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When a non-volatile solute is added to a pure solvent, it results in the lowering of the freezing point of the solvent. This phenomenon is called freezing point depression. The extent to which the freezing point is lowered depends on the molality of the solute -the number of moles of solute per kilogram of solvent and the cryoscopic constant of the solvent.From the plot of chemical potential, μ, against temperature, it is evident that the μ of both solid and liquid solvents decrease...
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Development of MODIS data-based algorithm for retrieving sea surface temperature in coastal waters.

Jiao Wang1, Zhiqiang Deng2

  • 1Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.

Environmental Monitoring and Assessment
|May 25, 2017
PubMed
Summary
This summary is machine-generated.

A new Artificial Neural Network (ANN) algorithm accurately retrieves sea surface temperature (SST) in coastal waters using MODIS satellite data. This method enhances SST mapping in nearshore areas, crucial for coastal resource management.

Keywords:
MODISRemote sensing algorithmSea surface temperature (SST)

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Area of Science:

  • Oceanography
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Accurate sea surface temperature (SST) data is vital for understanding coastal ecosystems and managing resources.
  • Existing satellite-based SST retrieval methods often struggle with accuracy in complex nearshore environments.

Purpose of the Study:

  • To develop and validate a novel algorithm for retrieving SST in coastal waters using satellite remote sensing data.
  • To improve the spatial resolution and coverage of SST data in shallow nearshore regions.

Main Methods:

  • Development of an Artificial Neural Network (ANN) algorithm for SST retrieval.
  • Training and testing the ANN algorithm using 8 years of Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite data.
  • Validation with in situ SST measurements from diverse US coastal waters.

Main Results:

  • The ANN algorithm demonstrated high accuracy, explaining 82-90% of observed SST variations.
  • The algorithm successfully maps SST at a 1 km spatial resolution, extending coverage to nearshore waters.
  • Input data requirements are minimal, utilizing only MODIS Aqua thermal bands 31 and 32 reflectance values.

Conclusions:

  • The developed ANN algorithm provides a unique and effective solution for SST retrieval in coastal and nearshore waters.
  • This algorithm significantly expands the utility of satellite remote sensing for coastal resource management applications.
  • The method offers improved performance in nearshore areas where traditional algorithms are less effective.