A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery-Reference-Cited by-同舟云学术

A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

Published:2020-12-20 Issue:5 Volume:42 Page:1841-1866
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Guo Hongwei¹^ORCID,Huang Jinhui Jeanne¹,Chen Bowen¹,Guo Xiaolong²,Singh Vijay P.³⁴

Affiliation:

1. College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, PR China

2. Department of Urban Management and Eco-protection, Tianjin High-Tech Area, Tianjin, PR China

3. Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX, USA

4. National Water Centre, UAE University, Al Ain, UAE

Funder

National Key Research and Development Program of China

Ministry of Science and Technology of the People’s Republic of China

Science and Technology Commission of Tianjin Binhai New Area

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1846222

Reference62 articles.

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5. A novel framework for the use of remote sensing for monitoring catchments at continental scales

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