@Article{CairoBLNCMFSC:2020:HyChAl,
author = "Cairo, Carolline Tressmann and Barbosa, Cl{\'a}udio Clemente
Faria and Lobo, Felipe de Lucia and Novo, Evlyn M{\'a}rcia
Le{\~a}o de Moraes and Carlos, Felipe Menino and Maciel, Daniel
Andrade and Flores Junior, Rog{\'e}rio and Silva, Edson Filisbino
Freire da and Curtarelli, Victor Pedroso",
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)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Hybrid chlorophyll-a algorithm for assessing trophic states of a
tropical brazilian reservoir based on MSI/Sentinel-2 data",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "1",
pages = "e12010040",
keywords = "Hybrid Chlorophyll-a Algorithm, monitoramento da qualidade da
{\'a}gua, clorofila-a.",
abstract = "Using remote sensing for monitoring trophic states of inland
waters relies on the calibration of chlorophyll-a (chl-a)
bio-optical algorithms. One of the main limiting factors of
calibrating those algorithms is that they cannot accurately cope
with the wide chl-a concentration ranges in optically complex
waters subject to different trophic states. Thus, this study
proposes an optical hybrid chl-a algorithm (OHA), which is a
combined framework of algorithms for specific chl-a concentration
ranges. The study area is Ibitinga Reservoir characterized by high
spatiotemporal variability of chl-a concentrations (31000 mg/m3 ).
We took the following steps to address this issue: (1) we defined
optical classes of specific chl-a concentration ranges using
Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a
bio-optical algorithms for each trophic class using simulated
Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we
applied a decision tree classifier in MSI/Sentinel-2 image to
detect the optical classes and to switch to the suitable algorithm
for the given class. The results showed that three optical classes
represent different ranges of chl-a concentration: class 1 varies
2.8922.83 mg/m3 , class 2 varies 19.5187.63 mg/m3 , and class 3
varies 75.89938.97 mg/m3 . The best algorithms for trophic classes
1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute
Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and
2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree
classifier showed an accuracy of 95% for detecting SAMs optical
trophic classes. The overall performance of OHA was satisfactory
(R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the
Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal
gap between matchups and the variability in reservoir
hydrodynamics. In summary, OHA proved to be a viable method for
estimating chl-a concentration in Ibitinga Reservoir and the
extension of this framework allowed a more precise chl-a estimate
in eutrophic inland waters.",
doi = "10.3390/rs12010040",
url = "http://dx.doi.org/10.3390/rs12010040",
issn = "2072-4292",
label = "lattes: 1596449770636962 2 CairoBLNCMFSC:2020:HyChAl",
language = "en",
targetfile = "cairo_remote.pdf",
urlaccessdate = "28 abr. 2024"
}