45 Hydrogeology (Posters)

Added due to amount of discipline abstracts submitted.

Wednesday, 11 March 2026
9:00 AM - 12:00 PM
RCC, Lower Level Hall

Enhancing Coastal Water Quality Mapping: Downscaling MODIS Ocean Color Using Machine Learning and Landsat Data

Paper number: 45-11
Program: Technical Sessions
Time: 9:00 AM – 9:00 AM

Authors

Abstract

Satellite-based ocean color observations play a vital role in the assessment of coastal water quality; however, their utility is often limited by inherent trade-offs between spatial and temporal resolution. The MODIS Aqua sensor offers daily measurements of remote sensing reflectance (Rrs), which are fundamental for estimating chlorophyll-a concentrations, yet its moderate spatial resolution of approximately 1-km constrains its capacity to capture fine-scale variability within coastal regions. In contrast, the Landsat 8 and 9 Operational Land Imager (OLI1 and OLI2) enable observations at a finer spatial resolution of 30 m, albeit with reduced temporal frequency.

This study introduces a machine learning framework designed to downscale MODIS Aqua Rrs at 469 nm and 555 nm to a 30 m resolution, specifically on Landsat overpass days. This approach employs atmospheric correction of Landsat top-of-atmosphere radiance in the blue and green spectral bands, enabling accurate derivation of water-leaving remote sensing reflectance according to established ocean color theory. Subsequently, Landsat-derived Rrs data are geometrically aligned with MODIS measurements and are spectrally and radiometrically harmonized through spatial aggregation at the 1-km scale.

Machine learning models are trained on pooled Landsat-MODIS match-up datasets to characterize the relationship between aggregated Landsat Rrs features and MODIS Rrs values. These models are then applied at the native Landsat resolution to generate MODIS-consistent Rrs fields at 30 m. The proposed downscaling method aims to improve the spatial resolution of MODIS Rrs and facilitate the development of advanced, higher resolution chlorophyll-a estimation algorithms for the Mississippi Sound.

Geological Society of America Abstracts with Programs. Vol. 58, No. 1

doi: 10.1130/abs/2026SE-411880

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