Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects-Reference-Cited by-同舟云学术

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Published:2022-02 Issue:2 Volume:10 Page:
ISSN:2328-4277
Container-title:Earth's Future
language:en
Short-container-title:Earth's Future

Author:

Chen Jinyue¹²³⁴^ORCID,Chen Shuisen¹³⁴^ORCID,Fu Rao⁵^ORCID,Li Dan¹³,Jiang Hao¹³^ORCID,Wang Chongyang¹³,Peng Yongshi¹,Jia Kai¹³,Hicks Brendan J.⁶

Affiliation:

1. Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou China

2. Environment Research Institute Shandong University Qingdao China

3. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

4. Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China

5. Institute of Blue and Green Development Shandong University Weihai China

6. Division of Health, Engineering, Computing and Science Environmental Research Institute School of Science The University of Waikato Hamilton New Zealand

Publisher

American Geophysical Union (AGU)

Subject

Earth and Planetary Sciences (miscellaneous),General Environmental Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021EF002289

Reference156 articles.

1. Artificial Neural Networks

2. A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data

3. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

4. Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas

5. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient

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