@Article{OgashawaraACANSSK:2014:ItReBr,
author = "Ogashawara, Igor and Alc{\^a}ntara, Enner H. and Curtarelli,
Marcelo Pedroso and Adami, Marcos and Nascimento, Renata F. F. and
Souza, Arley F. and Stech, Jose Luiz and Kampel, Milton",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Cartography
Engineering Department, State University of S{\~a}o Paulo, Rua
Roberto Simonsen 305, Presidente Prudente-SP 19060-900, Brazil and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and
Geopixel-Solu{\c{c}}{\~o}es em Geotecnologias, Rua Maestro
Egydio Pinto 190, S{\~a}o Jos{\'e} dos Campos-SP 12245-902,
Brazil and ETEP Faculdades, Avenida Bar{\~a}o Rio Branco 882,
S{\~a}o Jos{\'e} dos Campos-SP 12242-800, Brazil and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Performance analysis of MODIS 500-m spatial resolution products
for estimating chlorophyll-a concentrations in oligo- to
meso-trophic waters case study: Itumbiara reservoir, Brazil",
journal = "Remote Sensing",
year = "2014",
volume = "6",
number = "2",
pages = "1634--1653",
keywords = "Bio-optic modeling, Chlorophyll-a, MODIS, Time-series.",
abstract = "Monitoring chlorophyll-a (chl-a) concentrations is important for
the management of water quality, because it is a good indicator of
the eutrophication level in an aquatic system. Thus, our main
purpose was to develop an alternative technique to monitor chl-a
in time and space through remote sensing techniques. However, one
of the limitations of remote sensing is the resolution. To achieve
a high temporal resolution and medium space resolution, we used
the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m
reflectance product, MOD09GA, and limnological parameters from the
Itumbiara Reservoir. With these data, an empirical (O14a) and
semi-empirical (O14b) algorithm were developed. Algorithms were
cross-calibrated and validated using three datasets: one for each
campaign and a third consisting of a combination of the two
individual campaigns. Algorithm O14a produced the best validation
with a root mean square error (RMSE) of 30.4%, whereas O14b
produced an RMSE of 32.41% using the mixed dataset calibration.
O14a was applied to MOD09GA to build a time series for the
reservoir for the year of 2009. The time-series analysis revealed
that there were occurrences of algal blooms in the summer that
were likely related to the additional input of nutrients caused by
rainfall runoff. During the winter, however, the few observed
algal blooms events were related to periods of atmospheric
meteorological variations that represented an enhanced external
influence on the processes of mixing and stratification of the
water column. Finally, the use of remote sensing techniques can be
an important tool for policy makers, environmental managers and
the scientific community with which to monitor water quality.",
doi = "10.3390/rs6021634",
url = "http://dx.doi.org/10.3390/rs6021634",
issn = "2072-4292",
label = "scopus 2014-05 OgashawaraACANSSK:2014:ItReBr",
language = "en",
targetfile = "remotesensing-06-01634.pdf",
urlaccessdate = "27 abr. 2024"
}