A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data-Reference-Cited by-同舟云学术

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A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data

Published:2020 Issue: Volume:13 Page:6220-6235
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Krishna Kande Vamsi^ORCID,Shanmugam Palanisamy^ORCID,Nagamani Pullaiahgari Venkata^ORCID

Funder

Indian Institute of Technology Madras

MHRD fellowship

ISRO-IITM Cell

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/8994817/09205257.pdf?arnumber=9205257

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatiotemporal weighted neural network reveals surface seawater pCO2 distributions and underlying environmental mechanisms in the North Pacific Ocean;International Journal of Applied Earth Observation and Geoinformation;2024-09

2. Deep learning-based forecasting model for chlorophyll-a response to tropical cyclones in the Western North Pacific;Ocean Modelling;2024-06

3. Using satellite imagery to estimate CO2 partial pressure and exchange with the atmosphere in the Songhua River;Journal of Hydrology;2024-05

4. Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data;IEEE Transactions on Geoscience and Remote Sensing;2024

5. Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar;IEEE Transactions on Geoscience and Remote Sensing;2024

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