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230515 ||| eng |
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|a books978-3-0365-6439-5
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|a 9783036564388
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|a 9783036564395
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| 100 |
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|a Ho, Chung-Ru
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| 245 |
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|a Remote Sensing Applications in Ocean Observation
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (610 p.)
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| 653 |
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|a aerosols
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|a machine learning
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|a remote sensing sensors
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|a North Pacific subtropical gyre
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|a Landsat
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|a Bayesian algorithm
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|a alongshore current
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|a coastal waters of Myanmar
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|a mesoscale eddies
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|a cold-core anticyclonic eddy
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|a South China Sea
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|a satellite observations
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|a offshore detection
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|a high-frequency radar
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|a data fusion
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|a environmental change
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|a deep learning
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|a noise equivalent reflectance difference
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| 653 |
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|a baroclinic instability
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| 653 |
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|a plane wave fit method
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|a reclamation
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|a semidiurnal internal tides
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| 653 |
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|a Zostera marina L.
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|a ocean Scheimpflug lidar
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|a flood tide
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|a marine heatwaves
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|a radiative transfer equation
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|a radiation sensitivity
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| 653 |
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|a Taiwan Strait
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|a bibliometric analysis
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| 653 |
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|a Super Typhoon Goni
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|a Yellow Sea
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|a trophic state
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|a internal solitary waves
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|a ebb tide
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| 653 |
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|a ConvLSTM
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|a turbulence
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|a Taiwan
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|a volume scattering function
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|a the East Australian Current
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|a scatterometer
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|a gravest empirical modes
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|a the Sulu-Sulawesi Seas
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|a ship detection
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|a normalized water-leaving radiance
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|a gulf of Mexico
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|a SeaDAS
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|a water type taxonomies
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|a backward scattering intensity
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|a turbid water
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|a long-term changes
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|a energy cascade
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|a three-dimensional eddy reconstruction
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|a SAR images
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|a satellite remote sensing data
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|a wake detection
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|a salinity
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|a AI explanation
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|a meteorological data
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|a ocean subsurface salinity structure
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|a geostationary ocean color imager (GOCI)
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|a Gulf of Oman
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|a Sargassum
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|a temperature
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|a internal tides
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|a nonlinearity
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|a inherent optical properties
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|a oil slicks
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|a monsoon
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|a sea ice
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|a East China Sea
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|a Kuroshio Current Loop
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| 653 |
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|a Research and information: general / bicssc
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|a MODIS
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|a bathymetry
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| 653 |
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|a SST
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|a Yellow Sea coastal current
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| 653 |
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|a power plant installed capacity
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| 653 |
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|a satellite remote sensing
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| 653 |
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|a upwelling
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|a thermal discharge
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|a bias correction
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| 653 |
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|a loop current rings
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|a chlorophyll-a bloom
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|a n/a
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|a mapping
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| 653 |
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|a New South Wales
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|a wind field
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|a Himawari-8
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|a typhoon
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| 653 |
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|a atmospheric correction
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| 653 |
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|a turbulent mixing
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|a Forel-Ule Scale
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| 653 |
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|a northwestern Pacific Ocean
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|a split-window algorithm
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| 653 |
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|a oil detection
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| 653 |
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|a Daya Bay Nuclear Power Plants
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|a lidar
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|a modal structure
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|a algal blooms
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|a GDPS
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|a OLCI
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|a ocean color
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|a chlorophyll-a
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|a upwelling index
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|a cloud masking
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|a barotropic instability
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|a westerly jet
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|a drifter
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| 653 |
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|a HYCOM reanalysis results
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| 653 |
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|a Arabian Gulf
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|a sea surface temperature
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|a sea surface temperatures
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| 653 |
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|a fish assemblage
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|a coastal upwelling
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|a shelf circulation
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|a topographic position index
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|a summer 2021
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|a CFOSAT
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| 653 |
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|a sea surface height
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| 653 |
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|a Kuroshio branch
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| 653 |
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|a North Pacific Subtropical High
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| 653 |
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|a quantitative mapping
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| 653 |
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|a remote sensing
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| 653 |
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|a satellite observation
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| 653 |
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|a CNN
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|a spatial and temporal changes
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|a total suspended sediment
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|a tide
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|a Sentinel-1
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|a in situ observation
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| 653 |
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|a Kuroshio intrusion
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| 653 |
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|a remote equatorial forcing
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| 653 |
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|a spatiotemporal variation
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| 653 |
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|a SAR
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| 653 |
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|a 3D-C BAM
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| 653 |
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|a spectral variability
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| 653 |
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|a spatial analysis
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|a upper ocean response
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| 653 |
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|a seagrass
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| 653 |
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|a flow pattern
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| 653 |
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|a sea level anomaly
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| 653 |
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|a energy flux
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| 653 |
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|a the Indonesian Seas
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| 700 |
1 |
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|a Liu, Antony K.
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| 700 |
1 |
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|a Li, Xiaofeng
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| 700 |
1 |
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|a Ho, Chung-Ru
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-6439-5
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/6713
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/96767
|z DOAB: description of the publication
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|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.
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