Preprint / Version 1

Temporal Synchronization Determines the Accuracy of Sentinel-2 Turbidity Retrieval

Evidence from Community-Based Monitoring of the Buriganga River, Dhaka.

##article.authors##

  • Adneen Khan Mr

DOI:

https://doi.org/10.58445/rars.4253

Keywords:

Sentinel-2, Normalized Difference Turbidity Index, turbidity, temporal synchronization, Buriganga River, community-based monitoring, Google Earth Engine

Abstract

Community-based environmental organizations require continuous water quality data but are constrained by the cost and labor of physical sampling. Satellite remote sensing offers a free alternative, yet validation studies of inland waters frequently report weak agreement between satellite indices and ground measurements, and the cause of that disagreement is rarely isolated. This study analyzed 28 Sentinel-2 observations across 12 dates at three sites on the Buriganga River in Dhaka, Bangladesh, between January and April 2026, alongside eight calibrated field measurements of turbidity, pH, and dissolved oxygen. Three findings emerged. First, the reach adjacent to the Hazaribagh tannery district was more turbid than the Katashur Outfall on every one of 11 valid observation dates (Wilcoxon p = .001), establishing a persistent spatial gradient that discrete sampling campaigns had not resolved. Second, turbidity rose by 88% river-wide between January and February (p = .006), and the cleanest site deteriorated fastest, compressing the gradient between the tannery reach and the outfall from 2.0 to 1.5. Third, repeat same-day observations established a measurement noise floor 2.6 times smaller than between-date variation, indicating that 86% of observed variance reflects genuine day-to-day change. That last result predicts, and the data confirm, that pairing protocol governs retrieval accuracy: comparing generalized field averages against satellite observations from the same approximate period yielded no significant relationship (R² = .04, p = .46), whereas matching each field measurement to a cloud-free overpass on the same calendar date yielded strong agreement (R² = .94, 95% CI [.71, .99], n = 8, p < .001), with a cross-validated retrieval error of 5.4 NTU. Sensor capability was identical under both protocols. Weak validation outcomes in dynamic inland waters may therefore reflect protocol failure rather than sensor limitation.

References

Department of Environment (DoE). (1997). Environment Conservation Rules (ECR). Ministry of Environment and Forest, Government of the People's Republic of Bangladesh.

Fatema, S., et al. (2018). Water quality assessment of the river Buriganga, Bangladesh. Journal of Biodiversity and Environmental Sciences, 13(1), 22-31.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.

Kamal, M. M., Malmqvist, P. A., & Werellagama, I. (1999). Water quality modeling for the Buriganga River, Bangladesh. Water Science and Technology, 40(2), 27-34.

Kutser, T., et al. (2020). Remote sensing of shallow waters – A 50 year retrospective and future directions. Remote Sensing of Environment, 240, 111619.

Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., & Lafaye, M. (2007). Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74.

Majed, N., et al. (2022). Heavy metal pollution in the Buriganga River. Environmental Monitoring and Assessment, 194(3), 154.

Markert, K. N., et al. (2018). Historical and operational monitoring of surface water dynamics. Remote Sensing, 10(8).

NASA Applied Remote Sensing Training Program (ARSET). (2024). Water quality monitoring using Google Earth Engine. NASA Earthdata.

Downloads

Posted

2026-10-11