Remote sensing for optimal estimation of water temperature dynamics in shallow tidal environments
Abstract
© 2019 by the authors. Given the increasing anthropogenic pressures on lagoons, estuaries,
and lakes and considering the highly dynamic behavior of these systems, methods for
the continuous and spatially distributed retrieval of water quality are becoming vital
for their correct monitoring and management. Water temperature is certainly one of
the most important drivers that influence the overall state of coastal systems. Traditionally,
lake, estuarine, and lagoon temperatures are observed through point measurements carried
out during field campaigns or through a network of sensors. However, sporadic measuring
campaigns or probe networks rarely attain a density sufficient for process understanding,
model development/validation, or integrated assessment. Here, we develop and apply
an integrated approach for water temperature monitoring in a shallow lagoon which
incorporates satellite and in-situ data into a mathematical model. Specifically, we
use remote sensing information to constrain large-scale patterns of water temperature
and high-frequency in situ observations to provide proper time constraints. A coupled
hydrodynamic circulation-heat transport model is then used to propagate the state
of the system forward in time between subsequent remote sensing observations. Exploiting
the satellite data high spatial resolution and the in situ measurements high temporal
resolution, the model may act a physical interpolator filling the gap intrinsically
characterizing the two monitoring techniques.
Type
Journal articlePermalink
https://hdl.handle.net/10161/20361Published Version (Please cite this version)
10.3390/rs12010051Publication Info
Pivato, M; Carniello, L; Viero, DP; Soranzo, C; Defina, A; & Silvestri, S (2020). Remote sensing for optimal estimation of water temperature dynamics in shallow tidal
environments. Remote Sensing, 12(1). pp. 51-51. 10.3390/rs12010051. Retrieved from https://hdl.handle.net/10161/20361.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Sonia Silvestri
Adjunct Associate Professor
Silvestri received her doctoral training in Environmental System Modelling at the
University of Padova, with a focus on remote sensing and the interdependence of salt
marsh morphology and halophytic vegetation. She received her Laurea in Environmental
Sciences from the University Ca’ Foscari in Venice. Silvestri joined the Nicholas
School (Duke University) in 2011, where she teaches “Introduction to Satellite
Remote Sensing” and “Remote Sensing of Coastal Environments&rdq

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