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Understanding and monitoring nearshore environments is essential, given that these fine-scaled ecosystems are integral to human well-being. While satellites offer an opportunity to gain synchronous and spatially extensive data of coastal areas, off-the-shelf calibrated satellite sea surface temperature (SST) measurements have only been available at coarse resolutions of 1 km or larger. In this study, we develop a novel methodology to create a simple linear equation to calibrate fine-scale Landsat thermal infrared radiation brightness temperatures (calibrated for land sensing) to derive SST at a resolution of 100 m. The constants of this equation are derived from correlations of coincident MODIS SST and Landsat data, which we filter to find optimal pairs. Validation against in situ sensor data at varying distances from the shore in Northern California shows that our SST estimates are more accurate than prior off-the-shelf Landsat data calibrated for land surfaces. These fine-scale SST estimates also demonstrate superior accuracy compared with coincident MODIS SST estimates. The root mean square error for our minimally filtered dataset (n = 557 images) ranges from 0.76 to 1.20 °C with correlation coefficients from r = 0.73 to 0.92, and for our optimal dataset (n = 229 images), the error is from 0.62 to 0.98 °C with correlations from r = 0.83 to 0.92. Potential error sources related to stratification and seasonality are examined and we conclude that Landsat data represent skin temperatures with an error between 0.62 and 0.73 °C. We discuss the utility of our methodology for enhancing coastal monitoring efforts and capturing previously unseen spatial complexity. Testing the calibration methodology on Landsat images before and after the temporal bounds of accurate MODIS SST measurements shows successful calibration with lower errors than the off-the-shelf, land-calibrated Landsat product, extending the applicability of our approach. This new approach for obtaining high-resolution SST data in nearshore waters may be applied to other upwelling regions globally, contributing to improved coastal monitoring, management, and research.
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Effect of Satellite Spatial Resolution on Submesoscale Variance in Ocean Color and Temperature
Abstract The capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions.
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- PAR ID:
- 10636473
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 6
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2024GL114266
