Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province, Argentina

Abstract

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R 2 ) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s −1 , R 2 is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the 'skin and sub-skin layer' when wind speed is below 6 m s −1 . The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002-December 2010. The results show that interannual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains. waters, which becomes stronger in spring (Romero et al. 2006). The highly diverse

Seasonal and Inter-Annual Analysis of Chlorophyll-a and Inherent Optical Properties from Satellite Observations in the Inner and Mid-Shelves of the South of Buenos Aires Province (Argentina)

“…SST is thought to be the main driver of the phytoplankton seasonal behavior. In this region maximum estimates of aph(443) and chl-a were found in winter or late autumn and minimum in summer, in agreement with the dominant seasonal factor of the temperature variability (SST = 8 °C , 10 °C , 22 °C , respectively [69]). There could be an error in the satellite estimation of suspended sediment, since maximum values of bbp(443) were observed in winter and autumn.…”

“…In this frame, it is important to mention that chl-a estimates in the coastal zone could be influenced by the high scattering of particulate material. This area is characterized by a shallow slope of the bottom, reaching 15 m depth at 80 km from the coast line [2], which is responsible for the SST high seasonal amplitude, ranging from 23 °C (summer) to 7 °C (winter), with a maximum year amplitude of 15 °C [69]. The coastal zone is highly influenced by the discharge of the rivers, mainly the Negro and Colorado and the plume of the Bahí a Blanca Estuary.…”

Hábitat y historia de vida de Pachychelles laevidactylus (Crustacea, Anomura, Porcellanidae) en el intermareal rocoso de Mar del Plata, Argentina

“…Posteriormente fueron procesadas con rutinas informáticas especificas y se obtuvo el valor de TSM promedio para cada mes y correspondiente desvío estándar. Las imágenes satelitales MODIS de TSM fueron validadas para el sur de la provincia de Buenos Aires (Argentina) y han demostrado un buen desempeño (Delgado et al, 2014).…”

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

“…Lathlean et al, 2011), or an area averaged value whereby the mean SST measurements is calculated over a set number of pixels, including pixels offshore (Smit et al, 2013;Delgado et al, 2014;Stobart et al, 2016). Stobart et al (2016) assessed the daily temperature differences between satellite-derived and in situ measurements and averaged all the satellite-derived SST measurements within a 20 km 2 box centered on the in situ logger locations.…”

“…Stobart et al (2016) assessed the daily temperature differences between satellite-derived and in situ measurements and averaged all the satellite-derived SST measurements within a 20 km 2 box centered on the in situ logger locations. While Smit et al (2013) used an average over the adjacent nine SST data points and Delgado et al (2014) used 3 × 2 and 3 × 3 satellite pixels to compare with coastal and inner-shelf in situ measurements, respectively. For the latter study this area averaging, as well as incorporating time-scales over which the satellites passed the area, produced high correlations (R 2 of 0.99) between the two datasets.…”