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- MERIS phytoplankton time series products from the SW Iberian Peninsula (Sagres) using seasonal-trend decomposition based on loessPublication . Cristina, Sónia; Cordeiro, Clara; Lavender, Samantha; Goela, Priscila; Icely, John; Newton, AliceThe European Space Agency has acquired 10 years of data on the temporal and spatial distribution of phytoplankton biomass from the MEdium Resolution Imaging Spectrometer (MERIS) sensor for ocean color. The phytoplankton biomass was estimated with the MERIS product Algal Pigment Index 1 (API 1). Seasonal-Trend decomposition of time series based on Loess (STL) identified the temporal variability of the dynamical features in the MERIS products for water leaving reflectance ((w)()) and API 1. The advantages of STL is that it can identify seasonal components changing over time, it is responsive to nonlinear trends, and it is robust in the presence of outliers. One of the novelties in this study is the development and the implementation of an automatic procedure, stl.fit(), that searches the best data modeling by varying the values of the smoothing parameters, and by selecting the model with the lowest error measure. This procedure was applied to 10 years of monthly time series from Sagres in the Southwestern Iberian Peninsula at three Stations, 2, 10 and 18 km from the shore. Decomposing the MERIS products into seasonal, trend and irregular components with stl.fit(), the (w)() indicated dominance of the seasonal and irregular components while API 1 was mainly dominated by the seasonal component, with an increasing effect from inshore to offshore. A comparison of the seasonal components between the (w)() and the API 1 product, showed that the variations decrease along this time period due to the changes in phytoplankton functional types. Furthermore, inter-annual seasonal variation for API 1 showed the influence of upwelling events and in which month of the year these occur at each of the three Sagres stations. The stl.fit() is a good tool for any remote sensing study of time series, particularly those addressing inter-annual variations. This procedure will be made available in R software.
- Using remote sensing as a support to the implementation of the European Marine Strategy Framework Directive in SW PortugalPublication . Cristina, Sónia; Icely, John; Goela, Priscila; Angel DelValls, Tomás; Newton, AliceThe exclusive economic zones (EEZ) of coastal countries are coming under increasing pressure from various economic sectors such as fishing, aquaculture, shipping and energy production. In Europe, there is a policy to expand the maritime economic sector without damaging the environment by ensuring that these activities comply with legally binding Directives, such as the Marine Strategy Framework Directive (MSFD). However, monitoring an extensive maritime area is a logistical and economic challenge. Remote sensing is considered one of the most cost effective, methods for providing the spatial and temporal environmental data that will be necessary for the effective implementation of the MSFD. However, there is still a concern about the uncertainties associated with remote sensed products. This study has tested how a specific satellite product can contribute to the monitoring of a MSFD Descriptor for "good environmental status" (GES). The results show that the quality of the remote sensing product Algal Pigment Index 1 (API 1) from the MEdium Resolution Imaging Spectrometer (MERIS) sensor of the European Space Agency for ocean colour products can be effectively validated with in situ data from three stations off the SW Iberian Peninsula. The validation results show good agreement between the MERIS API 1 and the in situ data for the two more offshore stations, with a higher coefficient of determination (R-2) of 0.79, and with lower uncertainties for the average relative percentage difference (RPD) of 24.6% and 27.9% and a root mean square error (RMSE) of 0.40 and 0.38 for Stations B and C, respectively. Near to the coast, Station A has the lowest R-2 of 0.63 and the highest uncertainties with an RPD of 112.9% and a RMSE of 1.00. It is also the station most affected by adjacency effects from the land: when the Improved Contrast between Ocean and Land processor (ICOL) is applied the R-2 increases to 0.77 and there is a 30% reduction in the uncertainties estimated by RPD. The MERIS API 1 product decreases from inshore to offshore, with higher values occurring mainly between early spring and the end of the summer, and with lower values during winter. By using the satellite images for API 1, it is possible to detect and track the development of algal blooms in coastal and marine waters, demonstrating the usefulness of remote sensing for supporting the implementation of the MSFD with respect to Descriptor 5: Eutrophication. It is probable that remote sensing will also prove to be useful for monitoring other Descriptors of the MSFD.