Remote Sensing Applications in Ocean Observation
Since the launch of Seasat, TIROS-N, and Nimbus-7 satellites equipped with ocean observation sensors in 1978, a new era of ocean remote sensing has opened. Today, remotely sensed data have been widely used in oceanographic studies. This reprint collects various advanced ocean remote sensing technolo...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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072 | 7 | |a GP |2 bicssc | |
100 | 1 | |a Ho, Chung-Ru |4 edt | |
700 | 1 | |a Liu, Antony K. |4 edt | |
700 | 1 | |a Li, Xiaofeng |4 edt | |
700 | 1 | |a Ho, Chung-Ru |4 oth | |
700 | 1 | |a Liu, Antony K. |4 oth | |
700 | 1 | |a Li, Xiaofeng |4 oth | |
245 | 1 | 0 | |a Remote Sensing Applications in Ocean Observation |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2023 | ||
300 | |a 1 electronic resource (610 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a Since the launch of Seasat, TIROS-N, and Nimbus-7 satellites equipped with ocean observation sensors in 1978, a new era of ocean remote sensing has opened. Today, remotely sensed data have been widely used in oceanographic studies. This reprint collects various advanced ocean remote sensing technologies and their applications, including the use of artificial intelligence techniques to explore ocean information and bibliometric analysis to assess researchers and trends in this scientific field. The observations of various sensors enrich the application of ocean environment monitoring and ocean dynamical analysis. If you are interested in understanding the application of ocean remote sensing, this monograph should be very helpful. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Research & information: general |2 bicssc | |
653 | |a bibliometric analysis | ||
653 | |a remote sensing | ||
653 | |a oil slicks | ||
653 | |a oil detection | ||
653 | |a coastal waters of Myanmar | ||
653 | |a upwelling | ||
653 | |a monsoon | ||
653 | |a remote equatorial forcing | ||
653 | |a coastal upwelling | ||
653 | |a Himawari-8 | ||
653 | |a sea surface temperature | ||
653 | |a Taiwan | ||
653 | |a topographic position index | ||
653 | |a upwelling index | ||
653 | |a mapping | ||
653 | |a satellite remote sensing | ||
653 | |a quantitative mapping | ||
653 | |a spatial analysis | ||
653 | |a the East Australian Current | ||
653 | |a New South Wales | ||
653 | |a shelf circulation | ||
653 | |a seagrass | ||
653 | |a Zostera marina L. | ||
653 | |a reclamation | ||
653 | |a spatial and temporal changes | ||
653 | |a mesoscale eddies | ||
653 | |a the Indonesian Seas | ||
653 | |a sea level anomaly | ||
653 | |a nonlinearity | ||
653 | |a barotropic instability | ||
653 | |a baroclinic instability | ||
653 | |a SAR | ||
653 | |a CNN | ||
653 | |a Sentinel-1 | ||
653 | |a ship detection | ||
653 | |a geostationary ocean color imager (GOCI) | ||
653 | |a GDPS | ||
653 | |a SeaDAS | ||
653 | |a normalized water-leaving radiance | ||
653 | |a atmospheric correction | ||
653 | |a fish assemblage | ||
653 | |a temperature | ||
653 | |a environmental change | ||
653 | |a Yellow Sea coastal current | ||
653 | |a East China Sea | ||
653 | |a lidar | ||
653 | |a remote sensing sensors | ||
653 | |a backward scattering intensity | ||
653 | |a ocean Scheimpflug lidar | ||
653 | |a volume scattering function | ||
653 | |a Arabian Gulf | ||
653 | |a Gulf of Oman | ||
653 | |a MODIS | ||
653 | |a algal blooms | ||
653 | |a chlorophyll-a | ||
653 | |a SST | ||
653 | |a bathymetry | ||
653 | |a semidiurnal internal tides | ||
653 | |a the Sulu-Sulawesi Seas | ||
653 | |a sea surface height | ||
653 | |a plane wave fit method | ||
653 | |a energy flux | ||
653 | |a Kuroshio intrusion | ||
653 | |a Kuroshio Current Loop | ||
653 | |a cold-core anticyclonic eddy | ||
653 | |a cloud masking | ||
653 | |a turbid water | ||
653 | |a spectral variability | ||
653 | |a total suspended sediment | ||
653 | |a chlorophyll-a bloom | ||
653 | |a typhoon | ||
653 | |a South China Sea | ||
653 | |a alongshore current | ||
653 | |a marine heatwaves | ||
653 | |a sea surface temperatures | ||
653 | |a summer 2021 | ||
653 | |a northwestern Pacific Ocean | ||
653 | |a westerly jet | ||
653 | |a North Pacific Subtropical High | ||
653 | |a ocean color | ||
653 | |a water type taxonomies | ||
653 | |a trophic state | ||
653 | |a inherent optical properties | ||
653 | |a Forel-Ule Scale | ||
653 | |a Sargassum | ||
653 | |a aerosols | ||
653 | |a OLCI | ||
653 | |a Daya Bay Nuclear Power Plants | ||
653 | |a thermal discharge | ||
653 | |a long-term changes | ||
653 | |a Landsat | ||
653 | |a radiative transfer equation | ||
653 | |a split-window algorithm | ||
653 | |a power plant installed capacity | ||
653 | |a flood tide | ||
653 | |a ebb tide | ||
653 | |a wind field | ||
653 | |a bias correction | ||
653 | |a deep learning | ||
653 | |a ConvLSTM | ||
653 | |a 3D-C BAM | ||
653 | |a Kuroshio branch | ||
653 | |a salinity | ||
653 | |a North Pacific subtropical gyre | ||
653 | |a satellite observation | ||
653 | |a in situ observation | ||
653 | |a Taiwan Strait | ||
653 | |a flow pattern | ||
653 | |a high-frequency radar | ||
653 | |a drifter | ||
653 | |a tide | ||
653 | |a turbulent mixing | ||
653 | |a upper ocean response | ||
653 | |a Super Typhoon Goni | ||
653 | |a satellite observations | ||
653 | |a HYCOM reanalysis results | ||
653 | |a internal tides | ||
653 | |a spatiotemporal variation | ||
653 | |a modal structure | ||
653 | |a energy cascade | ||
653 | |a machine learning | ||
653 | |a ocean subsurface salinity structure | ||
653 | |a satellite remote sensing data | ||
653 | |a data fusion | ||
653 | |a offshore detection | ||
653 | |a SAR images | ||
653 | |a meteorological data | ||
653 | |a AI explanation | ||
653 | |a sea ice | ||
653 | |a Bayesian algorithm | ||
653 | |a CFOSAT | ||
653 | |a scatterometer | ||
653 | |a internal solitary waves | ||
653 | |a turbulence | ||
653 | |a Yellow Sea | ||
653 | |a wake detection | ||
653 | |a radiation sensitivity | ||
653 | |a noise equivalent reflectance difference | ||
653 | |a three-dimensional eddy reconstruction | ||
653 | |a loop current rings | ||
653 | |a gulf of Mexico | ||
653 | |a gravest empirical modes | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/6713 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/96767 |7 0 |z DOAB: description of the publication |