Top PDF The Use of Landsat 8 for Monitoring of Fresh and Coastal Waters

The Use of Landsat 8 for Monitoring of Fresh and Coastal Waters

The Use of Landsat 8 for Monitoring of Fresh and Coastal Waters

I would like to extend special thanks to the United States Geological Survey (USGS) for its sponsorship (contract number G12PC00065) and for the free-access Landsat archive that has made this effort possible. Thanks to the Fulbright Commission for opening me the doors and sponsoring my M.S. degree. Special thanks to the Monroe County Environmental Laboratory of the Monroe County Department of Environmental Services for the C a and T SS measurements used to validate our in-house concentration measurements, especially Scott, Providencia, and Gary. Nina Raque˜ no is thanked for her invaluable help in the logistic of the field data collections. Faculty, staff, and students at the Chester F. Carlson Center for Imaging Science at RIT for helping in the data collection, especially Paul Romanczyk, Rolando Raque˜ no, Aaron Gerace and Captain Mark. Thanks to the professors Dr. Kerekes, Dr. Messinger. Dr. Bachmann, and Dr. Vodacek, among others, for their support. Also, thanks to Dr. Christy Tyler for facilitating the Aquatic Ecology Lab at RIT for the C a measurements. Also, thanks to Ocean Color community, especially Dr. Emmanuel Boss and attendants of the 2013 Ocean color and instrumentation summer course at the Darling Marine Center for including me in their family. To my advisor Dr. John Schott for believing on me, guiding me and allowing me independence. Thanks to the administrative staff at the center, especially Cindy, Sue, and Amanda Zeluff for always helping me with a smile.
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Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters

Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters

AoI1 nearshore cross-profiles spaced by 1000 m and extracted from bathymetric contour maps 734. Bm1, Bm2, and Bm3[r]

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Retrieval of nearshore bathymetry from Landsat 8 images: a tool for coastal monitoring in shallow waters

Retrieval of nearshore bathymetry from Landsat 8 images: a tool for coastal monitoring in shallow waters

Nearshore bathymetry is likely to be the coastal variable that most limits the investigation of coastal processes and the accuracy of numerical models in coastal areas, as acquiring medium spatial resolution data in the near- shore is highly demanding and costly. As such, the ability to derive bathymetry using remote sensing techniques is a topic of increasing interest in coastal monitoring and research. This contribution focuses on the application of the linear transform algorithm to obtain satellite-derived bathymetry (SDB) maps of the nearshore, at medium resolution (30 m), from freely available and easily accessible Landsat 8 imagery. The algorithm was tuned with available bathymetric Light Detection and Ranging (LiDAR) data for a 60-km-long nearshore stretch of a highly complex coastal system that includes barrier islands, exposed sandy beaches, and tidal inlets (Ria Formosa, Portugal). A comparison of the retrieved depths is presented, enabling the con figuration of nearshore profiles and extracted isobaths to be explored and compared with traditional topographic/bathymetric techniques (e.g., high- and medium-resolution LiDAR data and survey-grade echo-sounding combined with high-precision positioning systems). The results demonstrate that the linear algorithm is ef ficient for retrieving bathymetry from multi-spectral satellite data for shallow water depths (0 to 12 m), showing a mean bias of −0.2 m, a median difference of −0.1 m, and a root mean square error of 0.89 m. Accuracy is shown to be depth dependent, an inherent limitation of passive optical detection systems. Accuracy further decreases in areas where turbidity is likely to be higher, such as locations adjacent to tidal inlets. The SDB maps provide reliable estimations of the shoreline position and of nearshore isobaths for different cases along the complex coastline analysed. The use of freely available satellite imagery proved to be a quick and reliable method for acquiring updated medium- resolution, high-frequency (days and weeks), low-cost bathymetric information for large areas and depths of up to 12 m in clear waters without wave breaking, allowing almost constant monitoring of the submerged beach and the shoreface.
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Application of Landsat 8 for monitoring impacts of wastewater discharge on coastal water quality

Application of Landsat 8 for monitoring impacts of wastewater discharge on coastal water quality

