Shungudzemwoyo P Garaba

Comparison of remote sensing reflectance from above-water and in-water measurements west of Greenland, Labrador Sea, Denmark Strait, and west of Iceland

The need to obtain ocean color essential climate variables (OC-ECVs) using hyperspectral technology has gained increased interest in recent years. Assessing ocean color on a large scale in high latitude environments using satellite remote sensing is constrained by polar environmental conditions. Nevertheless, on a small scale we can assess ocean color using above-water and in-water remote sensing. Unfortunately, above-water remote sensing can only determine apparent optical properties leaving the sea surface and is susceptible to near surface environmental conditions for example sky and sunglint. Consequently, we have to rely on accurate in-water remote sensing as it can provide both synoptic inherent and apparent optical properties of seawater. We use normalized water leaving radiance LWN or the equivalent remote sensing reflectance RRS from 27 stations to compare the differences in above-water and in-water OC-ECVs. Analysis of above-water and in-water RRS spectra provided very good match-ups (R2 > 0.97, MSE < 1.8*10(-7)) for all stations. The unbiased percent differences (UPD) between above-water and in-water approaches were determined at common OC-ECVs spectral bands (410, 440, 490, 510 and 555) nm and the classic band ratio (490/555) nm. The spectral average UPD ranged (5 - 110) % and band ratio UPD ranged (0 - 12) %, the latter showing that the 5% uncertainty threshold for ocean color radiometric products is attainable. UPD analysis of these stations West of Greenland, Labrador Sea, Denmark Strait and West of Iceland also suggests that the differences observed are likely a result of environmental and instrumental perturbations.

Physical, Bio-Optical State and Correlations in North–Western European Shelf Seas

Color of seawater has become an integral tool in understanding surface marine ecosystems and processes. In this paper we seek to assess the correlations and consequently the potential of using shipborne remote sensing products to infer marine environmental parameters. Typical seawater parameters are chlorophyll–a (chl–a), colored dissolved organic material (CDOM), suspended particulate material (SPM), Secchi–disk depth (SDD), temperature, and salinity. These parameters and radiometric quantities were observed from a total of 60 stations covering German Bight, North Sea, Inner Seas, Irish Sea, and Celtic Sea. Bio-optical models developed in this study were used to predict the in situ measured parameters, with low mean unbiased percent differences and absolute percent difference less than 35%. Our investigations show that the use of ocean color products namely the Forel–Ule Index to infer seawater parameters is encouraging. The constrained spatial and temporal span of measured in situ parameters does limit the accuracy of our models. Absorption coefficients of the main color producing agents CDOM, chl–a, and inorganic fraction of SPM (iSPM) were determined to estimate absorption budgets. During the field campaign, iSPM was the primary light absorber over the spectral range (400–700 nm) although variabilities were observed in the regional seas.

Sunglint Detection for Unmanned and Automated Platforms

We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture sea surface and sky images of the investigated points. The quality control consists of meteorological flags, to mask dusk, dawn, precipitation and low light conditions, utilizing incoming solar irradiance (ES) spectra. Using 629 from a total of 3,121 spectral measurements that passed the test conditions of the meteorological flagging, a new sunglint flag was developed. To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented. Applying this algorithm, two sets of data, one with (having too much or detectable white pixels or sunglint) and one without sunglint (having least visible/detectable white pixel or sunglint), were derived. To identify the most effective sunglint flagging criteria we evaluated the spectral characteristics of these two data sets using water leaving radiance (LW) and remote sensing reflectance (RRS). Spectral conditions satisfying ‘mean LW (700–950 nm) < 2 mW·m−2·nm−1·Sr−1’ or alternatively ‘minimum RRS (700–950 nm) < 0.010 Sr−1’, mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control.

Classifying Natural Waters with the Forel-Ule Colour Index System: Results, Applications, Correlations and Crowdsourcing

Societal awareness of changes in the environment and climate has grown rapidly, and there is a need to engage citizens in gathering relevant scientific information to monitor environmental changes due to recognition that citizens are a potential source of critical information. The apparent colour of natural waters is one aspect of our aquatic environment that is easy to detect and an essential complementary optical water quality indicator. Here we present the results and explore the utility of the Forel-Ule colour index (FUI) scale as a proxy for different properties of natural waters. A FUI scale is used to distinguish the apparent colours of different natural surface water masses. Correlation analysis was completed in an effort to determine the constituents of natural waters related to FUI. Strong correlations with turbidity, Secchi-disk depth, and coloured dissolved organic material suggest the FUI is a good indicator of changes related to other constituents of water. The increase in the number of tools capable of determining the FUI colours, (i) ocean colour remote sensing products; (ii) a handheld scale; and (iii) a mobile device app, make it a versatile relative measure of water quality. It has the potential to provide higher spatial and temporal resolution of data for a modernized classification of optical water quality. This FUI colour system has been favoured by several scientists in the last century because it is affordable and easy to use and provides indicative information about the colour of water and the water constituents producing that colour. It is therefore within the scope of a growing interest in the application and usefulness of basic measurement methodologies with the potential to provide timely benchmark information about the environment to the public, scientists and policymakers.