@Article{PahlevanMBSAABBBBGGFHHIKLLMMOOPPRCSSSSSTTVW:2021:GlAsAt,
author = "Pahlevan, Nima and Mangin, Antoine and Balasubramanian,
Sundarabalan V. and Smith, Brandon and Alikas, Krista and Arai,
Kohei and Barbosa, Cl{\'a}udio Clemente Faria and B{\'e}langer,
Simon and Binding, Caren and Bresciani, Mariano and Giardino,
Cl{\'a}udia and Gurlin, Daniela and Fan, Yongzhen and Harmel,
Tristan and Hunter, Peter and Ishikaza, Joji and Kratzer, Susanne
and Lehmann, Moritz K. and Ligi, Martin and Ma, Ronghua and
Martin-Lauzer, Fran{\c{c}}ois-R{\'e}gis and Olmanson, Leif and
Oppelt, Natascha and Pan, Yangun and Peters, Steef and Reynauld,
Nathalie and Carvalho, Lino Augusto Sander de and Simis, Stefan
and Spyrakos, Evangelos and Steinmetz, Fran{\c{c}}ois and
Stelzer, Kersint and Sterckx, Sindy and Tormos, Thierry and Tyler,
Andrew and Vanhellemont, Quinten and Warrren, Mark",
affiliation = "{NASA Goddard Space Flight Center} and ACRI-ST and {Geosensing and
Imaging Solution Consultancy} and {NASA Goddard Space Flight
Center} and {University of Tartu} and {Saga University} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universit{\'e} du Qu{\'e}bec} and {Environment and Climate
Change Canada} and {National Research Council of Italy} and
{National Research Council of Italy} and {Wisconsin Department of
Natural Resources} and {Stevens Institute of Technology} and
{G{\'e}osciences Environment Toulouse (GET)} and {University of
Stirling} and {Nagoya University} and {Stockholm University} and
{University of Waikato} and {University of Tartu} and {Chinese
Academy of Science} and ACRI-ST and {University of Minnesota} and
{Kiel University} and {Universit{\'e} du Qu{\'e}bec} and {Water
Insight} and {UR RECOVER} and {Universidade Federal do Rio de
Janeiro (UFRJ)} and {Plymouth Marine Laboratory} and {University
of Stirling} and Euratechnologies and {Brockmann Consult GmbH} and
{Flemish Institute for Technological Research (VITO)} and
{Unit{\'e} ECosyst{\`e}mes LAcustres} and {University of
Stirling} and {Royal Belgian Institute of Natural Sciences
(RBINS)} and {Plymouth Marine Laboratory}",
title = "ACIX-Aqua: A global assessment of atmospheric correction methods
for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal
waters",
journal = "Remote Sensing of Environment",
year = "2021",
volume = "258",
pages = "e112366",
month = "June",
abstract = "Atmospheric correction over inland and coastal waters is one of
the major remaining challenges in aquatic remote sensing, often
hindering the quantitative retrieval of biogeochemical variables
and analysis of their spatial and temporal variability within
aquatic environments. The Atmospheric Correction Intercomparison
Exercise (ACIX-Aqua), a joint NASA ESA activity, was initiated to
enable a thorough evaluation of eight state-of-the-art atmospheric
correction (AC) processors available for Landsat-8 and Sentinel-2
data processing. Over 1000 radiometric matchups from both
freshwaters (rivers, lakes, reservoirs) and coastal waters were
utilized to examine the quality of derived aquatic reflectances
(\ρ\̂w). This dataset originated from two sources:
Data gathered from the international scientific community
(henceforth called Community Validation Database, CVD), which
captured predominantly inland water observations, and the Ocean
Color component of AERONET measurements (AERONET-OC), representing
primarily coastal ocean environments. This volume of data
permitted the evaluation of the AC processors individually (using
all the matchups) and comparatively (across seven different
Optical Water Types, OWTs) using common matchups. We found that
the performance of the AC processors differed for CVD and
AERONET-OC matchups, likely reflecting inherent variability in
aquatic and atmospheric properties between the two datasets. For
the former, the median errors in \ρ\̂w560 and
\ρ\̂w664 were found to range from 20 to 30% for
best-performing processors. Using the AERONET-OC matchups, our
performance assessments showed that median errors within the 1530%
range in these spectral bands may be achieved. The largest
uncertainties were associated with the blue bands (25 to 60%) for
best-performing processors considering both CVD and AERONET-OC
assessments. We further assessed uncertainty propagation to the
downstream products such as near-surface concentration of
chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using
satellite matchups from the CVD along with in situ Chla and TSS,
we found that 2030% uncertainties in
\ρ\̂w490\≤\λ\≤743nm yielded 2570%
uncertainties in derived Chla and TSS products for top-performing
AC processors. We summarize our results using performance matrices
guiding the satellite user community through the OWT-specific
relative performance of AC processors. Our analysis stresses the
need for better representation of aerosols, particularly absorbing
ones, and improvements in corrections for sky- (or sun-) glint and
adjacency effects, in order to achieve higher quality downstream
products in freshwater and coastal ecosystems.",
doi = "10.1016/j.rse.2021.112366",
url = "http://dx.doi.org/10.1016/j.rse.2021.112366",
issn = "0034-4257",
language = "en",
targetfile = "pahlevan_acix.pdf",
urlaccessdate = "28 mar. 2024"
}