic and popular technique for surface reflectance estimation and has been successfully applied to MODIS [18,19], Landsat Thematic Mapper [10], and Advanced Very High Resolution Radiometer (AVHRR) [20,21], as well as several other sensors [22][23][24]. The DT approach assumes that aerosols over "dark" surfaces (such as vegetated land and dark oceans) will brighten the scene, and the aerosol signal is a major component of the top-of-atmosphere (TOA) signal in this case [24,25].…”

“…Dark Target (DT) is a classic and popular technique for surface reflectance estimation and has been successfully applied to MODIS [18,19], Landsat Thematic Mapper [10], and Advanced Very High Resolution Radiometer (AVHRR) [20,21], as well as several other sensors [22][23][24]. The DT approach assumes that aerosols over "dark" surfaces (such as vegetated land and dark oceans) will brighten the scene, and the aerosol signal is a major component of the top-of-atmosphere (TOA) signal in this case [24,25]. Thus, low surface reflectance values favor good discrimination between the surface and atmospheric contributions.…”