MERIS-based ocean colour classification with the discrete Forel-Ule scale

Multispectral information from satellite borne ocean colour sensors is at present used to characterize natural waters via the retrieval of concentrations of the three dominant optical constituents; pigments of phytoplankton, non-algal particles and coloured dissolved organic matter. A limitation of this approach is that accurate retrieval of these constituents requires detailed local knowledge of the specific absorption and scattering properties. In addition, the retrieval algorithms generally use only a limited part of the collected spectral information. In this paper we present an additional new algorithm that has the merit of using the full spectral information in the visible domain to characterize natural waters in a simple and globally valid way. This Forel–Ule MERIS (FUME) algorithm converts the normalized multiband reflectance information into a discrete set of numbers using uniform colourimetric functions. The Forel–Ule (<i>FU</i>) scale is a sea colour comparator scale that has been developed to cover all possible natural sea colours, ranging from indigo blue (the open ocean) to brownish-green (coastal water) and even brown (humic-acid dominated) waters. Data using this scale have been collected since the late nineteenth century, and therefore, this algorithm creates the possibility to compare historic ocean colour data with present-day satellite ocean colour observations. The FUME algorithm was tested by transforming a number of MERIS satellite images into Forel–Ule colour index images and comparing in situ observed <i>FU</i> numbers with <i>FU</i> numbers modelled from in situ radiometer measurements. Similar patterns and <i>FU</i> numbers were observed when comparing MERIS ocean colour distribution maps with ground truth Forel–Ule observations. The <i>FU</i> numbers modelled from in situ radiometer measurements showed a good correlation with observed <i>FU</i> numbers (<i>R</i>² = 0.81 when full spectra are used and <i>R</i>² = 0.71 when MERIS bands are used)

Microstructure observations during the spring 2011 STRATIPHYT-II cruise in the Northeast Atlantic

Small-scale temperature and conductivity variations have been measured in the upper 100 m of the northeast Atlantic during the STRATIPHYT-II cruise (Las Palmas–Reykjavik, 6 April–3 May 2011). The measurements were done at midday and comprised 2 to 15 vertical profiles at each station. The derived turbulent quantities show a transition between weakly-stratified (mixed layer depth, MLD, &lt;100) and well-mixed waters (MLD &gt; 100), which was centered at about 48° N. The temperature eddy diffusivities, &lt;I&gt;K&lt;sub&gt;T&lt;/sub&gt;&lt;/I&gt;, range from 10&lt;sup&gt;&amp;minus;5&lt;/sup&gt; to 10&lt;sup&gt;0&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt; s&lt;sup&gt;−1&lt;/sup&gt; in the weakly-stratified stations, and range from 3 &amp;times; 10&lt;sup&gt;&amp;minus;4&lt;/sup&gt; to 2 &amp;times; 10&lt;sup&gt;0&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt; s&lt;sup&gt;−1&lt;/sup&gt; in the well-mixed stations. The turbulent kinetic energy dissipation rates, ε, range from 3 &amp;times; 10&lt;sup&gt;&amp;minus;8&lt;/sup&gt; to 2 &amp;times; 10&lt;sup&gt;&amp;minus;6&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt; s&lt;sup&gt;−3&lt;/sup&gt; south of the transition zone, and from 10&lt;sup&gt;&amp;minus;7&lt;/sup&gt; to 10&lt;sup&gt;&amp;minus;5&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt; s&lt;sup&gt;−3&lt;/sup&gt; north of the transition zone. The station-averaged &lt;I&gt;K&lt;sub&gt;T&lt;/sub&gt;&lt;/I&gt; values throughout the mixed layer increase exponentially with the wind speed. The station-averaged ε values throughout the mixed layer scale with the wind stress similarity variable with a scaling factor of about 1.8 in the wind-dominated stations (&amp;epsilon; &amp;approx; 1.8 &lt;I&gt;u&lt;/I&gt;&lt;sub&gt;&amp;star;&lt;/sub&gt;&lt;sup&gt;3&lt;/sup&gt;/(&amp;minus;&amp;kappa;&lt;I&gt;z&lt;/I&gt;)). The values of &lt;I&gt;K&lt;sub&gt;T&lt;/sub&gt;&lt;/I&gt; and ε are on average 10 times higher compared to the values measured at the same stations in July 2009. The results presented here constitute a unique data set giving large spatial coverage of upper ocean spring turbulence quantities

Disparities between in situ and optically derived carbon biomassand growth rates of the prymnesiophyte <i>Phaeocystis globosa</i>

The oceans play a pivotal role in the global carbon cycle. It is not practical to measure the global daily production of organic carbon, the product of phytoplankton standing stock and its growth rate using discrete oceanographic methods. Instead, optical proxies from Earth-orbiting satellites must be used. To test the accuracy of optically derived proxies of phytoplankton physiology and growth rate, hyperspectral reflectance data from the wax and wane of a Phaeocystis bloom in laboratory mesocosms were compared with standard ex situ data. Chlorophyll biomass could be estimated accurately from reflectance using specific chlorophyll absorption algorithms. However, the conversion of chlorophyll (Chl) to carbon (C) was obscured by the non-linear increase in C : Chl under nutrient-limited growth. Although C : Chl was inversely correlated (r2 = 0.88) with the in situ fluorometric growth rate indicator Fv / Fm (Photosystem II quantum efficiency), none of them was linearly correlated to growth rate, constraining the accurate calculation of Phaeocystis growth or production rates. Unfortunately, the optical proxy ?ph (quantum efficiency of fluorescence: the ratio of the number of fluoresced photons to the number of photons absorbed by the phytoplankton) did not show any correlation with Phaeocystis growth rate, and therefore it is concluded that ?ph cannot be applied in the remotely sensed measurement of this species' carbon production rate

Microstructure observations during the spring 2011 STRATIPHYT-II cruise in the northeast Atlantic

Small-scale temperature and conductivity variations have been measured in the upper 100 m of the northeast Atlantic during the STRATIPHYT-II cruise (Las Palmas-Reykjavik, 6 April-3 May 2011). The measurements were done at midday and comprised 2 to 15 vertical profiles at each station. The derived turbulent quantities show a transition between weakly-stratified (mixed layer depth, MLD, 100), which was centered at about 48°N. The temperature eddy diffusivities, K T, range from 10 -5 to 100 m 2 s -1 in the weakly-stratified stations, and range from 3 × 10 -4 to 2 × 100 m 2 s -1 in the well-mixed stations. The turbulent kinetic energy dissipation rates, ο, range from 3 × 10 -8 to 2 × 10 -6 m 2 s -3 south of the transition zone, and from 10 -7 to 10 -5 m 2 s -3 north of the transition zone. The station-averaged K T values throughout the mixed layer increase exponentially with the wind speed. The station-averaged ο values throughout the mixed layer scale with the wind stress similarity variable with a scaling factor of about 1.8 in the wind-dominated stations (epsilon approx; 1.8 ≈ 3(-κz)). The values of K T and ο are on average 10 times higher compared to the values measured at the same stations in July 2009. The results presented here constitute a unique data set giving large spatial coverage of upper ocean spring turbulence quantities. © 2012 Author(s)

Calibration and Validation of the Sentinels Geophysical Observation Models

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes