TU2.R2.1
Improving the Near Infrared to Red band ratio Algorithms for Remote Sensing Estimation of Chlorophyll-a in Highly Turbid Coastal Waters
Behnaz Arabi, Meng Lu, University of Bayreuth, Germany; Masoud Moradi, Iranian National Institute of Oceanography and Atmospheric Science, Iran
Session:
TU2.R2: Retrieval of Water's Bio-optical Properties Oral
Track:
Oceans
Location:
TBD
Presentation Time:
Tue, 11 Aug, 11:00 - 11:15
Presentation
Discussion
Resources
No resources available.
Session TU2.R2
TU2.R2.1: Improving the Near Infrared to Red band ratio Algorithms for Remote Sensing Estimation of Chlorophyll-a in Highly Turbid Coastal Waters
Behnaz Arabi, Meng Lu, University of Bayreuth, Germany; Masoud Moradi, Iranian National Institute of Oceanography and Atmospheric Science, Iran
TU2.R2.2: Dissolved organic matter dynamics in a large river-dominated coastal margin from PACE-OCI using an adaptive quasi-analytic algorithm
Eurico D'Sa, Louisiana State University, United States; Bingqing Liu, Florida State University, United States; Nabid Hashar, Louisiana State University, United States
TU2.R2.3: Modeling Satellite-Inferred Chlorophyll Variability with SHAP-Interpretable Machine Learning
Jing Tan, Robert Frouin, Scripps Institution of Oceanography, United States
TU2.R2.4: Robust daily satellite sea surface salinity reconstruction using deep learning in low-salinity coastal regions
Sihun Jung, So-Hyun Kim, Ulsan National Institute of Science and Technology, Korea (South); Eunna Jang, Korea Institute of Ocean Science and Technology, Korea (South); Jaese Lee, Daehyeon Han, Jungho Im, Ulsan National Institute of Science and Technology, Korea (South)
Contacts