Estimating chlorophyll-a concentrations in tropical reservoirs from band-ratio machine learning models-Reference-Cited by-同舟云学术

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Estimating chlorophyll-a concentrations in tropical reservoirs from band-ratio machine learning models

Published:2022-01 Issue: Volume:25 Page:100678
ISSN:2352-9385
Container-title:Remote Sensing Applications: Society and Environment
language:en
Short-container-title:Remote Sensing Applications: Society and Environment

Author:

Chusnah Wachidatin Nisaul,Chu Hone-Jay

Funder

Ministry of Science and Technology, Taiwan

U.S. Environmental Protection Agency

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geography, Planning and Development

Reference55 articles.

1. Efficient water quality prediction using supervised machine learning;Ahmed;Water,2019

2. Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring;Boucher;Ecol. Appl.,2018

3. Improved red-edge chlorophyll-a detection for Sentinel 2;Bramich;Ecol. Indicat.,2021

4. Optical water quality model of Lake Ontario 2: determination of chlorophyll a and suspended mineral concentrations of natural waters from submersible and low altitude optical sensors;Bukata;Appl. Opt.,1981

5. Hybrid chlorophyll-a algorithm for assessing trophic states of a tropical brazilian reservoir based on msi/sentinel-2 data;Cairo;Rem. Sens.,2020

Cited by 10 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. Benefits of multiple remotely sensed datasets and machine learning models to predict the Chlorophyll-a concentration in the Nakdong River, South Korea;2024-02-19

2. Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review;Critical Reviews in Environmental Science and Technology;2023-11-29

3. Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring;Environments;2023-10-02

4. An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies;Drones;2023-08-23

5. Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality;Environmental Science and Pollution Research;2023-07-19

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