Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

Abstract

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained in three Galician rias (NW Spain) and the adjacent shelf, an area of extensive mussel cultures that experiences frequent harmful algal events. Regionally focused algorithms (NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data and the one that showed the best performance was applied in a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of a strong upwelling favourable winds a high biomass algal event was recorded in an area of low SST. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass "patches" were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the spatial structure of the phytoplankton distribution in the study area can be complex. Surface currents and winds off the rias Baixas affected the distribution of chla in the rias Baixas. This study showed that a regionally specific algorithm for an ocean colour sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla

Chlorophyll-a Concentration Retrieval in the Optically Complex Waters of the St. Lawrence Estuary and Gulf Using Principal Component Analysis

“…Their results showed that an algorithm based on two band-ratios (Rrs443/Rrs510 and Rrs443/Rrs555) yielded an estimation error of 70% using in situ radiometric measurements and 95% when using SeaWiFS-derived Rrs. Other studies in optically complex coastal waters showed that it is often necessary to avoid band-ratio-based algorithms to better reflect the specificity of the local/regional biophysical characteristics [66][67][68][69][70]. As part of the CoastColor validation program, the accuracy of a neural network approach was applied to MERIS to estimate Chl.…”

Optical types of inland and coastal waters

“…More recent studies have moved toward the differentiation of water types in optically complex environments using in situ and/or satellite‐derived reflectance data. Most of these studies have considered the range of optical classes in marine systems (English Channel and North Sea: Lubac and Loisel ; Tilstone et al ; Vantrepotte et al , Iberian coastal waters: Spyrakos et al ; Adriatic Sea: Mélin et al , Yellow Sea: Ye et al ; Northwest Atlantic shelf: Moore et al , global ocean: Moore et al , global coastal waters: Mélin and Vantrepotte ) with only a few studies focussed on inland systems (lakes and reservoirs in China: Le et al ; Shen et al ; Estonian and Finnish lakes: Reinart et al ). Overall, these classification schemes can substantially improve the remote sensing products associated with individual optical water types (OWTs), and have demonstrated the need for a better understanding of the underlying variability especially in nearshore and inland waterbodies (Moore et al ).…”

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

“…Machine-learning algorithms based on simulated datasets have been widely used to address this issue; however, they still fail to provide accurate results in regional and atypical situations (e.g. Spyrakos et al, 2011;Palmer et al, 2015). Moreover, careful selection of the initial inputs and parameterisation of these models are necessary to avoid imprecision in the prediction of the output and over-fitting.…”