SMAST Seminar - DEOS - October 9, 2024 - "Optical water classification of global oceans" By: Jianwei Wei
When: Wednesday,
October 9, 2024
12:30 PM
-
1:30 PM
Where: > See description for location
Description: Department of Estuarine and Ocean Sciences
"Optical water classification of global oceans"
Jianwei Wei, Senior Remote Sensing Scientist, Global Science and Technology, Inc., NOAA/NESDIS Center for Satellite Applications and Research (STAR) at College Park, Maryland
Wednesday, October 9, 2024
12:30pm-1:30pm
Remote presentation
Stream of lecture available in SMAST E 101-102 and via Zoom
Abstract:
Satellite ocean reflectance data cover diverse water types from coastal waters to open oceans. Spectral classification of these reflectance data allows for distinguishing and grouping of water bodies with characteristic bio-optical/biogeochemical features. In this talk, I will present the new optical water class products for global oceans. The new model accounts for the hyperspectral reflectance spectral shapes and resolves the global aquatic system into two dozen water classes. These classes are separable with distinct bio-optical and biogeochemical properties, such as light absorption and scattering coefficients, Chl-a, diffuse attenuation coefficient, and suspended particulate matter. The in situ and satellite matchup data show that the satellite water class data are accurate, especially in open oceans. The satellite water classes not only exhibit features comparable to the Longhurst ocean provinces but have captured additional aspects of the water classes, including the seasonality of ocean basins. The representation of coastal/inland environments is considerably improved. How to use the water class products is always appealing. With demonstrations, the water class data are used as an indicator of the subtropical ocean gyre expansion and of the coastal water quality fluctuations. To date, the global water class data have been generated at daily and monthly levels from multiple satellites: VIIRS/SNPP, OLCI/Sentinel-3, SGLI/GCOM-C, etc. The experimental data are freely accessible.
Join Zoom Meeting
https://umassd.zoom.us/j/97440069270?pwd=L2Z1bDZESTFCKzJYZWduYVhWenYvZz09
Meeting ID: 974 4006 9270
Passcode: 428029
For additional information, please contact Callie Rumbut at c.rumbut@umassd.edu
"Optical water classification of global oceans"
Jianwei Wei, Senior Remote Sensing Scientist, Global Science and Technology, Inc., NOAA/NESDIS Center for Satellite Applications and Research (STAR) at College Park, Maryland
Wednesday, October 9, 2024
12:30pm-1:30pm
Remote presentation
Stream of lecture available in SMAST E 101-102 and via Zoom
Abstract:
Satellite ocean reflectance data cover diverse water types from coastal waters to open oceans. Spectral classification of these reflectance data allows for distinguishing and grouping of water bodies with characteristic bio-optical/biogeochemical features. In this talk, I will present the new optical water class products for global oceans. The new model accounts for the hyperspectral reflectance spectral shapes and resolves the global aquatic system into two dozen water classes. These classes are separable with distinct bio-optical and biogeochemical properties, such as light absorption and scattering coefficients, Chl-a, diffuse attenuation coefficient, and suspended particulate matter. The in situ and satellite matchup data show that the satellite water class data are accurate, especially in open oceans. The satellite water classes not only exhibit features comparable to the Longhurst ocean provinces but have captured additional aspects of the water classes, including the seasonality of ocean basins. The representation of coastal/inland environments is considerably improved. How to use the water class products is always appealing. With demonstrations, the water class data are used as an indicator of the subtropical ocean gyre expansion and of the coastal water quality fluctuations. To date, the global water class data have been generated at daily and monthly levels from multiple satellites: VIIRS/SNPP, OLCI/Sentinel-3, SGLI/GCOM-C, etc. The experimental data are freely accessible.
Join Zoom Meeting
https://umassd.zoom.us/j/97440069270?pwd=L2Z1bDZESTFCKzJYZWduYVhWenYvZz09
Meeting ID: 974 4006 9270
Passcode: 428029
For additional information, please contact Callie Rumbut at c.rumbut@umassd.edu
Contact: > See Description for contact information
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