TU3.R2.1
SATELLITE-DRIVEN MACHINE LEARNING FRAMEWORK FOR SPATIO-TEMPORAL ALGAL BLOOM FORECASTING IN THE INDIAN EXCLUSIVE ECONOMIC ZONE
PUNYA P, RAMA RAO NIDAMANURI, Indian Institute of Space science and Technology, India
Session:
TU3.R2: Bloom Detection and Others Oral
Track:
Oceans
Location:
TBD
Presentation Time:
Tue, 11 Aug, 13:45 - 14:00
Presentation
Discussion
Resources
No resources available.
Session TU3.R2
TU3.R2.1: SATELLITE-DRIVEN MACHINE LEARNING FRAMEWORK FOR SPATIO-TEMPORAL ALGAL BLOOM FORECASTING IN THE INDIAN EXCLUSIVE ECONOMIC ZONE
PUNYA P, RAMA RAO NIDAMANURI, Indian Institute of Space science and Technology, India
TU3.R2.2: SURFACE CLUSTERING OF BIOARGO FLOATS DELINEATES THE VERTICAL STRUCTURE OF PHYTOPLANKTON IN THE ATLANTIC BASIN
Greg Silsbe, Victoria Coles, Xiaoxu Guo, Sairah Malkin, University of Maryland Center for Environmental Science, United States; Ratnaksha Lele, Sai Vikas Amaraneni, Vandana Janeja, University of Maryland Baltimore County, United States
TU3.R2.3: USING BIASES IN SCATTEROMETER DATA TO TRACK ASSOCIATED BIOLOGICAL SIGNALS OF BIOLUMINESCENT MILKY SEAS
Justin Hudson, Steven Miller, Colorado State University, United States
TU3.R2.4: PHYSICS-GUIDED PROBABILISTIC RED TIDE DETECTION FOR GOCI-II
Jong Hyuk Lee, Seungil Baek, Sooyoon Koh, Wonkook Kim, Pusan National university, Korea (South); Jae-Hyun Ahn, Korea Institute of Ocean Science and Technology, Republic of Korea, Korea (South)
Contacts