From 21 September−2 November 2015, the HTP discharged ∼39×10 3 m 3 h −1 of treated wastewater into Santa Monica Bay through their emergency 1-mile outfall pipe. Multi-sensor satellite remote sensing was employed to determine the biophysical impact of discharged wastewater in the shallow nearshore environment. Landsat 8 TIRS observed decreased sea surface temperatures (SST) associated with the surfacing wastewater plume. Chlorophyll-a (chl-a) concentrations derived from Landsat 8 OLI and Aqua MODIS satellite sensors were used to monitor the biological response to the addition of nutrient-rich wastewater. In situ chl-a and in situ remote sensing reflectance (Rrs) were measured before, during, and after the diversion event. These in situ data were paired with coincident OLI and MODIS satellite data to yield a more comprehensive view of the changing conditions in Santa Monica Bay due to the wastewater diversion. Two new local chl-a algorithms were empirically derived using in situ data for the OLI and MODIS sensors. These new local chl-a algorithms proved more accurate at measuring chl-a changes in Santa Monica Bay compared to the standard open ocean OC2 and OC3M algorithms, and the regional southern California CALFIT algorithm, as validated by in situ chl-a measurements. Additionally, the local OLI algorithm outperformed the local MODIS algorithm, especially in the nearshore region. A time series of chl-a, as detected by the local OLI chl-a algorithm, illustrated a very large increase in chl-a concentrations during the wastewater diversion, and a subsequent decrease in chl-a after the diversion.
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An Integrated physics-based approach to demonstrate the potential of the Landsat Data Continuity Mission (LDCM) for monitoring coastal/inland waters

An Integrated physics-based approach to demonstrate the potential of the Landsat Data Continuity Mission (LDCM) for monitoring coastal/inland waters

surface reflectance map. The retrieved concentration maps are then analyzed regarding the differences in the distributions of the concentrations. 2.2.2 Task II Our main objective is to develop a method to retrieve water constituents on a pixel-by- pixel basis using a coupled modeling system. The integration of L7 and the coupled modeling system enables capturing the dynamics of coastal waters. With traditional methods of remote sensing, the water constituents are obtained solely for an instant of time. In the light of this, the ALGE model is run for a certain period through which the model is stabilized. From this point on, the procedure is followed separately in the thermal and reflective domains by re-starting ALGE for a short period. In the first step, the model is calibrated via a model-matching technique in which the modeled surface temperatures, which are generated through multiple simulations, are optimized with the L7-derived surface temperature maps. In the second step, ALGE is re-started again by varying a set of variables controlling the material distribution throughout the model domain. This is followed by the application of an in-water radiative transfer model to convert profiles of material concentrations to surface reflectance. The best match is then determined via optimization against L7 surface reflectance products. This two-step approach is tested for two different river plumes, namely the Genesee River and the Niagara River, as well as Onondaga Lake located in New York state, USA. The proposed approach is implemented in six different timeframes for the river plume simulations and two periods for the Onondaga Lake simulations. This helps understand how well this approach works in different environmental conditions at different sites.
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Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

weighted average of the pixel radiance values surround- ing the target pixel. In an iterative manner, the optimal range (N) for defining the environmental influences is determined as shown in Figure 1 . Sterckx et al. ( 2011 ) defined optimal waveband settings for the “test” band as; (i) minimum influence of gaseous absorption, (ii) not lower than 690 nm because of increasing uncer- tainty, (iii) preferably located in the red-edge region of the spectrum where contrast between water and land is more pronounced. The availability of spectral bands is a restrictive factor in multispectral sensors. The band setting of Landsat-8 is not ideal because spectral bands at 655 nm and 865 nm are selected respectively as “test” and “reference”. Sentinel-2 allows a better spectral band selection with 705 nm and 783 nm selected respectively. Sterckx et al. ( 2014 ) defined restrictions for the use of SIMEC which tends to fail in (i) high turbid waters where the NIR reflectance is flattened (Doron, Bélanger, Doxaran, & Babin, 2011 ; Goyens, Jamet, & Ruddick, 2013 ), (ii) in waters with macrophyte growth or specific algae blooms and in (iii) areas where bottom effects are significant in the NIR (optically shallow waters).
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Determination of Total Suspended Solids (TSS) and Total Volatile Solids (TVS) in Waters of Fresh/Estuarine/Coastal Waters

Determination of Total Suspended Solids (TSS) and Total Volatile Solids (TVS) in Waters of Fresh/Estuarine/Coastal Waters

certified dissolved sample which is obtained from an external source. If a certified sample is not available, then use the standard material. 8. SAMPLE COLLECTION, PRESERVATION, AND STORAGE 8.1 Water collected for TSS and/or TVS should be filtered through a Whatman GF/F glass fiber filter (nominal pore size 0.7 m), or equivalent.

