@Article{MontanherNovBarRenSil:2014:EmMoEs,
author = "Montanher, Ot{\'a}vio C. and Novo, Evlyn M{\'a}rcia Le{\~a}o de
Moraes and Barbosa, Claudio Clemente Faria and Renn{\'o}, Camilo
Daleles and Silva, Thiago S. F.",
affiliation = "{Universidade Estadual de Maring{\'a}} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Universidade Estadual Paulista (UNESP)}",
title = "Empirical models for estimating the suspended sediment
concentration in Amazonian white water rivers using Landsat 5/TM",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2014",
volume = "29",
pages = "67--77",
month = "June",
keywords = "top of atmosphere reflectance, multiple regressions, geology of
the Amazon, fluvial sediments, spectral bands, band ratios.",
abstract = "Suspended sediment yield is a very important environmental
indicator within Amazonian fluvial systems, especially for rivers
dominated by inorganic particles, referred to as white water
rivers. For vast portions of Amazonian rivers, suspended sediment
concentration (SSC) is measured infrequently or not at all.
However, remote sensing techniques have been used to estimate
water quality parameters worldwide, from which data for suspended
matter is the most successfully retrieved. This paper presents
empirical models for SSC retrieval in Amazonian white water rivers
using reflectance data derived from Landsat 5/TM. The models use
multiple regression for both the entire dataset (global model, N =
504) and for five segmented datasets (regional models) defined by
general geological features of drainage basins. The models use
VNIR bands, band ratios, and the SWIR band 5 as input. For the
global model, the adjusted R2 is 0.76, while the adjusted R2
values for regional models vary from 0.77 to 0.89, all significant
(p-value < 0.0001). The regional models are subject to the
leave-one-out cross validation technique, which presents robust
results. The findings show that both the average error of
estimation and the standard deviation increase as the SSC range
increases. Regional models were more accurate when compared with
the global model, suggesting changes in optical proprieties of
water sampled at different sampling stations. Results confirm the
potential for the estimation of SSC from Landsat/TM historical
series data for the 1980s and 1990s, for which the in situ
database is scarce. Such estimates supplement the SSC temporal
series, providing a more comprehensive SSC temporal series which
may show environmental dynamics yet unknown.",
doi = "10.1016/j.jag.2014.01.001",
url = "http://dx.doi.org/10.1016/j.jag.2014.01.001",
issn = "0303-2434",
label = "self-archiving-INPE-MCTI-GOV-BR",
language = "en",
url = "http://dx.doi.org/10.1016/j.jag.2014.01.001",
urlaccessdate = "03 maio 2024"
}