TY - JOUR
T1 - Spatio-temporal Analysis of Total Suspended Solids Concentration in Porong River Estuary using Sentinel-2 Satellite Imageries
AU - Kirmandi, Muhammad Bagus
AU - Syariz, Muhammad Aldila
AU - Qomariyah, Lailatul
AU - Heriza, Dewinta
AU - Van Nguyen, Manh
AU - Denaro, Lino Garda
N1 - Publisher Copyright:
© 2024 Institute of Physics Publishing. All rights reserved.
PY - 2024
Y1 - 2024
N2 - The Sidoarjo Mudflow has been channelled into Porong River since its firstly occurred in 2006. The mud endangers the river not only physically due to sedimentation, but also biologically due to mud contamination. Therefore, monitoring of water quality both spatial- and temporally is important to ensure the life below water in the river. The main water indicator for mud is total suspended solid (TSS) which was further used in this study. The TSS concentration was modelled using a simple linear regression and estimated by utilizing Sentinel-2 imageries with limited amount of in-situ water samples. The model was then compared to existing related models quantitatively by means of root mean squared error (RMSE) and normalized mean absolute error (NMAE). The results showed that the stepwise evaluation extracts vegetation red edge band (band 8A) of Sentinel 2 with central wavelength of 865 nm as the best regressor. After the implementation, the model outperforms the existing models with RMSE of 10.893 mg/L.
AB - The Sidoarjo Mudflow has been channelled into Porong River since its firstly occurred in 2006. The mud endangers the river not only physically due to sedimentation, but also biologically due to mud contamination. Therefore, monitoring of water quality both spatial- and temporally is important to ensure the life below water in the river. The main water indicator for mud is total suspended solid (TSS) which was further used in this study. The TSS concentration was modelled using a simple linear regression and estimated by utilizing Sentinel-2 imageries with limited amount of in-situ water samples. The model was then compared to existing related models quantitatively by means of root mean squared error (RMSE) and normalized mean absolute error (NMAE). The results showed that the stepwise evaluation extracts vegetation red edge band (band 8A) of Sentinel 2 with central wavelength of 865 nm as the best regressor. After the implementation, the model outperforms the existing models with RMSE of 10.893 mg/L.
UR - http://www.scopus.com/inward/record.url?scp=85213861514&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/1418/1/012070
DO - 10.1088/1755-1315/1418/1/012070
M3 - Conference article
AN - SCOPUS:85213861514
SN - 1755-1307
VL - 1418
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012070
T2 - 9th Geomatics International Conference 2024, GeoICON 2024
Y2 - 24 July 2024
ER -