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ASSESSING THE WATER QUALITY OF FRESH AND MARINE WATERS IN COASTAL AREAS OF RIVERS STATE, NIGERIA

ASSESSING THE WATER QUALITY OF FRESH AND MARINE WATERS IN COASTAL AREAS OF RIVERS STATE, NIGERIA

The coastal area of Rivers State is located in the Niger Delta of Nigeria and is criss-crossed by different rivers, streams, creeks and rivulets and they are exposed to uncontrolled, untreated non-point source pollution as they receive run-offs, sediments and sewage directly into them. The aim of this study is to compare the water quality of a fresh and marine water in the coastal area. The water samples were collected in sterile containers for physicochemical and bacteriological estimations using standard methods. The physicochemical parameters were determined by using the Hatchi (2000) instrument that analysed the pH, temperature, conductivity and turbidity in situ, while the spectrophotometric techniques were used for the nitrate, phosphate and other parameters as chloride and alkalinity were determined by titration methods. The standard conventional culturing technique of isolation, identification and biochemical tests were used for the bacteriological analysis of water. The results of the physicochemical parameters are pH 5.44 ± 0.37 to 7.42 ± 0.28, temperature 27.5 o C ± 1.10 to 28.9 ± 1.13 o C, conductivity 13.75 ± 0.35 to 32750 ± 353.55µs/cm. Dissolved oxygen 2.94 ± 0.01 to 7.57 ± 0.05 mg/l, chloride 1.00 ± 0.00 to 10397.40 ± 32.81 mg/l and the others. The identified bacteria in the water samples were Staphylococcus spp, Bacillus spp, Klebsiella spp, Vibrio spp, Pseudomonas spp, Escherichia coli and Chromobacteria violaceum. It can be inferred that the presence of the isolated bacteria could result to outbreak of epidemics and knowledge of the strains of the bacteria would help in managing their presence in public water.
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The  tide  in  coastal  waters

The tide in coastal waters

determining the ownership of the copyright, and for obtaining permission for your intended use. Yale University makes no warranty that your distribution, reproduction, or other use of these materials will not infringe the rights of third parties. This work is licensed under the Creative Commons Attribution-

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REPORT BIOLOGICAL MONITORING OF COASTAL MARINE WATERS AND LAKES - BENTHIC INVERTEBRATE FAUNA

REPORT BIOLOGICAL MONITORING OF COASTAL MARINE WATERS AND LAKES - BENTHIC INVERTEBRATE FAUNA

The historical data available for station Cocketrice (BG2BS000C009) encompasses a considerable period of 17 years since 1992 to 2008, though missing data for 6 years makes the dataset somewhat sporadic and irregular. AMBI fluctuates in the range 2.03 – 3.3, i.e. good ecological state is observed throughout the period 1992 – 2008 (Figure 6, Table 8), no trend of improvement or worsening is evident. Diversity shows strong variability in 1992, the three replicate samples with H’ falling within the moderate, good and high range of values, the good state accepted as an average (Figure 6, Table 8). In 1993 H’ values for both replicates are in the range of the moderate state. Since 1995 H’ fluctuates weaker within the range of the good ecological state with the exception of 2007 when diversity shows moderate ecological state. The historical trends in M-AMBI largely follow the pattern of H’ with stronger variability between moderate and high status in the beginning of the 1990s (1992, 1993) and more narrow fluctuation within the range of the good ecological state since 1995 to the present (Figure 7, Table 8). On the overall the indices show stabilization of the ecological state within the range of the good state values since mid1990s.
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SOLUBLE  ffiON  IN  COASTAL  WATERS

SOLUBLE ffiON IN COASTAL WATERS

determining the ownership of the copyright, and for obtaining permission for your intended use. Yale University makes no warranty that your distribution, reproduction, or other use of these materials will not infringe the rights of third parties. This work is licensed under the Creative Commons Attribution-

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Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

With the increase in usage of OC products among the science community, and the need for robust R rs products, it is important to understand the potential of AC algorithms. Most of the ocean colour community has for years been using water-based AC methods for a wide range of applications from coastal to inland waters, so it is important that the effects of relevant environmental variables on the R rs retrieval accuracy of these AC algorithms is examined. Such knowledge may assist in the choice of AC algorithm for a given set of environmental conditions, and/or improved R rs retrieval under a wider range of conditions. Equally important is that other users interested in studying inland waters (e.g., biogeochemists, aquatic biologists) fully understand the accuracy of generic AC processors, in particular the land surface reflectance product, which is commonly used.
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Slope algorithm to map algal blooms in inland waters for Landsat 8/ Operational Land Imager images

Slope algorithm to map algal blooms in inland waters for Landsat 8/ Operational Land Imager images

