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Materials and Methods
Ocean Color Study
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Environmental Science
Ecological Modeling

Applications of Remote Sensing in Oceanographic Research

Profile image of Hafez AhmadHafez Ahmad

2019, International Journal of Oceanography & Aquaculture

https://doi.org/10.23880/IJOAC-16000159
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Abstract

Remote sensing (RS) has a wide range of applications in the field of physical, biological, coastal, satellite oceanography. RS in Oceanographic research is the collection of oceanographic, monitoring of coastal and oceanic processes data and analysis various processes using space borne and air borne sensors. RS offers many advantages over conventional procedures such as synoptic coverage, repeated observations, and area averaging. The main applications are ocean weather and climate studies, measuring primary productivity, water quality monitoring, detection of potential fishing zone, marine life assessment, marine pollution monitoring, determination of near shore bathymetry and mapping, sensing of ocean current and wave, human impacts on marine and coastal life etc. This study aims to identify and explain the importance of RS, advancements, rationality of applications, and future trends in oceanic research.

Figures (7)
Table 1: Ocean parameters with their importance of each parameters [4,5]. RS is so high sophisticated technology for this purpose. The benefits of RS in oceanography include the ability to collect data over vast ocean to characterize natural features or physical objects on the ground, to observe surface of ocean and objects on a systematic basis and monitor their changes over time, and the ability to integrate this data with other information to aid decision making. RS from satellites can be collected at various spatial resolutions. Images can detect features in a size range of tens to hundreds of meters or larger. Remote sensing instruments may also acquire data in different spectral bands of the electromagnetic spectrum which provides information. Application of RS in Oceanographic research refers to collection of oceanographic data and analysis various processes using space borne and air borne sensors. monitoring of coastal and oceanic processes is very important in sea level, sea surface temperature (SST) changes due to climate change and global warming, shore line changes, coastal morphology due to dynamic nature of coastal zone, detection of chlorophyll content, oil spill, suspended or dissolved matter, algal bloom, measurement of near shore bathymetry, natural hazards, management of coastal zone, exploration and exploitation living and nonliving resource and determination of human impacts [3]. Materials and Methods
Table 1: Ocean parameters with their importance of each parameters [4,5]. RS is so high sophisticated technology for this purpose. The benefits of RS in oceanography include the ability to collect data over vast ocean to characterize natural features or physical objects on the ground, to observe surface of ocean and objects on a systematic basis and monitor their changes over time, and the ability to integrate this data with other information to aid decision making. RS from satellites can be collected at various spatial resolutions. Images can detect features in a size range of tens to hundreds of meters or larger. Remote sensing instruments may also acquire data in different spectral bands of the electromagnetic spectrum which provides information. Application of RS in Oceanographic research refers to collection of oceanographic data and analysis various processes using space borne and air borne sensors. monitoring of coastal and oceanic processes is very important in sea level, sea surface temperature (SST) changes due to climate change and global warming, shore line changes, coastal morphology due to dynamic nature of coastal zone, detection of chlorophyll content, oil spill, suspended or dissolved matter, algal bloom, measurement of near shore bathymetry, natural hazards, management of coastal zone, exploration and exploitation living and nonliving resource and determination of human impacts [3]. Materials and Methods
(passive remote sensing). The possible interactions are absorption and scattering by the water constituents, reflection and absorption by the sea surface, reflection and refraction at the water/air interface and absorption and scattering by the atmosphere. Figure 1: Simple Data collection techniques in water body via remote sensing [2].
(passive remote sensing). The possible interactions are absorption and scattering by the water constituents, reflection and absorption by the sea surface, reflection and refraction at the water/air interface and absorption and scattering by the atmosphere. Figure 1: Simple Data collection techniques in water body via remote sensing [2].
Figure 2: July 2008 and July 2018 SST maps are based on observations by the MODIS on NASA’s Aqua satellite (source www.earthobservatory.nasa.gov). Remote sensing has given major advances in understanding the climate system and its change by quantifying processes and spatio temporal states of atmosphere, land and oceans. Satellite data are frequently used with climate models to stimulate the dynamic of the climate system and to improve climate projections [6]. Sea surface temperature (SST) plays an important role in many oceanic processes that is directly related to heat transfer between ocean and atmosphere and served as important indicator of climate state.in numerical weather prediction models, the SST field exerts its influence in the calculation of surface fluxes over the oceans [3]. The Advanced Very High Resolution Radiometer on board the National Oceanic and Atmospheric Administration (NOAA) satellites allows us to monitor the SST worldwide. Satellite observations also reveal an uneven warming pattern [7].
