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Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually.

We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel–Ule index, hue angle and Secchi disk depth. Source data come from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data-merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ∼4 km at the Equator and are available at PANGAEA (https://doi.org/10.1594/PANGAEA.904266; Pitarch et al., 2019a). Two smaller and easier-to-handle test datasets have been produced from the former: a global dataset at 1∘ spatial resolution and another one for the North Atlantic at 0.25∘ resolution. The computer code for the generation of the Forel–Ule index, hue angle and Secchi disk depth from a given remote-sensing reflectance is also shared at https://doi.org/10.5281/zenodo.4439646 (Pitarch et al., 2021) and can be easily set in loop mode for batch calculations.

Soil moisture affects the partitioning of water and energy and is recognized as an essential climate variable. Soil moisture estimates derived from passive microwave remote sensing can improve model estimates through data assimilation, but the relative effectiveness of microwave retrievals in different frequencies is unclear. Land Parameter Retrieval Model (LPRM) satellite soil moisture derived from L-, C-, and X-band frequency remote sensing were assimilated in the Australian Water Resources Assessment landscape hydrology model (AWRA-L) using an ensemble Kalman filter approach. Two sets of experiments were performed. First, each retrieval was assimilated individually for comparison. Second, each possible combination of two retrievals was assimilated jointly. Results were evaluated against field-measured top-layer and root-zone soil moisture at 24 sites across Australia. Assimilation generally improved the coefficient of correlation (r) between modeled and field-measured soil moisture. L- and X-band retrievals were more informative than C-band retrievals, improving r by an average of 0.11 and 0.08 compared to 0.04, respectively. Although L-band retrievals were more informative for top-layer soil moisture in most cases, there were exceptions, and L- and X-band were equally informative for root-zone soil moisture. The consistency between L- and X-band retrievals suggests that they can substitute for each other, for example when transitioning between sensors and missions. Furthermore, joint assimilation of retrievals resulted in a model performance that was similar to or better than assimilating either retrieval individually. Comparison of model estimates obtained with global precipitation data and with higher-quality, higher-resolution regional data, respectively, demonstrated that precipitation data quality does determine the overall benefit that can be expected from assimilation. Further work is needed to assess the potentially complementary spatial information that can be derived from retrievals from different frequencies.

Relationships between sea surface temperature (SST, > 10 m) and vertical density stratification, nutrient concentrations, and phytoplankton biomass, composition, and chlorophyll a (Chl a) specific absorption were assessed in spring and summer from latitudes 29 to 63° N in the northeast Atlantic Ocean. The goal of this study was to identify relationships between phytoplankton and abiotic factors in an existing SST and stratification gradient. Furthermore, a bio-optical model was used to estimate productivity for five phytoplankton groups. Nutrient concentration (integrated from 0 to 125 m) was inversely correlated with SST in spring and summer. SST was also inversely correlated with near-surface (0–50 m) Chl a and productivity for stratified stations. Near-surface Chl a and productivity showed exponential relationships with SST. Chl a specific absorption and excess light experiments indicated photoacclimation to lower irradiance in spring as compared to summer. In addition, Chl a specific absorption suggested that phytoplankton size decreased in summer. The contribution of cyanobacteria to water column productivity of stratified stations correlated positively with SST and inversely with nutrient concentration. This suggests that a rise in SST (over a 13–23 °C range) stimulates productivity by cyanobacteria at the expense of haptophytes, which showed an inverse relationship to SST. At higher latitudes, where rising SST may prolong the stratified season, haptophyte productivity may expand at the expense of diatom productivity. Depth-integrated Chl a (0–410 m) was greatest in the spring at higher latitudes, where stratification in the upper 200 m was weakest. This suggests that stronger stratification does not necessarily result in higher phytoplankton biomass standing stock in this region.

The colours from natural waters differ markedly over the globe, depending on the water composition and illumination conditions. The space-borne “ocean colour” instruments are operational instruments designed to retrieve important water-quality indicators, based on the measurement of water leaving radiance in a limited number (5 to 10) of narrow (≈10 nm) bands. Surprisingly, the analysis of the satellite data has not yet paid attention to colour as an integral optical property that can also be retrieved from multispectral satellite data. In this paper we re-introduce colour as a valuable parameter that can be expressed mainly by the hue angle (α). Based on a set of 500 synthetic spectra covering a broad range of natural waters a simple algorithm is developed to derive the hue angle from SeaWiFS, MODIS, MERIS and OLCI data. The algorithm consists of a weighted linear sum of the remote sensing reflectance in all visual bands plus a correction term for the specific band-setting of each instrument. The algorithm is validated by a set of 603 hyperspectral measurements from inland-, coastal- and near-ocean waters. We conclude that the hue angle is a simple objective parameter of natural waters that can be retrieved uniformly for all space-borne ocean colour instruments.

