Improved Water Chlorophyll-A Retrieval Method Based On Mixture Density Networks Using In-Situ Hyperspectral Remote Sensing Data-Reference-Cited by-同舟云学术

Improved Water Chlorophyll-A Retrieval Method Based On Mixture Density Networks Using In-Situ Hyperspectral Remote Sensing Data

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Dehkordi Alireza Taheri¹,Javad Valadan Zoej Mohammad¹,Chegoonian Amir Masoud²,Mehran Ali³,Jafari Mohsen⁴

Affiliation:

1. K. N. Toosi University of Technology,Faculty of Geomatics Engineering,Department of Photogrammetry and Remote Sensing,Tehran,Iran

2. University of Waterloo,Department of Geography and Environmental Management,Canada

3. San Jose State University,Department of Civil and Environmental Engineering,California,USA

4. Shiraz University,School of Engineering,Department of Civil and Environmental Engineering,Shiraz,Iran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10283009.pdf?arnumber=10283009

Reference19 articles.

1. Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation;yan;Environmental Pollution,2022

2. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach

3. Application Of Different Simulated Spectral Data And Machine Learning To Estimate The Chlorophyll A Concentration Of Several Inland Waters

4. Sensitivity of remotely sensed pigment concentration via Mixture Density Networks (MDNs) to uncertainties from atmospheric correction;zolfaghari;Journal of Great Lakes Research,2022

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