Updated MISR dark water research aerosol retrieval algorithm – Part 1: Coupled 1.1 km ocean surface chlorophyll <i>a</i> retrievals with empirical calibration corrections

Abstract

Abstract. As aerosol amount and type are key factors in the "atmospheric correction" required for remote-sensing chlorophyll a concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites.We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl in situ < 1.5 mg m −3 , the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-meansquare error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample KolmogorovSmirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p > 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl < 1.5 mg m −3 , MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15.The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

A study of 15-year aerosol optical thickness and direct shortwave aerosol radiative effect trends using MODIS, MISR, CALIOP and CERES

“…A negligible AOT trend (0.0003 AOT decade −1 ) is found using Aqua C6 MODIS DT data, but a higher AOT trend of 0.008 AOT decade −1 is found using Terra C6 MODIS DT data, while a slight negative trend is derived using MISR data (−0.005 AOT decade −1 ). It is suspected that the difference may be introduced by calibration-related issues for one or all sensors, such as the recently reported cross-talk in thermal channels for Terra MODIS (Moeller and Frey, 2016) and a slight decrease in signal sensitivity for Terra MISR (Limbacher and Kahn, 2017). After accounting for potential calibration drifts, negligible AOT trends are found over global oceans using data from all sensors.…”

“…For example, a recent study suggests potential cross-talk among Terra MODIS thermal channels, which will affect MODIS cloud detection (Moeller and Frey, 2016) and, correspondingly, Terra MODIS AOT trends. Similarly, Limbacher and Kahn (2017) reported an up to 2 % decrease in MISR signals from 2002 to 2014 that could affect MISR AOT trends. AOT trends estimated from this study are henceforth adjusted based on AOT trends detected from the remote-ocean region; this is done to reduce potential impacts from upstream data used in the AOT retrievals by assuming that a near-zero AOT trend should be observed over the remote-ocean region (shown in Table 3).…”

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

“…These include quantization effects apparent in the distribution of reported AODs, a gap in the retrieved AOD values between 0.00 and 0.02, a lack of several aerosol component optical analogues and mixtures in the algorithm climatology that are common in the atmosphere, and a systematic underestimation of the AOD for mid-visible AOD values above about 0.4 that is related at least in part to surface boundary conditions. Part of the motivation for the development of the MISR V23 aerosol product was to address several of these issues, while other issues are being explored with the MISR research algorithm (e.g., Limbacher and Kahn, 2019).…”

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

“…Satellite remote sensing, as the most direct and effective way to capture the spatial distributions and temporal variations of aerosols over large regions, has been widely used for researches at the local and global scales compared with ground-based Sun photometers (Garcia et al, 2016;Kahn, 2013;Kokhanovsky et al, 2007;Li et al, 2005;Noh et al, 2009;Qin et al, 2016;Sun et al, 2016;Veefkind et al, 1999;Zheng et al, 2017). During past 20 years, a series of sensors have been launched and used in aerosol related researches over land and ocean, such as National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (Geogdzhayev et al, 2004;Hsu et al, 2017), Multi-angle Imaging SpectroRadiometer (Diner et al, 2005;Limbacher & Kahn, 2017), Advanced Along Track Scanning Radiometer (Grey et al, 2006;Guo et al, 2009;Mei et al, 2013;Xue et al, 2009), Polarization and Directionality of the Earth's Reflectance (Deuze et al, 2001), Sea-Viewing Wide Field-Of-View Sensor (Melin et al, 2007;Sayer et al, 2012), Landsat Operational Land Imager (Tian et al, 2018), MODIS (MODerate-Resolution Imaging Spectroradiometer; Levy et al, 2007), VIIRS (Visible Infrared Imaging Radiometer Suite; Jackson et al, 2013;Su et al, 2015;Zhang et al, 2016), and Chinese FengYun Medium Resolution Spectral Imager (Han et al, 2015;Tong et al, 2011). One of the main challenges for the AOD retrieval using satellites is to isolate aerosol particle scattering contributions from satellite recorded signals, which is the superposition of atmospheric path reflectance including atmospheric molecules and aerosol matters as well as surface reflectance signals .…”