Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0–3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data

Influence of vertical distribution of phytoplankton on remote sensing signal of Case II waters : southern Caspian Sea case study

Reliable monitoring of coastal waters is not possible without using remote sensing data. On the other hand, it is quite difficult to develop remote sensing algorithms that allow one to retrieve water characteristics (like chlorophyll-a concentration) in optically complex coastal and inland waters (called also Case II waters) as the concentrations of optically active substances (phytoplankton, suspended matter, and colored dissolved organic matter) vary independently from each other and the range of variability is often high. Another problem related to developing remote sensing algorithms for retrieving concentrations of optically active substances in such complex waters is vertical distribution of these substances. For example, phytoplankton distribution in the water column is often characterized with maxima just below the surface mixed layer, and some phytoplankton species even have the capability to migrate in the water column and tend to form layers at depths optimal for their growth. Twenty-three field campaigns were performed during the spring-summer period in the coastal waters of the southern Caspian Sea where vertical distribution of phytoplankton was measured by means of chlorophyll-a fluorometer. There results showed that there is usually a chlorophyll-a maximum between 10 and 20 m where the concentration is about one order of magnitude higher than in the top mixed layer. The Hydrolight 5.0 radiative transfer model used to estimate if the vertical distribution of biomass have detectable impact on remote sensing signal in these waters. For that purpose, several stations with distinctly different chlorophyll-a profiles were selected and two simulations for each of those measuring stations was carried out. First the Hydrolight was run with the actual chlorophyll-a vertical distribution profile and second a constant chlorophyll-a value (taken as an average of measured chlorophyll-a in the surface layer) was used in the model simulation. The modelling results show that the “deep” chlorophyll maximum has negligible effect on the remote sensing reflectance spectra. Consequently, there is no need to take into account the vertical distribution of phytoplankton while developing remote sensing algorithms for the Caspian Sea coastal water

Management Options to Improve Water Quality in Lake Peipsi: Insights from Large Scale Models and Remote Sensing

Nutrient pollution causes frequent blooms of potentially harmful cyanobacteria in Lake Peipsi (Estonia/Russia). Although external nutrient loading has reduced since the 1990s, lake water quality has barely improved, and eutrophication is still considered a threat to lake biota and water usage. To understand the recovery dynamics of the lake it is necessary to analyse the effects of land use and lake management on water quality to develop mitigation strategies. Comprehensive analysis has thus far failed due to information gaps inherent to conventional monitoring strategies. We show how two large-scale hydrological models using Earth observation data provide spatial information on pollution and can help explain the causes of past and current lake eutrophication. WaterGAP3.2 provides valid estimates of present and probable future phosphorus concentration in the lake water, based on past hydrological conditions. WaterWorld models spatial potential water quality and a scenario of optimal pollution reduction. Remotely sensed optical water quality data can be used to analyse recent, spatial water quality dynamics. The spatial and temporal algae distributions and can help explain eutrophication causes at Lake Peipsi and its catchment, adding value to in situ monitoring and supporting river basin management with large scale data

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Digitise This! A Quick and Easy Remote Sensing Method to Monitor the Daily Extent of Dredge Plumes

Technological advancements in remote sensing and GIS have improved natural resource managers’ abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and associated algorithms to predict the total suspended solids (TSS) concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L-1. However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L-1, and began providing false-negatives (excluding actual plume) at a threshold as low as 4 mg L-1. Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training

Global divergent trends of algal blooms detected by satellite during 1982–2018

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2) around the globe over a 37-year time span (1982–2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p<.01) and AR (p <.01) before 1999, followed by a decrease in ABs FRQR (p <.01) and AR (p <.05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters

Colorful Niches of Phytoplankton Shaped by the Spatial Connectivity in a Large River Ecosystem: A Riverscape Perspective

Large rivers represent a significant component of inland waters and are considered sentinels and integrators of terrestrial and atmospheric processes. They represent hotspots for the transport and processing of organic and inorganic material from the surrounding landscape, which ultimately impacts the bio-optical properties and food webs of the rivers. In large rivers, hydraulic connectivity operates as a major forcing variable to structure the functioning of the riverscape, and–despite increasing interest in large-river studies–riverscape structural properties, such as the underwater spectral regime, and their impact on autotrophic ecological processes remain poorly studied. Here we used the St. Lawrence River to identify the mechanisms structuring the underwater spectral environment and their consequences on pico- and nanophytoplankton communities, which are good biological tracers of environmental changes. Our results, obtained from a 450 km sampling transect, demonstrate that tributaries exert a profound impact on the receiving river’s photosynthetic potential. This occurs mainly through injection of chromophoric dissolved organic matter (CDOM) and non-algal material (tripton). CDOM and tripton in the water column selectively absorbed wavelengths in a gradient from blue to red, and the resulting underwater light climate was in turn a strong driver of the phytoplankton community structure (prokaryote/eukaryote relative and absolute abundances) at scales of many kilometers from the tributary confluence. Our results conclusively demonstrate the proximal impact of watershed properties on underwater spectral composition in a highly dynamic river environment characterized by unique structuring properties such as high directional connectivity, numerous sources and forms of carbon, and a rapidly varying hydrodynamic regime. We surmise that the underwater spectral composition represents a key integrating and structural property of large, heterogeneous river ecosystems and a promising tool to study autotrophic functional properties. It confirms the usefulness of using the riverscape approach to study large-river ecosystems and initiate comparison along latitudinal gradients

DELINEAMENTO AMOSTRAL EM RESERVATÓRIOS UTILIZANDO IMAGENS LANDSAT-8/OLI: UM ESTUDO DE CASO NO RESERVATÓRIO DE NOVA AVANHANDAVA (ESTADO DE SÃO PAULO, BRASIL)

O uso do sensoriamento remoto voltado para a determinação de amostras de campo é de grande valia para estudos ambientais, uma vez que as imagens de satélite apresentam atributos capazes de avaliar a variabilidade espectral da superfície da água considerando uma área extensa. Desse modo, a abordagem deste trabalho objetiva definir um método de seleção estratificada de amostras baseada na variabilidade de imagens no espectro do visível e infravermelho oriundos do sensor Landsat-8/OLI. O método conta com a utilização de dados raster que representam o desvio padrão de uma série temporal de imagens Landsat-8/OLI e em seguida a definição automática de pontos de campo apoiada na técnica de amostragem estratificada aleatória. A escolha da imagem que deu origem a seleção dos pontos foi baseada na componente de maior variabilidade espectral por meio da técnica de Principal Componente. Como resultado foram obtidos vinte pontos representativos de um total de seis classes espectralmente semelhantes