Abstract
The 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 (ρw(λ)) 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 ρw(λ) 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 ρw(λ) 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.
Highlights
Satellite ocean color remote sensing provides a valuable source of information on the status of marine ecosystems
The ideal method should allow for variations in the seasonal pattern and should be robust in the presence of outlying observations. This current study has selected the Seasonal-Trend decomposition of time series based on Loess (STL, local polynomial regression fitting) [11]
The analysis of the ρw was made for all the wavelengths, this paper focuses on 443, 490, 510 and 560 nm, since these are the wavebands used to retrieve TChla and thereby provide the data for the MEdium Resolution Imaging Spectrometer (MERIS) Algal Pigment Index 1 (API 1) algorithm [36,37]
Summary
Satellite ocean color remote sensing provides a valuable source of information on the status of marine ecosystems. The ideal method should allow for variations in the seasonal pattern and should be robust in the presence of outlying observations This current study has selected the Seasonal-Trend decomposition of time series based on Loess (STL, local polynomial regression fitting) [11]. The stl.fit() includes the advantages of the STL and allows an automatic selection of the smoothing parameters based on minimizing the error measure This approach provides benefits for the studies of time series that need to evaluate the inter-annual variability. How can stl.fit() be used to describe and explain the variability of the study area?
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