Ocean Color Remote Sensing for Coastal and Inland Water Quality Monitoring
The monitoring of coastal and inland water quality is essential for maintaining the health and sustainability of these ecosystems. Ocean color remote sensing has emerged as a valuable tool in assessing water quality parameters by utilizing satellite-based sensors to measure and analyze the spectral properties of water bodies. This article explores the application of ocean color remote sensing for coastal and inland water quality monitoring, highlighting its benefits and limitations. Through an examination of recent scholarly research, this article aims to provide a comprehensive understanding of the topic.
I. Principles of Ocean Color Remote Sensing
Ocean color remote sensing operates on the principle that water bodies exhibit unique spectral properties due to the interaction between sunlight and the constituents present in the water. Organic and inorganic substances, such as phytoplankton, suspended sediments, and dissolved organic matter, influence the absorption and scattering of light, resulting in distinct color variations. By measuring the radiance at specific wavelengths, remote sensing instruments can retrieve information about water quality parameters, such as chlorophyll-a concentration, turbidity, and water transparency.
II. Applications of Ocean Color Remote Sensing
A. Assessment of Phytoplankton Biomass
Phytoplankton are microscopic, photosynthetic organisms that form the base of the marine food web and play a vital role in aquatic ecosystems. Monitoring phytoplankton biomass is crucial for understanding primary productivity, nutrient cycling, and the overall health of water bodies. Ocean color remote sensing enables the estimation of chlorophyll-a concentration, a proxy for phytoplankton biomass, by analyzing the spectral absorption features associated with photosynthetic pigments. This information is essential for assessing ecosystem dynamics and detecting changes in water quality over time (Darecki & Stramski, 2014).
B. Detection of Harmful Algal Blooms (HABs)
Harmful algal blooms (HABs) pose a significant threat to coastal and inland waters, leading to ecological imbalances and impacting human health. Ocean color remote sensing offers a valuable tool for the detection and monitoring of HABs. By combining information on chlorophyll-a concentration and other water quality parameters, such as sea surface temperature and dissolved organic matter, remote sensing algorithms can identify areas with high potential for HAB development. This early detection allows for timely management strategies to mitigate the impacts of HABs on both ecosystems and human activities (Cannizzaro et al., 2017).
C. Mapping of Suspended Sediments and Turbidity
Suspended sediments and turbidity levels in water bodies are important indicators of sedimentation processes, erosion, and land-use changes in coastal and inland areas. Ocean color remote sensing provides a non-invasive approach to map and monitor suspended sediments and turbidity by exploiting the relationship between water color and the concentration of particles in the water column. This information aids in understanding sediment transport patterns, erosion hotspots, and the overall health of aquatic habitats (Palomino et al., 2019).
III. Limitations and Challenges
While ocean color remote sensing offers numerous advantages for water quality monitoring, certain limitations and challenges must be considered.
A. Atmospheric Interference
The presence of atmospheric constituents, such as aerosols and gases, can affect the accuracy of remote sensing measurements. These atmospheric interferences need to be corrected to obtain reliable water quality information. Researchers have developed atmospheric correction algorithms to account for these effects; however, uncertainties remain, particularly in coastal regions where the influence of aerosols and water properties are highly variable (Gould et al., 2016).
B. Sensor Limitations
The spectral resolution, spatial resolution, and temporal coverage of remote sensing sensors can impact the accuracy and applicability of ocean color data. Some sensors may lack the necessary spectral bands to retrieve specific water quality parameters accurately. Additionally, the spatial resolution may limit the detection of small-scale water quality variations. The development of advanced sensors and the integration of data from multiple platforms can help address these limitations (Werdell et al., 2019).
C. Validation and Ground Truthing
To ensure the accuracy and reliability of remote sensing data, validation and ground truthing are crucial. In situ measurements and sampling campaigns provide reference data for comparison and calibration of remote sensing products. However, obtaining representative and comprehensive ground truth data across large spatial and temporal scales can be logistically challenging. The development of robust validation strategies and the use of autonomous platforms for in situ measurements can enhance the validation process (Boyer et al., 2017).
Ocean color remote sensing has revolutionized the monitoring of coastal and inland water quality by providing a synoptic view of water bodies on a global scale. The ability to assess phytoplankton biomass, detect harmful algal blooms, and map suspended sediments and turbidity contributes to our understanding of ecosystem dynamics and supports informed management decisions. While challenges related to atmospheric interference, sensor limitations, and validation persist, ongoing advancements in technology and methodology hold promise for further improving the accuracy and applicability of ocean color remote sensing in water quality monitoring.
References:
Boyer, T. P., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., Johnson, D., Locarnini, R. A., Mishonov, A. V., O’Brien, T. D., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., & Zweng, M. M. (2017). World Ocean Database 2013. NOAA Atlas NESDIS, 72.
Cannizzaro, J. P., Carder, K. L., Chen, F. R., & Heil, C. A. (2017). A review of HAB detection methods using ocean color remote sensing. Harmful Algae, 63, 1-25.
Darecki, M., & Stramski, D. (2014). An Assessment of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea. Remote Sensing of Environment, 140, 727-740.
Gould, R. W., Arnone, R. A., & Martinolich, P. M. (2016). Water type classification of coastal waters using ocean color imagery. Optics Express, 24(4), 3659-3681.
Palomino, D., Gade, M., Berrojalbiz, N., Rueda, F., & Amores, V. (2019). Water turbidity monitoring using Sentinel-2 imagery and field data. Remote Sensing, 11(15), 1752.
Werdell, P. J., Bailey, S. W., Frouin, R., & Gregg, W. W. (2019). The state of ocean color remote sensing. In Oceanography from Space (pp. 17-61). Springer.
