MERIS Phytoplankton Time Series Products from the SW Iberian Peninsula (Sagres) Using Seasonal-Trend Decomposition Based on Loess

Abstract

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 (ρ w (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 ρ w (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 ρ w (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.

Chloride Balance in Freshwater System of a Highly Anthropized Subalpine Area: Load and Source Quantification Through a Watershed Approach

“…The STL is an iterative nonparametric procedure that repeatedly uses a locally weighted regression (LOESS) smoother to refine and improve estimates of the S t and T t components. At the end of the STL process, the seasonal and trend components are extracted from the data series (Cristina et al, ). The analysis was performed on the time series of the overall discharges in Lake Iseo and on the time series of the lake chloride concentration recorded in the whole water column (0–250 m) and in the superficial layers (0–10 m).…”

Technical Note: Algal Pigment Index 2 in the Atlantic off the Southwest Iberian Peninsula: standard and regional algorithms

“…The selection of satellite images was restricted to images without clouds and contamination, as indicated by not having specific product confidence (PCD), sun glint, and ice flags. More details on the image selection criteria and full description of flags are reported in Cristina et al (2016a). TChl a concentration (monovinyl Chl a + divinyl Chl a + chlorophyllide a + phaeopigments) was determined by high-performance liquid chromatography (HPLC), according to Wright and Jeffrey (1997), herein referred to as TChl a REF HPLC .…”

“…The MEdium Resolution Image Spectrometer (MERIS) space sensor, operated by the European Space Agency (ESA) on-board the ENVISAT platform from 2002 to 2012, has been continuously supported by investigations for the assessment and improvement of data products. Commissioned studies include the validation of radiometric data such as the R rs (Cristina et al, 2014;Kajiyama et al, 2014), as well as the analyses of derived product maps D'Alimonte et al, 2014;Cristina et al, 2016b). These MERIS validation activities have established an important basis to address Earth observation (EO) capabilities through the Ocean Land Colour Instrument (OLCI) sensor launched on the Sentinel-3 satellite in February 2016.…”

Biodiversity in Marine Ecosystems—European Developments toward Robust Assessments

“…The joint use of remote sensing biomass observations and ship-of-opportunity fluorescence measurements are a powerful combination to detect changes both in the phytoplankton biomass and composition. Remote sensing, bio-optics, microscopy, and CHEMTAX results provide a combination of analytical tools that can be used to develop a phytoplankton biomass (chlorophyll a) index for marine and coastal waters off the Iberian peninsula, in Portugal (Gohin, 2011b;Cristina et al, 2015Cristina et al, , 2016aGoela et al, 2015).…”