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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Australian aquatic bio-optical dataset with applications for satellite calibration, algorithm development and

Nathan Drayson¹, Janet Anstee¹, Hannelie Botha¹

¹CSIRO, Oceans and Atmosphere, Black Mountain, Canberra, ACT 2601, Australia.

|August 15, 2022

Summary

This study provides extensive bio-optical data from 34 Australian inland waterbodies, crucial for developing and validating remote sensing algorithms for water quality monitoring.

Keywords:

Aquatic bio-optical properties Aquatic remote sensing algorithm development Aquatic remote sensing reflectance Inland water quality

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Area of Science:

* Aquatic Optics
* Remote Sensing
* Water Quality Monitoring

Background:

* Understanding the optical properties of inland waters is essential for effective environmental monitoring.
* A comprehensive dataset is needed to support the development of satellite-based water quality algorithms.

Purpose of the Study:

* To present a unique bio-optical dataset collected from Australian inland waters.
* To provide data suitable for the development and validation of remote sensing algorithms.
* To support machine learning applications in aquatic science.

Main Methods:

* Collected 316 sets of in-situ bio-optical measurements from 34 Australian inland waterbodies (2013-2021).
* Acquired radiometric measurements including remote sensing reflectance (Rrs) and diffuse attenuation coefficient (Kd).
* Quantified optical properties such as backscattering, absorption by CDOM, phytoplankton, and non-algal particles, alongside chlorophyll-a, TSS, and organic carbon concentrations.

Main Results:

* The dataset encompasses a wide spectrum of optical water types found in Australian inland environments.
* Measurements were synchronized with Landsat 8 and Sentinel-2 satellite overpasses, enabling direct application.
* Data includes detailed spectral absorption and scattering properties, pigment analysis, and water composition.

Conclusions:

* The presented bio-optical dataset is a valuable resource for advancing inland water remote sensing.
* It facilitates algorithm development, satellite calibration/validation, and machine learning model training.
* The data supports improved understanding and monitoring of aquatic ecosystems through remote sensing techniques.