Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Estimation of Suspended Particulate Matter Using Landsat 9 Imagery: Generating Algorithms and Spatio-Temporal Distributions

ILMU KELAUTAN: Indonesian Journal of Marine Sciences · Vol. 29 No. 1 (2024) · pp. 15–28

DOI
10.14710/ik.ijms.29.1.15-28
ISSN
2406-7598, 0853-7291
Published
2024-03-27
Publisher
Marine Science Department Diponegoro University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The fluctuation of suspended particulate matter (SPM) is essential to the biogeochemical cycle and ecological health of coastal waters. Anthropogenic activities potentially trigger an increase in SPM, so it needs to be monitored continuously. Spatial and temporal monitoring of SPM can be carried out cost-effectively with broad coverage using a remote sensing application. This study aims to build the SPM algorithm and estimate its temporal variability. The algorithm model in this study is based on an empirical formula between field data and reflectance data with the same acquisition. Water samples were taken from 100 stations in July 2022. Half were used for model building and the other for model validation. Suspended Particulate Matter was determined gravimetrically and estimated their temporal variability was based on Landsat 9 image records from December 2021 - November 2022. The results of the analysis show that the best algorithm for SPM estimation can be built based on coastal aerosol canals (B1), blue canals (B2), and green canals (B3) with the accuracy test result of (R2 = 0.68; RMSE = 5.551 mg.L-1; MAPE= 7.07%; Bias= 0.28). The SPM temporal fluctuations were generally higher in the west monsoon and lowered in the east monsoon, ranging from 30 to 70 mg.L-1. On the other hand, the spatial distribution shows a higher magnitude in the estuary than in the offshore waters, with a deviation of about 30 mg.L-1. Regional authorities can use the results obtained to improve the management of coastal water quality and monitoring systems.

Keywords

Collaboration network of the authors

40 authors · 130 co-authorship links · 2 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Diponegoro University
  • Universitas Gadjah Mada
  • Cenderawasih University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Elis IndrayantiAffiliation unknown6
Parichat WetchayontAffiliation unknown4
Ruzana DhiauddinAffiliation unknown3
Kunarso KunarsoAffiliation unknown3
Muhammad ZainuriDiponegoro University3
Denny Nugroho SugiantoAffiliation unknown3
Aris IsmantoAffiliation unknown3
Anis Yasmin SabilaAffiliation unknown3
Indra Budi PrasetyawanAffiliation unknown3
Gunardi KusumahAffiliation unknown2
Wisnu Arya GemilangAffiliation unknown2
Guntur Adhi RahmawanAffiliation unknown2
Ibnu PratiktoAffiliation unknown2
Riza Yuliratno SetiawanUniversitas Gadjah Mada2
Muhammad HelmiDiponegoro University2
Semeidi HusrinAffiliation unknown2
Purwanto PurwantoCenderawasih University2
Galuh PermatasariAffiliation unknown2
Warsito AtmodjoAffiliation unknown2
Azis RifaiAffiliation unknown2
Haryanto .Affiliation unknown1
Siti MaesarohAffiliation unknown1
Siti Nurindah SariAffiliation unknown1
Muhammad Rehan AnwarAffiliation unknown1
Aprizon PutraAffiliation unknown1
Koko OndaraAffiliation unknown1
Dwi Haryo IsmunartiAffiliation unknown1
Bayu MunandarAffiliation unknown1
Raden Dwi SusantoAffiliation unknown1
Chrisna Adhi SuryonoAffiliation unknown1
Sigit PurnomoAffiliation unknown1
Petrus SubardjoAffiliation unknown1
Jusup SuprijantoDiponegoro University1
Chrisna Adi SuryonoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ejournal.undip.ac.id/index.php/ijms/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Estimation of Suspended Particulate Matter Using  Landsat 9 Imagery: Generating Algorithms and Spatio-Temporal Distributions</dc:title>
	<dc:creator>Maslukah, Lilik</dc:creator>
	<dc:creator>Sadewo, M. Firouz Dimas</dc:creator>
	<dc:creator>Rochaddi, Baskoro</dc:creator>
	<dc:creator>Widada, Sugeng</dc:creator>
	<dc:creator>Wirasatriya, Anindya</dc:creator>
	<dc:creator>Wisha, Ulung Jantama</dc:creator>
	<dc:subject xml:lang="en-US">Algorithm; spatial analysis; Landsat 9; Banjir Kanal Timur</dc:subject>
	<dc:description xml:lang="en-US">The fluctuation of suspended particulate matter (SPM) is essential to the biogeochemical cycle and ecological health of coastal waters. Anthropogenic activities potentially trigger an increase in SPM, so it needs to be monitored continuously. Spatial and temporal monitoring of SPM can be carried out cost-effectively with broad coverage using a remote sensing application. This study aims to build the SPM algorithm and estimate its temporal variability. The algorithm model in this study is based on an empirical formula between field data and reflectance data with the same acquisition. Water samples were taken from 100 stations in July 2022. Half were used for model building and the other for model validation. Suspended Particulate Matter was determined gravimetrically and estimated their temporal variability was based on Landsat 9 image records from December 2021 - November 2022. The results of the analysis show that the best algorithm for SPM estimation can be built based on coastal aerosol canals (B1), blue canals (B2), and green canals (B3) with the accuracy test result of (R2 = 0.68; RMSE = 5.551 mg.L-1; MAPE= 7.07%; Bias= 0.28). The SPM temporal fluctuations were generally higher in the west monsoon and lowered in the east monsoon, ranging from 30 to 70 mg.L-1. On the other hand, the spatial distribution shows a higher magnitude in the estuary than in the offshore waters, with a deviation of about 30 mg.L-1. Regional authorities can use the results obtained to improve the management of coastal water quality and monitoring systems. </dc:description>
	<dc:publisher xml:lang="en-US">Marine Science Department Diponegoro University</dc:publisher>
	<dc:date>2024-03-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejournal.undip.ac.id/index.php/ijms/article/view/56599</dc:identifier>
	<dc:identifier>10.14710/ik.ijms.29.1.15-28</dc:identifier>
	<dc:source xml:lang="en-US">ILMU KELAUTAN: Indonesian Journal of Marine Sciences; Vol 29, No 1 (2024): Ilmu Kelautan; 15-28</dc:source>
	<dc:source>2406-7598</dc:source>
	<dc:source>0853-7291</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejournal.undip.ac.id/index.php/ijms/article/view/56599/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 ILMU KELAUTAN: Indonesian Journal of Marine Sciences</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>