Increased frequency and extent of potentially harmful blooms in coastal and inland waters world-wide require the development of methods for operative and reliable monitoring of the blooms over vast ...coastal areas and a large number of lakes. Remote sensing could provide the tool. An overview of the literature in this field suggests that operative monitoring of the extent of some types of blooms (i.e. cyanobacteria) is relatively straightforward. Operative monitoring of inland waters is currently limited to larger lakes or using airborne and hand-held remote sensing instruments as there are no satellite sensors with sufficient spatial resolution to provide daily coverage. Extremely high spatial and vertical variability in biomass during blooms of some phytoplankton species and the strong effects of this on the remote sensing signal suggest that water sampling techniques and strategies have to be redesigned for highly stratified bloom conditions, especially if the samples are collected for algorithm development and validation of remote sensing data. Comparing spectral signatures of different bloom-forming species with the spectral resolution available in most satellites and taking into account variability in optical properties of different water bodies suggests that developing global algorithms for recognizing and quantitative mapping of (harmful) algal blooms is questionable. On the other hand some authors cited in the present paper have found particular cases where satellites with coarse spectral and spatial resolution can be used to recognize phytoplankton blooms even at species level. Thus, the algorithms and methods to be used depend on the optical complexity of the water to which they will be applied. The aim of this paper is to summarize different methods and algorithms available in an attempt to assist in selecting the most appropriate method for a particular site and problem under investigation.
An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are ...poorly constrained. We used high‐resolution satellite imagery to produce a GLObal WAter BOdies database (GLOWABO), comprising all lakes greater than 0.002 km2. GLOWABO contains geographic and morphometric information for ~117 million lakes with a combined surface area of about 5 × 106 km2, which is 3.7% of the Earth's nonglaciated land area. Large and intermediate‐sized lakes dominate the total lake surface area. Overall, lakes are less abundant but cover a greater total surface area relative to previous estimates based on statistical extrapolations. The GLOWABO allows for the global‐scale evaluation of fundamental limnological problems, providing a foundation for improved quantification of limnetic contributions to the biogeochemical processes at large scales.
Key Points
Earth has 117 million lakes > 0.002 km2Large and intermediate lakes dominate the total surface area of lakesPower law‐based extrapolations do not adequately estimate lake abundance
The extent of cyanobacterial blooms has been mapped using different satellite sensors from weather satellites to synthetic aperture radars. Quantitative detection of chlorophyll in cyanobacterial ...blooms by remote sensing, however, has been less successful. The first civilian hyperspectral sensor in space, Hyperion, acquired an image of cyano-bacterial bloom in the western part of the Gulf of Finland on 14 July 2002. A chlorophyll concentration map was produced from this image using a spectral library that was created by running a bio-optical model with variable concentrations of chlorophyll. The results show that chlorophyll concentrations in the bloom area were much higher than reported by conventional water-monitoring programs, ships-of-opportunity, and satellite measurements. The reason why both in situ and satellite methods underestimate the amount of phytoplankton during cyanobacterial blooms is vertical and horizontal distribution of cyanobacteria, because cyanobacteria can regulate their buoyancy and are not uniformly distributed within the top mixed layer of water column in calm weather conditions. Aggregations of cyanobacteria form dense subsurface blooms and surface scums during extensive blooms. This study demonstrates that it is difficult to collect representative water samples from research vessels using standard methods because ships and water samplers destroy the natural distribution of cyanobacteria in the sampling process. Flow-through systems take water samples from the depths at which the concentration of cyanobacteria is not correlated with the amount of phytoplankton that remote sensing instruments detect. The chlorophyll estimation accuracy in cyanobacterial blooms by many satellites is limited because of spatial resolution, as significant changes in chlorophyll concentration occur even at a smaller spatial scale than 30 m.
Abstract Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is ...important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997–2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter ( b bp (555)), the colored dissolved organic matter, CDOM ( a dg (412)), and the light diffuse attenuation coefficient in water ( K d (490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and b bp (555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time.
Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional ...in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with
0.84-0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.
The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ...ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km
. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L
. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.
The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. ...Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.
Many lakes in boreal and arctic regions have high concentrations of CDOM (coloured dissolved organic matter). Remote sensing of such lakes is complicated due to very low water leaving signals. There ...are extreme (black) lakes where the water reflectance values are negligible in almost entire visible part of spectrum (400–700 nm) due to the absorption by CDOM. In these lakes, the only water-leaving signal detectable by remote sensing sensors occurs as two peaks—near 710 nm and 810 nm. The first peak has been widely used in remote sensing of eutrophic waters for more than two decades. We show on the example of field radiometry data collected in Estonian and Swedish lakes that the height of the 810 nm peak can also be used in retrieving water constituents from remote sensing data. This is important especially in black lakes where the height of the 710 nm peak is still affected by CDOM. We have shown that the 810 nm peak can be used also in remote sensing of a wide variety of lakes. The 810 nm peak is caused by combined effect of slight decrease in absorption by water molecules and backscattering from particulate material in the water. Phytoplankton was the dominant particulate material in most of the studied lakes. Therefore, the height of the 810 peak was in good correlation with all proxies of phytoplankton biomass—chlorophyll-a (R2 = 0.77), total suspended matter (R2 = 0.70), and suspended particulate organic matter (R2 = 0.68). There was no correlation between the peak height and the suspended particulate inorganic matter. Satellite sensors with sufficient spatial and radiometric resolution for mapping lake water quality (Landsat 8 OLI and Sentinel-2 MSI) were launched recently. In order to test whether these satellites can capture the 810 nm peak we simulated the spectral performance of these two satellites from field radiometry data. Actual satellite imagery from a black lake was also used to study whether these sensors can detect the peak despite their band configuration. Sentinel 2 MSI has a nearly perfectly positioned band at 705 nm to characterize the 700–720 nm peak. We found that the MSI 783 nm band can be used to detect the 810 nm peak despite the location of this band is not in perfect to capture the peak.
Optical types of inland and coastal waters Spyrakos, Evangelos; O’Donnell, Ruth; Hunter, Peter D. ...
Limnology and oceanography,
March 2018, Letnik:
63, Številka:
2
Journal Article
Recenzirano
Odprti dostop
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.
•A workflow was proposed to map SAV abundance in heterogeneous benthic environment.•Water column corrected reflectance provided highest accuracy %cover estimates.•%cover predicted a significant ...proportion of biomass for all studied SAV classes.•CASI and Sentinel-2 data were compared in mapping SAV distribution and abundance.•Sentinel-2 showed a good ability in mapping SAV biomass in shallow coastal waters.
This work assessed the capability of Compact Airborne Spectrographic Imager (CASI) and satellite multispectral Sentinel-2 image data for mapping the distribution, percent cover (%cover) and biomass of submerged aquatic vegetation (SAV) in optically complex coastal waters of the Baltic Sea. As a first step, the distribution maps of SAV were created for brown macroalgae, green macroalgae and higher plants classes. Secondly, %cover maps were retrieved by building class level relationships between in situ estimated %cover and image reflectance. Thirdly, statistical models were built for estimating class specific SAV biomass as a function of SAV %cover. Finally, developed biomass models were applied to class specific %cover maps derived from the step 2 for landscape scale biomass estimation. CASI sensor had higher classification accuracy (78%) compared to Sentinel-2 sensor (69%). CASI also outperformed Sentinel-2 in the %cover assessment showing R2 values in the range of 0.55–0.73, while R2 values in the range of 0.36–0.49 were retrieved for Sentinel-2. However, both sensors provided similar distribution and %cover patterns of benthic vegetation. The %cover-biomass models showed a very good fit explaining 66–82% of variance of different SAV classes. Comparison of biomass estimates from both images revealed that the total dry biomass (t) was underestimated by Sentinel-2 by 10.6%. However, if biomasses were retrieved per unit area (t/km2), then both instruments resulted in nearly identical total SAV biomasses.
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