Application Of Remote Sensing For Estimating Suspended Sediment Concentration And Identifying Critical Erosion-Prone Sub-Basins In The Gamasyab Watershed

Parvizi, Sara; Hosseini, Seyed Zeynalabedin; Talebi, Ali; Aghayari, Jamal; Behifar, Maede

doi:10.48311/ecopersia.2026.122329.82936

Application Of Remote Sensing For Estimating Suspended Sediment Concentration And Identifying Critical Erosion-Prone Sub-Basins In The Gamasyab Watershed

Document Type : Original Article

Authors

Sara Parvizi ¹

Seyed Zeynalabedin Hosseini ²

Ali Talebi ²

Jamal Aghayari ³

Maede Behifar ³

¹ Department of Rangeland and Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran

² yazd university

³ Iranian Space Research Institute Research Center, Tehran, Iran.

10.48311/ecopersia.2026.122329.82936

Abstract

Aims: Large amounts of fertile soil are lost annually due to erosion, and significant losses are caused by sedimentation. In this context, remote sensing is an efficient tool for monitoring erosion and sedimentation because it offers wide coverage at low cost. This study evaluated the applicability of Sentinel-2-based empirical suspended sediment concentration (SSC) estimation in the data-limited Gamasyab watershed. It used the derived spatial SSC patterns to prioritize sediment-prone sub-watersheds relatively.
Materials & Methods: Sentinel-2 images from 2016–2020 were quality-checked, and cloud- or error-affected scenes were excluded. We extracted spectral reflectance at the Pole Chehr station, accounting for river width and band resolution, and compared it with field suspended sediment concentration (SSC) data to develop linear, exponential, and polynomial models. We used the best-performing model, an exponential model based on Band 5, to estimate SSC for six dates and rank the sub-watersheds.
Findings: The Band 5 exponential model showed the best calibration performance (R² = 0.83, RMSE = 23.19, MAD = 16.92). Estimated SSC indicated that sub-watershed 1 had the highest concentrations among the five sub-watersheds, ranging from 31.20 to 640 mg.L⁻¹, likely due to highly erodible geological formations. Sub-watershed 2 showed intermediate SSC values, whereas sub-watersheds 3–5 were classified as medium- to low-erodibility areas.
Conclusion: The results indicate that Sentinel-2, particularly band 5, effectively estimates SSC and identifies erosion-prone areas in the Gamasyab watershed, with sub-watershed 1 identified as the highest-priority sediment-prone area based on satellite-derived SSC patterns.

