An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters

Surya Prakash Tiwari; Abdul Azeez S

doi:10.1109/InGARSS59135.2023.10490323

An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters

Surya Prakash Tiwari^*
, Abdul Azeez S

^*Corresponding author for this work

Applied Research Center for Environment and Marine Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Machine Learning (ML) offers a modern approach for estimating water quality parameters from aquatic remote sensing. We compared three ML algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boost Regression (GBR), assessing their suitability for estimating chlorophyll (Chl) using remote sensing reflectance (Rrs) in coastal and open waters. We trained and validated the models using a global in-situ dataset from the NOMAD database, encompassing Chl and Rrs values. Chl predictions from all models closely aligned with the in-situ dataset ranges. Comparing the ML models, GBR exhibited better Chl predictions with lower root mean square error and high correlation values. We applied the GBR model to Sentinel-2A/B images to analyse coastal waters in Saudi Arabia. Using five years of Sentinel-2A/B images (2017-2021), we observed Chl changes driven by seasonal variations. This study highlights the adaptability of ML algorithms for aquatic remote sensing.

Original language	English
Title of host publication	2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350325591
DOIs	https://doi.org/10.1109/InGARSS59135.2023.10490323
State	Published - 2023
Event	3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023 - Bangalore, India Duration: 10 Dec 2023 → 13 Dec 2023

Publication series

Name	2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

Conference

Conference	3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023
Country/Territory	India
City	Bangalore
Period	10/12/23 → 13/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Aquatic Remote Sensing
Chlorophyll
GBR
ML
Remote Sensing Reflectance

ASJC Scopus subject areas

Computer Networks and Communications
Earth and Planetary Sciences (miscellaneous)
Earth-Surface Processes
Space and Planetary Science
Aerospace Engineering
Instrumentation

Access to Document

10.1109/InGARSS59135.2023.10490323

Cite this

Tiwari, S. P., & Azeez S, A. (2023). An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters. In 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023 (2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/InGARSS59135.2023.10490323

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title = "An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters",

abstract = "Machine Learning (ML) offers a modern approach for estimating water quality parameters from aquatic remote sensing. We compared three ML algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boost Regression (GBR), assessing their suitability for estimating chlorophyll (Chl) using remote sensing reflectance (Rrs) in coastal and open waters. We trained and validated the models using a global in-situ dataset from the NOMAD database, encompassing Chl and Rrs values. Chl predictions from all models closely aligned with the in-situ dataset ranges. Comparing the ML models, GBR exhibited better Chl predictions with lower root mean square error and high correlation values. We applied the GBR model to Sentinel-2A/B images to analyse coastal waters in Saudi Arabia. Using five years of Sentinel-2A/B images (2017-2021), we observed Chl changes driven by seasonal variations. This study highlights the adaptability of ML algorithms for aquatic remote sensing.",

keywords = "Aquatic Remote Sensing, Chlorophyll, GBR, ML, Remote Sensing Reflectance",

author = "Tiwari, \{Surya Prakash\} and \{Azeez S\}, Abdul",

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year = "2023",

doi = "10.1109/InGARSS59135.2023.10490323",

language = "English",

series = "2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023",

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Tiwari, SP & Azeez S, A 2023, An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters. in 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023. 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023, Bangalore, India, 10/12/23. https://doi.org/10.1109/InGARSS59135.2023.10490323

An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters. / Tiwari, Surya Prakash; Azeez S, Abdul.
2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Tiwari, Surya Prakash

AU - Azeez S, Abdul

PY - 2023

Y1 - 2023

N2 - Machine Learning (ML) offers a modern approach for estimating water quality parameters from aquatic remote sensing. We compared three ML algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boost Regression (GBR), assessing their suitability for estimating chlorophyll (Chl) using remote sensing reflectance (Rrs) in coastal and open waters. We trained and validated the models using a global in-situ dataset from the NOMAD database, encompassing Chl and Rrs values. Chl predictions from all models closely aligned with the in-situ dataset ranges. Comparing the ML models, GBR exhibited better Chl predictions with lower root mean square error and high correlation values. We applied the GBR model to Sentinel-2A/B images to analyse coastal waters in Saudi Arabia. Using five years of Sentinel-2A/B images (2017-2021), we observed Chl changes driven by seasonal variations. This study highlights the adaptability of ML algorithms for aquatic remote sensing.

AB - Machine Learning (ML) offers a modern approach for estimating water quality parameters from aquatic remote sensing. We compared three ML algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boost Regression (GBR), assessing their suitability for estimating chlorophyll (Chl) using remote sensing reflectance (Rrs) in coastal and open waters. We trained and validated the models using a global in-situ dataset from the NOMAD database, encompassing Chl and Rrs values. Chl predictions from all models closely aligned with the in-situ dataset ranges. Comparing the ML models, GBR exhibited better Chl predictions with lower root mean square error and high correlation values. We applied the GBR model to Sentinel-2A/B images to analyse coastal waters in Saudi Arabia. Using five years of Sentinel-2A/B images (2017-2021), we observed Chl changes driven by seasonal variations. This study highlights the adaptability of ML algorithms for aquatic remote sensing.

KW - Aquatic Remote Sensing

KW - Chlorophyll

KW - GBR

KW - ML

KW - Remote Sensing Reflectance

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DO - 10.1109/InGARSS59135.2023.10490323

M3 - Conference contribution

AN - SCOPUS:85190967505

T3 - 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

BT - 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 10 December 2023 through 13 December 2023

ER -

Tiwari SP, Azeez S A. An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters. In 2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023). doi: 10.1109/InGARSS59135.2023.10490323

An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this