Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

Abstract

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.

Short-Range Water Temperature Profiling in a Lake with Coastal Acoustic Tomography

“…C 01 + l 32 C 02 + l 33 C 03 + l C 04 = t 03 (2) After taking the Taylor expansion under the assumption that C 0 δC of Equation 1, by neglecting the second and higher order terms of the Taylor expansion and combining Equation (2) [22], we obtain:…”

“…140 m Start and end time 17:30-21:00 round intervals 3 90 s 1 Q value is the number of cycles per digit in M sequence. 2 The distance is varied during the experiment. 3 Each station transmits signals every 3 min.…”

“…Water temperature can be measured by fixed-point sensors such as temperature chain, thermocouple and other in situ measuring devices, which is time consuming with distribution measurement [1]. Water temperature of a surface layer can also be sensed by remote sensing methods such as radar and satellite [2], while distribution and variation progress of water columns cannot be monitored using those technique. Ocean acoustic tomography (OAT) is a powerful technology that can map the internal distribution structures of water parameters [3][4][5], and many experiments have demonstrated the effectiveness of OAT in the past few years for flow current profiling and internal tide and sound speed measuring [6][7][8].…”

“…This distance is obtained by averaging the direct paths of all data 2. The reference travel time of each ray, which may change as the station position changes.…”

“…C represents the sound speed (m/s);2 MA represents the moving average;3 T represents the temperature (℃);4 The reference sound speed obtained from CTD;5 The reference temperature obtained from CTD.…”