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Empirical modelling of suspended sediments using spectral data from spectroradiometer and sentinel-2 in Mula Dam Reservoir, Maharashtra, India

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Title: Empirical modelling of suspended sediments using spectral data from spectroradiometer and sentinel-2 in Mula Dam Reservoir, Maharashtra, India
Authors: J. K. Joshi, A. A. Atre, S. B. Nandgude, M. G. Shinde, A. G. Durgude, S. D. Gorantiwar, M. R. Patil
Source: Scientific Reports, Vol 15, Iss 1, Pp 1-20 (2025)
Publisher Information: Nature Portfolio, 2025.
Publication Year: 2025
Collection: LCC:Medicine
LCC:Science
Subject Terms: Suspended sediment concentration (SSC), Band ratio/ index, Spectral integration, Sentinel-2 MSI, Regression modelling, Transitive relation model, Medicine, Science
Description: Abstract This study presents a novel methodology for estimating Suspended Sediment Concentration (SSC) in the Mula Dam reservoir, Maharashtra, by integrating in-situ hyperspectral reflectance with Sentinel-2 satellite imagery. While conventional remote sensing techniques or field-based spectroscopy have been employed independently for SSC monitoring, this research introduces a spectral integration framework that bridges these two data sources through a transitive relation model. Field data collection was conducted from October 2021 to February 2022, during which 121 surface water samples were obtained and their spectral signatures recorded using an SVC HR-1024i Spectroradiometer. Simultaneously, Sentinel-2 MSI Level 2A images were processed to extract spectral reflectance at corresponding sampling points. Strong correlations were observed between SSC and reflectance in the Green, Red, and Red Edge 1 bands. Multiple spectral indices and band ratios were evaluated to identify optimal SSC estimators, with the combination (Green × Red Edge 1)/Red demonstrating the highest predictive capability. A spectral integration function was developed using a two-stage regression approach: first, linking observed SSC to Spectroradiometer-derived indices; second, connecting these indices to Sentinel-2 reflectance data. The resulting models were validated using linear regression, Student’s t-test, residual analysis, and k-fold cross-validation. Among all models, the (Green × Red Edge 1)/Red function achieved superior performance with an R2 of 0.80, RMSE of 8.58 mg/L, and MAPE of 19.41%. The approach was further tested for temporal SSC mapping using past Sentinel-2 imagery, revealing seasonal sediment trends. The study concludes that the proposed spectral integration method provides a robust, scalable, and transferable framework for accurate SSC monitoring in large water bodies. This advancement holds significant implications for sediment management, water quality assessment, and hydrological research.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-15719-w
Access URL: https://doaj.org/article/1b1e7cfd9064458aa66e1e7ea786dbc9
Accession Number: edsdoj.1b1e7cfd9064458aa66e1e7ea786dbc9
Database: Directory of Open Access Journals
Description
Abstract:Abstract This study presents a novel methodology for estimating Suspended Sediment Concentration (SSC) in the Mula Dam reservoir, Maharashtra, by integrating in-situ hyperspectral reflectance with Sentinel-2 satellite imagery. While conventional remote sensing techniques or field-based spectroscopy have been employed independently for SSC monitoring, this research introduces a spectral integration framework that bridges these two data sources through a transitive relation model. Field data collection was conducted from October 2021 to February 2022, during which 121 surface water samples were obtained and their spectral signatures recorded using an SVC HR-1024i Spectroradiometer. Simultaneously, Sentinel-2 MSI Level 2A images were processed to extract spectral reflectance at corresponding sampling points. Strong correlations were observed between SSC and reflectance in the Green, Red, and Red Edge 1 bands. Multiple spectral indices and band ratios were evaluated to identify optimal SSC estimators, with the combination (Green × Red Edge 1)/Red demonstrating the highest predictive capability. A spectral integration function was developed using a two-stage regression approach: first, linking observed SSC to Spectroradiometer-derived indices; second, connecting these indices to Sentinel-2 reflectance data. The resulting models were validated using linear regression, Student’s t-test, residual analysis, and k-fold cross-validation. Among all models, the (Green × Red Edge 1)/Red function achieved superior performance with an R2 of 0.80, RMSE of 8.58 mg/L, and MAPE of 19.41%. The approach was further tested for temporal SSC mapping using past Sentinel-2 imagery, revealing seasonal sediment trends. The study concludes that the proposed spectral integration method provides a robust, scalable, and transferable framework for accurate SSC monitoring in large water bodies. This advancement holds significant implications for sediment management, water quality assessment, and hydrological research.
ISSN:20452322
DOI:10.1038/s41598-025-15719-w