TU2.R2.3
Modeling Satellite-Inferred Chlorophyll Variability with SHAP-Interpretable Machine Learning
Jing Tan, Robert Frouin, Scripps Institution of Oceanography
Session:
TU2.R2: Retrieval of Water's Bio-optical Properties Oral
Track:
Oceans
Location:
Northwest
Presentation Time:
Tuesday, 11 August, 11:30 - 11:45
Session Co-Chairs:
Eurico D'Sa, Louisiana State University and Robert Frouin, University of California San Diego
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; Masoud Moradi, Iranian National Institute of Oceanography and Atmospheric Science
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; Bingqing Liu, Florida State University; Nabid Hashar, Louisiana State University
TU2.R2.3: Modeling Satellite-Inferred Chlorophyll Variability with SHAP-Interpretable Machine Learning
Jing Tan, Robert Frouin, Scripps Institution of Oceanography
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; Eunna Jang, Korea Institute of Ocean Science and Technology; Jaese Lee, Daehyeon Han, Jungho Im, Ulsan National Institute of Science and Technology
TU2.R2.5: EFFICIENT SPATIOTEMPORAL CHARACTERISATION OF RIVER PLUMES USING DEEP LEARNING: THE CASE OF SURUGA BAY, JAPAN
Takashi Kobayashi, Kirara Kotani, Kota Sato, Masaki Hisada, NTT, Inc. Space Environment and Energy Laboratories
Resources
No resources available.