Shahab A. Shojaeezadeh Hydrology · GIS · Data Science
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2024Science of the Total Environment1 citation

Assessing road construction effects on turbidity in adjacent water bodies using Sentinel-1 and Sentinel-2

MG Mooselu, MR Nikoo, H Liltved, MS Bjørkenes, A Elnashar, et al.

Read at Science of the Total Environment All publications

Research Context

Road construction degrades nearby water quality, but monitoring it usually depends on sparse gauge data. Satellites could watch the whole corridor continuously — if the atmospheric correction can be trusted.

Method

Sentinel-1 radar delineates water extents; Sentinel-2 top-of-atmosphere imagery, corrected with the MAIN algorithm, estimates turbidity. The correction was validated globally against GLORIA lake reflectance observations, and turbidity was retrieved with the Novoa and Nechad algorithms. The framework was applied to the E18 Arendal–Tvedestrand highway in southeastern Norway, 2017 to 2021.

Key Insights

  • MAIN recovers water-leaving reflectance well (Pearson r > 0.7 between 490 and 705 nm) and turbidity acceptably (r > 0.6), with Nechad the more effective retrieval algorithm.
  • Using MAIN-corrected top-of-atmosphere data instead of bottom-of-atmosphere data yields a more complete time series.
  • Road construction demonstrably increases turbidity in adjacent water bodies.

Abstract

Road construction significantly affects water resources by introducing contaminants, fragmenting habitats, and degrading water quality. This study examines the use of Remote Sensing (RS) data of Sentinel-1 (S1) and Sentinel-2 (S2) in Google Earth Engine (GEE) to do spatio-temporal analysis of turbidity in adjacent water bodies during the construction and operation of the E18 Arendal-Tvedestrand highway in southeastern Norway from 2017 to 2021. S1 radiometric data helped delineate water extents, while S2-Top of Atmosphere (TOA) multispectral data, corrected using the Modified Atmospheric correction for INland waters (MAIN), used to estimate turbidity levels. To ensure a comprehensive time series of RS data, we utilized S2-TOA data corrected with the MAIN algorithm rather than S2-Bottom Of Atmosphere (BOA) data. We validated the MAIN algorithm's accuracy against GLORIA (Global Observatory of Lake Responses to Interventions and Drivers) observations of surface water reflectance in lakes, globally. Subsequently, the corrected S2 data is used to calculate turbidity using the Novoa and Nechad retrieval algorithms and compared with GLORIA turbidity observations. Findings indicate that the MAIN algorithm adequately estimates water-leaving surface reflectance (Pearson correlation > 0.7 for wavelengths between 490 and 705 nm) and turbidity (Pearson correlation > 0.6 for both algorithms), determining Nechad as the more effective algorithm. In this regard, we used S2 corrected images with MAIN to estimate turbidity in the study area and evaluated with local gauge data and observational reports. Results indicate that the proposed framework effectively captures trends and patterns of turbidity variation in the study area. Findings verify that road construction can increase turbidity in adjacent water bodies and emphasis the employing RS data in cloud platforms like GEE can provide insights for effective long-term water quality management strategies during construction and operation phases.