Decadal analysis of sea surface temperature patterns, climatology, and anomalies in temperate coastal waters with Landsat-8 TIRS observations

TL;DR

Coastal SST monitoring requires high spatial resolution to resolve land–sea interactions; the authors develop an operational 100 m SST retrieval from Landsat-8 TIRS, build a daily 100 m baseline climatology for 2014–2023, and map SST anomalies across temperate South Australia. Their end-to-end workflow uses a radiative-transfer model with spatiotemporally explicit atmospheric inputs from CAMS EAC4 and three libRadtran simulations, with a parallel tile-based processing framework. Validation shows strong agreement with in-situ data ($r^2=0.97$, RMSE ≈ $0.38^\circ$C) and reveals pronounced seasonal variability in semi-enclosed bays; anomaly probabilities exceed 40% in near-shore regions like Upper Spencer Gulf and Kangaroo Island, especially during warm months. The high-resolution climatology advances coastal ecosystem management and fisheries planning, and the study outlines pathways for multi-sensor data fusion and real-time anomaly detection in temperate coastal environments.

Abstract

Sea surface temperature (SST) is a fundamental physical parameter characterising the thermal state of sea surface. Due to the intricate thermal interactions between land, sea, and atmosphere, the spatial gradients of SST in coastal waters often appear at finer spatial scales than those in open ocean waters. The Thermal Infrared Sensor (TIRS) onboard Landsat-8, with its 100-meter spatial resolution, offers a unique opportunity to uncover fine-scale coastal SST patterns that would otherwise be overlooked by coarser-resolution thermal sensors. In this study, we first analysed the spatiotemporal patterns of SST in South Australia's temperate coastal waters from 2014 to 2023 by developing an operational approach for SST retrieval from the Landsat-8 TIRS sensor. A buoy was deployed off the coast of Port Lincoln, South Australia, to validate the quality of SST retrievals. Then the daily baseline climatology of SST with 100 m resolution was constructed, which allowed for the detection and analysis of anomalous SST events. Our results suggest the following: (1) the satellite-derived SST data aligned well with the in-situ measured SST values; (2) the semi-enclosed, shallow regions of Upper Spencer Gulf and Upper St Vincent Gulf showed higher temperatures during summer and cooler temperatures during winter than waters closer to the open ocean, resulting in a higher seasonal variation in SST; (3) the near-shore shallow areas in Spencer Gulf and St Vincent Gulf, and regions surrounding Kangaroo Island, were identified to have a higher probability of SST anomalies compared to the rest of the study area; and (4) anomalous SST events were more likely to happen during the warm months than the cool months. We hope these findings would be helpful in supporting the fishing and aquaculture industries in the coastal waters of South Australia.

Figures (16)

• Figure 1: The study area of this work encompasses the Australian territorial waters off the coast of South Australia, including Spencer Gulf, St Vincent Gulf, and their adjacent waters.
• Figure 2: (a) The number of total satellite observations in the study area over 2014--2023, and (b) the chance of cloudy observations over the same time period as derived from the Landsat Collection 2 Pixel Quality Assessment Band.
• Figure 3: Example vertical profiles of (a) air pressure, (b) air temperature, (c) specific humidity, (d) ozone content, (e) nitrogen dioxide content, (f) nitrogen content, (g) oxygen content, (h) carbon dioxide content, (i) partial pressure of dry air, (j) partial pressure of water vapour, (k) air density, and (l) water vapour content from the sea surface (0 km altitude) to the top of atmosphere (60 km altitude).
• Figure 4: A buoy has been set up near Port Lincoln in this study to collect in-situ sea surface temperature (SST) for validating the satellite-derived SST data. (a) Location of the buoy; (b) Zoomed-in view of the buoy location; and (c) A photo of the buoy before deployment.
• Figure 5: Simplified radiative transfer process from the radiance of sea water to at-sensor radiance, with interference from atmospheric scattering, absorption, and emission. $L_s(\lambda)$ is the black-body radiance of sea water; $L_t(\lambda)$ is the at-sensor radiance; $L_u(\lambda)$ and $L_d(\lambda)$ are atmospheric upwelling and downwelling radiances, respectively; $\tau(\lambda)$ is the transmittance of atmosphere; $\varepsilon(\lambda)$ is the emissivity of sea water; and $\lambda$ denotes spectral dependency.