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.