About this Research Topic
This research topic focuses on the use of advanced measurement technologies, such as autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and CubeSats in addition to conventional airborne and spaceborne systems, and water quality parameter retrieval techniques for remote sensing of coastal and inland waters, including coastal wetlands.
Coastal and inland waters are critical resources of immense economic and environmental value. However, due to natural and anthropogenic factors, coastal and inland waters around the globe are under increasing stress. The health and biophysical status of these ecosystems need to be regularly monitored in order to ensure that they maintain their ecological functionality and services. This includes estimating concentrations of organic and inorganic constituents in water, monitoring the health and distribution of submerged aquatic vegetation and corals, characterizing the biodiversity of phytoplankton, and tracking spatio-temporal dynamics of complex biophysical and biogeochemical processes occurring in the water and adjoining wetlands.
Remote sensing has become an indispensable tool for monitoring coastal and inland waters, and hyperspectral remote sensing has gained increased use in the last decade. The optical complexity typically encountered in coastal and inland waters necessitates hyperspectral sensors with a fine spectral resolution. Hyperspectral capability enables species discrimination of aquatic vegetation and detection of fine reflectance features of biogenic and inorganic substances in water and accessory pigments such as phycocyanin and phycoerythrin that occur in significant amounts during bloom conditions. In addition to hyperspectral capability, a fine spatial resolution is needed to capture spatial heterogeneity of bio-optical features in waters where spatial variability may occur in scales as fine as a few meters. Sensors with hyperspectral capability and/or high spatial resolution are being used for coastal and inland water applications from a variety of platforms such as moorings, shipboard platforms, UAVs, and airborne and spaceborne systems.
Several space-borne sensors, including CubeSats, with high resolution in the spatial and/or spectral domains that are suitable for coastal and inland water remote sensing, have been launched recently or are scheduled to be launched in the near future. Current spaceborne assets have either the required spectral resolution or spatial resolution but not both, thereby limiting their use for monitoring coastal and inland waters. This limitation is addressed through a number of software and hardware options. Advanced image processing algorithms can combine data from coarse-spatial-resolution hyperspectral data with fine-spatial-resolution multispectral data. Advanced algorithms based on radiative transfer modeling and machine learning concepts are being developed to retrieve multiple water quality parameters from airborne and spaceborne multispectral and hyperspectral data. Hyperspectral sensors on UAVs are used to collect data at fine spatial and spectral resolutions. Lidar systems on AUVs are used to study vertical structures in water.
We solicit articles that address achievements and challenges in the research, development, and application of innovative measurement technologies and water quality parameter retrieval algorithms for remote sensing of coastal and inland waters and wetlands using AUVs, UAVs, CubeSats, manned aircraft, and recently launched spaceborne missions. Studies on potential capabilities and limitations of future sensors currently under development are also welcome.
Coastal and inland waters are critical resources of immense economic and environmental value. However, due to natural and anthropogenic factors, coastal and inland waters around the globe are under increasing stress. The health and biophysical status of these ecosystems need to be regularly monitored in order to ensure that they maintain their ecological functionality and services. This includes estimating concentrations of organic and inorganic constituents in water, monitoring the health and distribution of submerged aquatic vegetation and corals, characterizing the biodiversity of phytoplankton, and tracking spatio-temporal dynamics of complex biophysical and biogeochemical processes occurring in the water and adjoining wetlands.
Remote sensing has become an indispensable tool for monitoring coastal and inland waters, and hyperspectral remote sensing has gained increased use in the last decade. The optical complexity typically encountered in coastal and inland waters necessitates hyperspectral sensors with a fine spectral resolution. Hyperspectral capability enables species discrimination of aquatic vegetation and detection of fine reflectance features of biogenic and inorganic substances in water and accessory pigments such as phycocyanin and phycoerythrin that occur in significant amounts during bloom conditions. In addition to hyperspectral capability, a fine spatial resolution is needed to capture spatial heterogeneity of bio-optical features in waters where spatial variability may occur in scales as fine as a few meters. Sensors with hyperspectral capability and/or high spatial resolution are being used for coastal and inland water applications from a variety of platforms such as moorings, shipboard platforms, UAVs, and airborne and spaceborne systems.
Several space-borne sensors, including CubeSats, with high resolution in the spatial and/or spectral domains that are suitable for coastal and inland water remote sensing, have been launched recently or are scheduled to be launched in the near future. Current spaceborne assets have either the required spectral resolution or spatial resolution but not both, thereby limiting their use for monitoring coastal and inland waters. This limitation is addressed through a number of software and hardware options. Advanced image processing algorithms can combine data from coarse-spatial-resolution hyperspectral data with fine-spatial-resolution multispectral data. Advanced algorithms based on radiative transfer modeling and machine learning concepts are being developed to retrieve multiple water quality parameters from airborne and spaceborne multispectral and hyperspectral data. Hyperspectral sensors on UAVs are used to collect data at fine spatial and spectral resolutions. Lidar systems on AUVs are used to study vertical structures in water.
We solicit articles that address achievements and challenges in the research, development, and application of innovative measurement technologies and water quality parameter retrieval algorithms for remote sensing of coastal and inland waters and wetlands using AUVs, UAVs, CubeSats, manned aircraft, and recently launched spaceborne missions. Studies on potential capabilities and limitations of future sensors currently under development are also welcome.
Keywords: Hyperspectral, unmanned aerial vehicles (UAVs), CubeSats, machine learning, coastal and inland waters, wetlands, autonomous underwater vehicles (AUVs)
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