Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)

Empirical vs Semi-Analytic Model for Total Suspended Solid Detection

Authors
Pingkan Mayestika Afgatiani1, *, Syifa Wismayati Adawiah2, Syarif Budhiman1
1Research Centre for Remote Sensing, Research Organization for Aeronautics and Space, National Research and Innovation Agency (BRIN), Depok City, Indonesia
2Directorate of Environment, Maritime, Natural Resources, and Nuclear Policy, Deputy for Development Policy, National Research and Innovation Agency (BRIN), Maritime, Indonesia
*Corresponding author. Email: pingkan.mayestika.afgatiani@brin.go.id
Corresponding Author
Pingkan Mayestika Afgatiani
Available Online 28 December 2022.
DOI
10.2991/978-94-6463-086-2_94How to use a DOI?
Keywords
Water quality; Suspended matter; Water pollution; Landsat-8 satellite
ABSTRACT

Many parameters are used to determine the quality of waters, one of which is Total Suspended Solids (TSS). In addition to direct data measurement and field sampling, TSS concentrations can also be estimated from satellite data by developing models based on the reflectance of the light received by the sensor. This article aims to compare two available empirical and semi-analytic models that can estimate TSS concentrations in Bekasi coastal waters using Landsat 8 and Sentinel 2 satellite data. The empirical model was made in 2018 in Bekasi coastal waters, while the semi-analytic model was created in 2004 in the Mahakam coastal waters. As a validation using field data from the Bekasi coastal waters, taken in 2019. The results showed that the semi-analytic model has a smaller error value than the empirical model, with RMSE 51.4 mg/l and 585777.2 mg/l respectively. The result indicates that the semi-analytic model can better estimate TSS even though it is applied at different times and locations. In contrast, the empirical model shows a very high error even though it is used in the same area when the empirical model was created. For further study, we tried to apply the semi-analytic model to the Sentinel-2 image, and it was found that the semi-analytic model also has good capabilities with a lower RMSE value of 44.1 mg/l. In conclusion, the semi-analytic model is better for extracting TSS information than the empirical model.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)
Series
Advances in Biological Sciences Research
Publication Date
28 December 2022
ISBN
978-94-6463-086-2
ISSN
2468-5747
DOI
10.2991/978-94-6463-086-2_94How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Pingkan Mayestika Afgatiani
AU  - Syifa Wismayati Adawiah
AU  - Syarif Budhiman
PY  - 2022
DA  - 2022/12/28
TI  - Empirical vs Semi-Analytic Model for Total Suspended Solid Detection
BT  - Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)
PB  - Atlantis Press
SP  - 711
EP  - 716
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-086-2_94
DO  - 10.2991/978-94-6463-086-2_94
ID  - Afgatiani2022
ER  -