@Article{BothaAnsSagLecMed:2020:ClAuWa,
author = "Botha, Elizabeth J. and Anstee, Janet M. and Sagar, Stephen and
Lechmann, Eric and Medeiros, Thais Andrade Galv{\~a}o de",
affiliation = "{CSIRO Oceans \& Atmosphere} and {CSIRO Oceans \& Atmosphere}
and {Geoscience Australia} and {CSIRO Data61} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Classification of Australian waterbodies across a wide range of
optical water types",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "18",
pages = "e3018",
month = "Sept.",
abstract = "Baseline determination and operational continental scale
monitoring of water quality are required for reporting on marine
and inland water progress to Sustainable Development Goals (SDG).
This study aims to improve our knowledge of the optical complexity
of Australian waters. A workflow was developed to cluster the
modelled spectral response of a range of in situ bio-optical
observations collected in Australian coastal and continental
waters into distinct optical water types (OWTs). Following
clustering and merging, most of the modelled spectra and modelled
specific inherent optical properties (SIOP) sets were clustered in
11 OWTs, ranging from clear blue coastal waters to very turbid
inland lakes. The resulting OWTs were used to classify Sentinel-2
MSI surface reflectance observations extracted over relatively
permanent water bodies in three drainage regions in Eastern
Australia. The satellite data classification demonstrated clear
limnological and seasonal differences in water types within and
between the drainage divisions congruent with general
limnological, topographical, and climatological factors. Locations
of unclassified observations can be used to inform where in situ
bio-optical data acquisition may be targeted to capture a more
comprehensive characterization of all Australian waters. This can
contribute to global initiatives like the SDGs and increases the
diversity of natural water in global databases.",
doi = "10.3390/RS12183018",
url = "http://dx.doi.org/10.3390/RS12183018",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-12-03018-v2.pdf",
urlaccessdate = "13 jun. 2024"
}