Figure 2: July 2008 and July 2018 SST maps are based on observations by the MODIS on NASA’s Aqua satellite (source www.earthobservatory.nasa.gov). Remote sensing has given major advances in understanding the climate system and its change by quantifying processes and spatio temporal states of atmosphere, land and oceans. Satellite data are frequently used with climate models to stimulate the dynamic of the climate system and to improve climate projections [6]. Sea surface temperature (SST) plays an important role in many oceanic processes that is directly related to heat transfer between ocean and atmosphere and served as important indicator of climate state.in numerical weather prediction models, the SST field exerts its influence in the calculation of surface fluxes over the oceans [3]. The Advanced Very High Resolution Radiometer on board the National Oceanic and Atmospheric Administration (NOAA) satellites allows us to monitor the SST worldwide. Satellite observations also reveal an uneven warming pattern [7].
Figure 3: Global mean sea level trends (1992-2018).
Figure 3: Global mean sea level trends (1992-2018).
Table 2: Applications of ocean colors and usefulness of ocean color sensors. A multi-spectral image was derived from MODIS and corrected by a supervised classification system to enhance and characterize oil on water [9]. Laser fluorosensors use the phenomenon that oil aromatic compounds interact with ultraviolet light, absorb the light energy, and release the extra energy as visible light. The absorption and emission wavelengths are unique to oil [11]. Satellite-based oil pollution monitoring capabilities in the Norwegian waters were demonstrated in the early 1990s by using images from the ERS-1 satellite [12]. Ocean Primary Productivity Measuring phytoplankton. Data from Coastal Zone Colour Scanner (CZCS) provided first view of the synoptic scale distribution of chlorophyll concentration [13]. Three- band algorithm has been used to estimates of Chla in Ocean color is an important aspect for RS. It provides numerous potentials and useful information for oceanographic research. Ocean color sensors are having high radiometry to measure water quality with reasonable accuracy and precision. Thermal infrared sensors provide sea surface temperatures (SST) and features with varying sea surface temperatures.
Table 2: Applications of ocean colors and usefulness of ocean color sensors. A multi-spectral image was derived from MODIS and corrected by a supervised classification system to enhance and characterize oil on water [9]. Laser fluorosensors use the phenomenon that oil aromatic compounds interact with ultraviolet light, absorb the light energy, and release the extra energy as visible light. The absorption and emission wavelengths are unique to oil [11]. Satellite-based oil pollution monitoring capabilities in the Norwegian waters were demonstrated in the early 1990s by using images from the ERS-1 satellite [12]. Ocean Primary Productivity Measuring phytoplankton. Data from Coastal Zone Colour Scanner (CZCS) provided first view of the synoptic scale distribution of chlorophyll concentration [13]. Three- band algorithm has been used to estimates of Chla in Ocean color is an important aspect for RS. It provides numerous potentials and useful information for oceanographic research. Ocean color sensors are having high radiometry to measure water quality with reasonable accuracy and precision. Thermal infrared sensors provide sea surface temperatures (SST) and features with varying sea surface temperatures.
Figure 4: Example of detection the oil slick shown by the white rectangle (MODIS, deep water horizon, www.nasa.gov noted: many clouds in the image. Oil spill (OS) can destroy marine life and habitat of land animal as well as human. For OS mapping and prediction some information must need to know these are Spill location, size and extent of oil spill, direction and magnitude, wind, current and wave information for predicting future oil movement. Annually, 48% of the oil pollution in the oceans is fuels and 29% are crude oil. Tanker accidents contribute with only 5% of all pollution entering into the sea. The detection and mapping of oil on a water surface is the most common uses of oil spill remote sensing [9,10].
Figure 4: Example of detection the oil slick shown by the white rectangle (MODIS, deep water horizon, www.nasa.gov noted: many clouds in the image. Oil spill (OS) can destroy marine life and habitat of land animal as well as human. For OS mapping and prediction some information must need to know these are Spill location, size and extent of oil spill, direction and magnitude, wind, current and wave information for predicting future oil movement. Annually, 48% of the oil pollution in the oceans is fuels and 29% are crude oil. Tanker accidents contribute with only 5% of all pollution entering into the sea. The detection and mapping of oil on a water surface is the most common uses of oil spill remote sensing [9,10].
turbid and very high biomass hypertrophic waters. Sensors such as Landsat, SPOT, and IKONOS are also frequently used to estimate Chla [14-17]. Figure 5: Chlorophyll concentration maps of July 2008 and July 2018 are based on observations by the MODIS on NASA's Aqua satellite (source: https://earthobservatory.nasa.gov/global- maps/MY1DMM_CHLORA). Places where chlorophyll amounts were very low, indicating very low numbers of phytoplankton are blue. Places where chlorophyll concentrations were high, meaning many phytoplankton were growing, are dark green.
turbid and very high biomass hypertrophic waters. Sensors such as Landsat, SPOT, and IKONOS are also frequently used to estimate Chla [14-17]. Figure 5: Chlorophyll concentration maps of July 2008 and July 2018 are based on observations by the MODIS on NASA's Aqua satellite (source: https://earthobservatory.nasa.gov/global- maps/MY1DMM_CHLORA). Places where chlorophyll amounts were very low, indicating very low numbers of phytoplankton are blue. Places where chlorophyll concentrations were high, meaning many phytoplankton were growing, are dark green.

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