Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due to their shallow depth, tidal variability, and the presence of strong currents and breaking waves. High resolution satellite sensors can be used to provide synoptic views of Surface Temperature (ST), but the performance of such ST products in the nearshore zone is poorly understood. Close to the shoreline, the ST pixels can be influenced by mixed composition of water and land, as a result of the sensor’s spatial resolution. This can cause thermal adjacency effects due to the highly different diurnal temperature cycles of water bodies and land. Previously, temperature data collected during surfing sessions has been proposed for validation of moderate resolution (1 km pixel size) satellite ST products. In this paper we use surfing temperature data to validate three high resolution (100 m resampled to 30 m pixel size) ST products derived from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 (L8). ST was derived from Collection 1 and 2 Level 1 data (C1L1 and C2L1) using the Thermal Atmospheric Correction Tool (TACT), and was obtained from the standard Collection 2 Level 2 product (USGS C2L2). This study represents one of the first evaluations of the new C2 products, both L1 and L2, released by USGS at the end of 2020. Using automated matchup and image quality control, 88 matchups between L8/TIRS and surfers were identified, distributed across the North-Western semihemisphere. The unbiased Root Mean Squared Difference (uRMSD) between satellite and in situ measurements was generally ¡ 2 K, with warm biases (Mean Average Difference, MAD) of 1.7 K (USGS C2L2), 1.3 K (TACT C1L1) and 0.8 K (TACT C2L1). Large interquartile ranges of ST in 5 × 5 satellite pixels around the matchup location were found for several images, especially for the summer matchups around the Californian coast. By filtering on target stability the number of matchups reduced to 31, which halved the uRMSD across the three methods (to around 1.1K), MAD were much lower, i.e. 1.1 K (USGS C2L2), 0.6 K (TACT C1L1), and 0.2 K (TACT C2L1). The larger biases of the C2L2 product compared to TACT C2L1 are caused as a result of: (1) a lower emissivity value for water targets used in USGS C2L2, and (2) differences in atmospheric parameter retrieval, mainly from differences in upwelling atmospheric radiance and lower atmospheric transmittance retrieved by USGS C2L2. Additionally, tiling artefacts are present in the C2L2 product, which originate from a coarser atmospheric correction process. Overall, the L8/TIRS derived ST product compares well with in situ measurements made while surfing, and we found the best performing ST product for nearshore coastal waters to be the Collection 2 Level 1 data processed with TACT.
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RBINS Staff Publications 2022
This paper presents results obtained with MIRO&CO-3D, a biogeochemical model dedicated to the study of eutrophication and applied to the Channel and Southern Bight of the North Sea (48.5°N–52.5°N). The model results from coupling of the COHERENS-3D hydrodynamic model and the biogeochemical model MIRO, which was previously calibrated in a multi-box implementation. MIRO&CO-3D is run to simulate the annual cycle of inorganic and organic carbon and nutrients (nitrogen, phosphorus and silica), phytoplankton (diatoms, nanoflagellates and Phaeocystis), bacteria and zooplankton (microzooplankton and copepods) with realistic forcing (meteorological conditions and river loads) for the period 1991–2003. Model validation is first shown by comparing time series of model concentrations of nutrients, chlorophyll a, diatom and Phaeocystis with in situ data from station 330 (51°26.00′N, 2°48.50′E) located in the centre of the Belgian coastal zone. This comparison shows the model's ability to represent the seasonal dynamics of nutrients and phytoplankton in Belgian waters. However the model fails to simulate correctly the dissolved silica cycle, especially during the beginning of spring, due to the late onset (in the model) of the early spring diatom bloom. As a general trend the chlorophyll a spring maximum is underestimated in simulations. A comparison between the seasonal average of surface winter nutrients and spring chlorophyll a concentrations simulated with in situ data for different stations is used to assess the accuracy of the simulated spatial distribution. At a seasonal scale, the spatial distribution of surface winter nutrients is in general well reproduced by the model with nevertheless a small overestimation for a few stations close to the Rhine/Meuse mouth and a tendency to underestimation in the coastal zone from Belgium to France. PO4 was simulated best; silica was simulated with less success. Spring chlorophyll a concentration is in general underestimated by the model. The accuracy of the simulated phytoplankton spatial distribution is further evaluated by comparing simulated surface chlorophyll a with that derived from the satellite sensor MERIS for the year 2003. Reasonable agreement is found between simulated and satellite-derived regions of high chlorophyll a with nevertheless discrepancies close to the boundaries.
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