@Article{WatanabeMiAsRoAlImBa:2016:PaCaQu,
author = "Watanabe, Fernanda and Mishra, Deepak R. and Astuti, Ike and
Rodrigues, Thanan and Alc{\^a}ntara, Enner and Imai, Nilton N.
and Barbosa, Cl{\'a}udio Clemente Faria",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {University of
Georgia} and {University of Georgia} and {Universidade Estadual
Paulista (UNESP)} and {Universidade Estadual Paulista (UNESP)} and
{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)}",
title = "Parametrization and calibration of a quasi-analytical algorithm
for tropical eutrophic waters",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2016",
volume = "121",
pages = "28--47",
month = "Nov.",
keywords = "Algal bloom, Bio-optical model, Inherent optical properties,
Inland waters, Quasi-analytical algorithm, Remote sensing
reflectance.",
abstract = "Quasi-analytical algorithm (QAA) was designed to derive the
inherent optical properties (IOPs) of water bodies from
above-surface remote sensing reflectance (Rrs). Several variants
of QAA have been developed for environments with different
bio-optical characteristics. However, most variants of QAA suffer
from moderate to high negative IOP prediction when applied to
tropical eutrophic waters. This research is aimed at parametrizing
a QAA for tropical eutrophic water dominated by cyanobacteria. The
alterations proposed in the algorithm yielded accurate absorption
coefficients and chlorophyll-a (Chl-a) concentration. The main
changes accomplished were the selection of wavelengths
representative of the optically relevant constituents (ORCs) and
calibration of values directly associated with the pigments and
detritus plus colored dissolved organic material (CDM) absorption
coefficients. The re-parametrized QAA eliminated the retrieval of
negative values, commonly identified in other variants of QAA. The
calibrated model generated a normalized root mean square error
(NRMSE) of 21.88% and a mean absolute percentage error (MAPE) of
28.27% for at(\λ), where the largest errors were found at
412 nm and 620 nm. Estimated NRMSE for aCDM(\λ) was 18.86%
with a MAPE of 31.17%. A NRMSE of 22.94% and a MAPE of 60.08% were
obtained for a\φ(\λ). Estimated a\φ(665) and
a\φ(709) was used to predict Chl-a concentration.
a\φ(665) derived from QAA for Barra Bonita Hydroelectric
Reservoir (QAA_BBHR) was able to predict Chl-a accurately, with a
NRMSE of 11.3% and MAPE of 38.5%. The performance of the Chl-a
model was comparable to some of the most widely used empirical
algorithms such as 2-band, 3-band, and the normalized difference
chlorophyll index (NDCI). The new QAA was parametrized based on
the band configuration of MEdium Resolution Imaging Spectrometer
(MERIS), Sentinel-2A and 3A and can be readily scaled-up for
spatio-temporal monitoring of IOPs in tropical waters.",
doi = "10.1016/j.isprsjprs.2016.08.009",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2016.08.009",
issn = "0924-2716",
language = "en",
targetfile = "watanabe_parametrization.pdf",
urlaccessdate = "15 jun. 2024"
}