@Article{OgashawaraCuSoAuAlSt:2014:InCoEn,
author = "Ogashawara, Igor and Curtarelli, Marcelo Pedroso and Souza, Arley
F. and Augusto-Silva, P{\'e}tala Bianchi and Alc{\^a}ntara,
Enner H. and Stech, Jos{\'e} Luiz",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {ETEP Faculdades} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {State
University of S{\~a}o Paulo} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Interactive correlation environment (ICE)-A statistical web tool
for data collinearity analysis",
journal = "Remote Sensing",
year = "2014",
volume = "6",
number = "4",
pages = "3059--3074",
month = "Apr.",
keywords = "2D correlation, Band ratios, Bio-optical, Hyperspectral sensors,
Interactive correlation, Open access platforms, Root mean square
errors, Web tools, Biogeochemistry, Chlorophyll, Correlation
methods, Mean square error, Optical correlation, Reservoirs
(water), Tools, Ice.",
abstract = "Web tools for statistical investigation with an interactive and
friendly interface enable users without programming knowledge to
conduct their analyses. We develop an Interactive Correlation
Environment (ICE) in an open access platform to perform spectral
collinearity analysis for biogeochemical activity retrieval. We
evaluate its performance on different browsers and applied it to
retrieve chlorophyll-a (chl-a) concentration in a tropical
reservoir. The use of ICE to retrieve water chl-a concentration
got a Root Mean Square Error (RMSE) lower than 7% for seasonal
datasets, enhancing ICE's ability to adapt it within season. An
RMSE of 17% was found for the mixed dataset with a large range of
chl-a concentrations. We conclude that the use of ICE is
recommended, due to its quick response, easily manipulation, high
accuracy, and empirical adaptation to seasonal variability. Its
use is enhanced by the development of hyperspectral sensors, which
allow the identification of several biogeochemical components,
such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf
nitrogen, and leaf chl-a concentration.",
doi = "10.3390/rs6043059",
url = "http://dx.doi.org/10.3390/rs6043059",
issn = "2072-4292",
label = "scopus 2014-05 CurtarelliOgStAuSoAl:2014:InCoEn",
language = "en",
targetfile = "remotesensing-06-03059.pdf",
url = "http://www.mdpi.com/2072-4292/6/4/3059",
urlaccessdate = "03 jun. 2024"
}