Keywords

Sediment Hotspots

Sentinel-2 MSI

Spectral Band Analysis

Suspended Sediment Concentration

Watershed Prioritization

Subjects

Ecohydrology

1.Saeediyan H., Moradi H.R. Comparison of the runoff and sediment of different land uses in Gachsaran and Aghajari formations under rain simulation. Water Soil Manag. Model. 2022; 2(2):55-68. (In Persian).
2. Pimentel D., Harvey C., Resosudarmo P., Sinclair K., Kurz D., McNair M., Crist S., Shpritz L., Fitton L., Saffouri R., Blair R. Environmental and economic costs of soil erosion and conservation benefits. Science 1995; 267(5201):1117-1123.
3. Perera D., Williams S., Smakhtin V. Present and future losses of storage in large reservoirs due to sedimentation: a country-wise global assessment. Sustainability 2023; 15(1): 219 .
4. Sadeghi S.H.R. Soil erosion in Iran: State of the art, tendency and solutions. Agric. For. 2017; 63(3):33-37.
5. Tabatabaee M., Soleymani K. Estimation of fluvial suspended sediment concentration using MODIS sensor (case study: Mollasani Hydrometry Station). Irrig. Sci. Eng. 2014; 36(2):83-95. (In Persian).
6. Rodríguez-Guzmán V., Gilbes-Santaella F. Using MODIS 250 m imagery to estimate total suspended sediment in a tropical open bay. Int. J. Syst. Appl. Eng. Dev. 2009; 3(1):36-44.
7. Ritchie J.C., Schiebe F.R. Water quality. In: Schultz G.A., Engman E.T. editors. Remote Sensing in Hydrology and Water Management. Berlin, Heidelberg: Springer-Verlag; 2000. p. 287-303
8. Wackerman C., Hayden A., Jonik J. Deriving spatial and temporal context for point measurements of suspended sediment concentration using remote-sensing imagery in the Mekong Delta. Cont. Shelf Res. 2017; 147(1):231-245.
9. Caballero I., Steinmetz F., Navarro G. Evaluation of the first year of operational Sentinel-2A data for retrieval of suspended solids in medium- to high-turbidity waters. Remote Sens. 2018; 10(7):982.
10. Marinho R.R., Harmel T., Martinez J.M., Filizola Junior N.P. Spatiotemporal dynamics of suspended sediments in the Negro River, Amazon Basin, from in situ and Sentinel-2 remote sensing data. ISPRS Int. J. Geo-Inf. 2021; 10(2):86.
11. Zhang M., Dong Q., Cui T., Xue C., Zhang S. Suspended sediment monitoring and assessment for Yellow River estuary from Landsat TM and ETM+ imagery. Remote Sens. Environ. 2014; 146(1):136-147.
12. Paulista R.S.D., de Almeida F.T., de Souza A.P., Hoshide A.K., De Abreu D.C., Da Silva Araújo J.W., Martim C.C. Estimating suspended sediment concentration using remote sensing for the Teles Pires River, Brazil. Sustainability. 2023; 15(9):7049.
13. Parvizi S., Hosseini S.Z., Talebi A., Talebi Esfandarani S., Jalili H., Zakerinejad R. The relationship between the suspended sediment concentration of Doab Mereg and Pol Chehr hydrometric stations with vegetation in upstream areas using Sentinel-2 MSI images. J. Watershed Manag. Res. 2024; 15(1):147-158. (In Persian).
14. Song G., Jiang Y., Lei X., Zhai S. Measurement of suspended sediment concentration at the outlet of the Yellow River Canyon using Sentinel-2 images and machine learning. Remote Sens. 2025;17(14):2424.
15. Zabihi Silabi M., Sadeghi S.H.R., Vafakhah M. Soil erosion elasticity initiative for prioritizing sub-watersheds. Int. Soil Water Conserv. Res. 2025; 13(2):277-289.
16. Cao B., Yang X., Ran L., Chen S., Park E., Picco L. Monitoring suspended sediment longitudinal fluctuations along the Pearl River. Catena 2026; 262:109648.
17. Nones M., Guo C. Remote sensing as a support tool to map suspended sediment concentration over extended river reaches. Acta Geophys 2025; 73(5):4655-4668.
18. Fang X., Zhang J., Yu X., Zhang S., Kong D., Wang X., Ali S., Ullah H., Xu N. Accurate estimation of suspended sediment concentration integrated remote sensing information and a novel stacking machine learning model. Stoch. Environ. Res. Risk Assess. 2025; 39(4):1517-1535.
19. Al-Hantouli F., Awawdeh M., Obeidat M. Soil erosion prioritization of Yarmouk River Basin, Jordan, using multiple approaches in a GIS environment. In: Water Resources Management and Sustainability: Solutions for Arid Regions. Cham: Springer; 2023; 121(1):291-318.
20. Benzougagh B., Meshram S.G., Dridri A., Boudad L., Baamar B., Sadkaoui D., Khedher K.M. Identification of critical watershed at risk of soil erosion using morphometric and geographic information system analysis. Appl. Water Sci. 2022; 12(1):8.
21. Chitsaz N., Malekian A. Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran). Environ. Earth Sci. 2016; 75(22):1448.
22. Zarezadeh Mehrizi S., Khoorani A., Bazrafshan J., Bazrafshan O. Assessing the efficiency of the SWAT model for runoff simulation in Gamasiyab watershed. J. Range Watershed Manag. 2018; 70(4):881-893.(In Persian).
23. Louis J., Debaecker V., Pflug B., Main-Knorn M., Bieniarz J., Mueller-Wilm U., Cadau E., Gascon F. Sentinel-2 Sen2Cor: L2A processor for users. In: Ouwehand L., editor. Proceedings of the Living Planet Symposium 2016; 2016 May 9-13; Prague, Czech Republic. Noordwijk (Netherlands): European Space Agency; 2016; SP-740:1-8.
24. Pahlevan N., Sarkar S., Franz B.A., Balasubramanian S.V., He J. Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: demonstrations and validations. Remote Sens. Environ. 2017; 201(1):47-56.
25. Long C.M., Pavelsky T.M. Remote sensing of suspended sediment concentration and hydrologic connectivity in a complex wetland environment. Remote Sens. Environ. 2013; 129(1):197-209.
26. Dekker A.G., Vos R.J., Peters S.W.M. Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int. J. Remote Sens. 2002; 23(1):15-35.
27. Yu Z., Wang J., Li Y., Shum C.K., Wang B., He X., Xu H., Xu Y., Zhou B. Remote sensing of suspended sediment in high-turbidity estuary from Sentinel-3A/OLCI: a case study of Hangzhou Bay. Front. Mar. Sci. 2022; 9(1):1008070.
28. Binding C.E., Bowers D.G., Mitchelson-Jacob E.G. Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters: the impact of variable particle scattering properties. Remote Sens. Environ. 2005; 94(3):373-383.
29. Ouillon S., Douillet P., Andréfouët S. Coupling satellite data with in situ measurements and numerical modeling to study fine suspended-sediment transport: a study for the lagoon of New Caledonia. Coral Reefs. 2004; 23(1):109-122.