@Article{JorgeBaCaAfLoNo:2017:SNSiRa,
author = "Jorge, Daniel Schaffer Ferreira and Barbosa, Cl{\'a}udio Clemente
Faria and Carvalho, Lino Augusto Sander de and Affonso, Adriana
Gomes and Lobo, Felipe de Lucia and Novo, Evlyn M{\'a}rcia
Le{\~a}o de Moraes",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "SNR (signal-to-noise ratio) impact on water constituent retrieval
from simulated images of optically complex Amazon lakes",
journal = "Remote Sensing",
year = "2017",
volume = "9",
number = "7",
pages = "Article number 644",
month = "July",
keywords = "signal-to-noise ratio, Remote Sensing Reflectance, bio-optical
algorithms, inland waters.",
abstract = "Uncertainties in the estimates of water constituents are among the
main issues concerning the orbital remote sensing of inland
waters. Those uncertainties result from sensor design, atmosphere
correction, model equations, and in situ conditions (cloud cover,
lake size/shape, and adjacency effects). In the Amazon floodplain
lakes, such uncertainties are amplified due to their seasonal
dynamic. Therefore, it is imperative to understand the suitability
of a sensor to cope with them and assess their impact on the
algorithms for the retrieval of constituents. The objective of
this paper is to assess the impact of the SNR on the Chl-a and TSS
algorithms in four lakes located at Mamirau{\'a} Sustainable
Development Reserve (Amazonia, Brazil). Two data sets were
simulated (noisy and noiseless spectra) based on in situ
measurements and on sensor design (MSI/Sentinel-2,
OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using
three and four algorithms for TSS and Chl-a, respectively. The
results showed that the impact of the SNR on each algorithm
displayed similar patterns for both constituents. For additive and
single band algorithms, the error amplitude is constant for the
entire concentration range. However, for multiplicative
algorithms, the error changes according to the model equation and
the Rrs magnitude. Lastly, for the exponential algorithm, the
retrieval amplitude is higher for a low concentration. The OLCI
sensor has the best retrieval performance (error of up to 2 µg/L
for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive
and single band algorithms for TSS and Chl-a are low (up to 5 mg/L
and 1 µg/L, respectively); but for the multiplicative algorithm,
the errors were above 10 µg/L. The OLI simulation resulted in
errors below 3 mg/L for TSS. However, the number and position of
OLI bands restrict Chl-a retrieval. Sensor and algorithm selection
need a comprehensive analysis of key factors such as sensor
design, in situ conditions, water brightness (Rrs), and model
equations before being applied for inland water studies.",
doi = "10.3390/rs9070644",
url = "http://dx.doi.org/10.3390/rs9070644",
issn = "2072-4292",
language = "en",
targetfile = "jorge_snr.pdf",
urlaccessdate = "27 abr. 2024"
}