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 (FU) 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 FU numbers with FU numbers modelled from in situ radiometer measurements. Similar patterns and FU numbers were observed when comparing MERIS ocean colour distribution maps with ground truth Forel–Ule observations. The FU numbers modelled from in situ radiometer measurements showed a good correlation with observed FU numbers (R2 = 0.81 when full spectra are used and R2 = 0.71 when MERIS bands are used).

Marine primary productivity is an important agent in the global cycling of carbon dioxide, a major 'greenhouse gas', and variations in the concentration of the ocean's phytoplankton biomass can therefore explain trends in the global carbon budget. Since the launch of satellite-mounted sensors globe-wide monitoring of chlorophyll, a phytoplankton biomass proxy, became feasible. Just as satellites, the Forel-Ule (FU) scale record (a hardly explored database of ocean colour) has covered all seas and oceans--but already since 1889. We provide evidence that changes of ocean surface chlorophyll can be reconstructed with confidence from this record. The EcoLight radiative transfer numerical model indicates that the FU index is closely related to chlorophyll concentrations in open ocean regions. The most complete FU record is that of the North Atlantic in terms of coverage over space and in time; this dataset has been used to test the validity of colour changes that can be translated to chlorophyll. The FU and FU-derived chlorophyll data were analysed for monotonously increasing or decreasing trends with the non-parametric Mann-Kendall test, a method to establish the presence of a consistent trend. Our analysis has not revealed a globe-wide trend of increase or decrease in chlorophyll concentration during the past century; ocean regions have apparently responded differentially to changes in meteorological, hydrological and biological conditions at the surface, including potential long-term trends related to global warming. Since 1889, chlorophyll concentrations have decreased in the Indian Ocean and in the Pacific; increased in the Atlantic Ocean, the Mediterranean, the Chinese Sea, and in the seas west and north-west of Japan. This suggests that explanations of chlorophyll changes over long periods should focus on hydrographical and biological characteristics typical of single ocean regions, not on those of 'the' ocean.

Morphochronologic slip‐rates on the Altyn Tagh Fault (ATF) along the southern front of the Pingding Shan at ∼90.5°E are determined by cosmogenic radionuclide (CRN) dating of seven offset terraces at two sites. The terraces are defined based upon morphology, elevation and dating, together with fieldwork and high‐resolution satellite analysis. The majority of the CRN model ages fall within narrow ranges (<2 ka) on the four main terraces (T1, T2, T3 and T3′), and allow a detailed terrace chronology. Bounds on the terrace ages and offsets of 5 independent terraces yield consistent slip‐rate estimates. The long‐term slip‐rate of 13.9 ± 1.1 mm/yr is defined at the 95% confidence level, as the joint rate probability distribution of the rate derived from each independent terrace. It falls within the bounds of all the rates defined on the central Altyn Tagh Fault between the Cherchen He (86.4°E) and Akato Tagh (∼88°E) sites. This rate is ∼10 mm/yr less than the upper rate determined near Tura at ∼87°E, in keeping with the inference of an eastward decreasing rate due to progressive loss of slip to thrusts branching off the fault southwards but it is greater than the 9 ± 4 mm/yr rate determined at ∼90°E by GPS surveys and other geodetic short‐term rates defined elsewhere along the ATF. Whether such disparate rates will ultimately be reconciled by a better understanding of fault mechanics, resolved transient deformations during the seismic cycle or by more accurate measurements made with either approach remains an important issue. Key Points The ATF long‐term slip‐rate of 13.9 +/‐ 1.1 mm/yr at 90 deg E This rate is the joint probability of 5 independent terrace rate distributions Our results highlight an apparent slip‐rate decrease over time

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.

This study presents vertical profiles of turbulence parameters obtained in the upper 100 m of the northeastern Atlantic Ocean along a transect from tropical permanently stratified waters to subpolar seasonally stratified waters in July–August 2009. The focus is to fully characterize the vertical mixing along this transect for further studies related to phytoplankton and nutrient distributions. Derived values of temperature eddy diffusivity KT, of temperature variance dissipation rate χT, and of turbulent kinetic energy dissipation rate ε indicate a northward increase of turbulent mixing below the mixed layer. In the northern stations where the wind stress is sufficiently high, the vertical distributions of ε in the mixed layer follow the wind stress scaling. The related low wind‐scaling factors, and the low values of ε encountered at this cruise, are in agreement with a strongly stratified upper water column at midday in summer. Key Points Novel data set of turbulence microstructure measurements Large spatial coverage Atmospheric forcing of TKE dissipation rates