The moderate-resolution imaging spectroradiometer (MODIS) and medium resolution im- aging spectrometer (MERIS) are the most commonly used remote sensors for monitoring water quality. 10 However, because of their moderate spatial resolution, 1000 and 300 m respec- tively, it is not possible to monitor water quality from small aquatic systems or to detect spatial variability within them. The most recent satellite from the Landsat family (Landsat 8) carries the Operational Land Imager (OLI) sensor, which is a nine band push broom imager with eight channels at 30-m spatial resolution. 11 This spatial resolution combined with the global data avail- ability makes OLI an important sensor for the monitoring of worldwide aquatic systems. 12 , 13 Although OLI presents a better spatial resolution when compared to MODIS and MERIS, OLI ’s temporal resolution is not appropriate for an operational monitoring once algal blooms quickly respond to changes in the environment. Therefore, OLI can be used to map algal bloom extension. The use of sensors such as MODIS and MERIS, which have a higher temporal res- olution, are appropriated in areas where spatial resolution is not an issue and/or there is a need to create a time series of the obsevervations. 14 , 15 Another important issue was highlighted by Hu 16 who observed that indices commonly used for algal blooms identification were influenced by the atmosphere. Therefore, an algorithm that can map the extension of algal blooms without being influenced by the atmospheric correction used has a considerably advantage.
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Present Status of Coastal Environmental Monitoring in Korean Waters. Using Remote Sensing Data

Present Status of Coastal Environmental Monitoring in Korean Waters. Using Remote Sensing Data

3. Result In order to evaluate the usefulness of remote sensing techniques as a monitoring tool for the marine environment including coastal area, a case study was conducted in the southern waters of the Korean Peninsula and northern part of the ECS. We also monitored cold water mass in the southeast of Korea.

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Lake Michigan Pilot Study. National Monitoring Network for U.S. Coastal Waters and Their Tributaries

Lake Michigan Pilot Study. National Monitoring Network for U.S. Coastal Waters and Their Tributaries

Management options for improved beach monitoring One of the toughest management options is to determine when routine monitoring is no longer cost- effective and funding needs to be directed towards improvements to the beach monitoring program. For example, one of the management options is to use limited funds for strategic monitoring and source tracking methods at the expense of routine monitoring practices at beaches that have consistently excellent or consistently poor water quality. Beaches that have consistently excellent water quality are likely to have a lower health risk and may not need routine monitoring. Beaches that have consistently poor water quality are not likely to improve without remediation. Limited funding that had been used for routine monitoring may be better spent for strategic monitoring and source tracking efforts until sources of contamination can be identified and remediated. Another example is when routine monitoring is no longer acceptable due to the significant delay in getting the results and funding is directed towards the development of a predictive model. Several beach managers have commented that decisions about beach status can be inaccurate up to 100% of the time when using data from routine monitoring. Predictive models are improving their accuracy with some models achieving 90% and higher accuracy in predicting water quality conditions. Again, limited funding that had been used for routine monitoring may be better spent for the development of a predictive model.
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Assessing the Potential of Remotely sensed Data for Water Quality Monitoring of Coastal and Inland Waters

Assessing the Potential of Remotely sensed Data for Water Quality Monitoring of Coastal and Inland Waters

Empirical and semi-empirical algorithms are easy to use, however, the coefficients used in empirical algorithms are derived from data sets that do not necessarily represent all natural variations. The performance of such algorithms is always subject to compatibility between the waters under study and the waters from which data were obtained for algorithm development. The clear vision and understanding of relationship between water quality parameters and optical measurements made by means of remote sensing techniques is imperative for monitoring water bodies. This study demonstrates that the remotely sensed data can be a useful tool for monitoring the distributions of water quality parameters in coastal and inland waters. The water quality and quantity “Q & Q” connection” is vital for sustainable water resources development &
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Landsat image enhancement techniques for subtidal and intertidal seagrass detection and distribution mapping in the coastal waters of Sungai Pulai estuary, Malaysia

Landsat image enhancement techniques for subtidal and intertidal seagrass detection and distribution mapping in the coastal waters of Sungai Pulai estuary, Malaysia

E-mail:‌muta@upm.edu.my *Author‌for‌correspondence Received 23 June 2014; Accepted 28 October 2014 Abstract — In Malaysia, seagrasses commonly inhabit shallow intertidal waters, semi enclosed lagoons, mangroves, coral reef flats and shoals in subtidal zones. Seagrass meadows have widely been surveyed by field sampling methods. As an alternative means to field-based surveys, airborne and/or satellite based sensors have been used to produce cost-effective and, more impor- tantly, repetitive sources of information on seagrass distribution over wider areas. The satellite-based sensors Landsat imagery have been used as relatively economic alternatives to aerial photographs to produce seagrass cover maps and change analysis. Two radiometric image enhancement techniques (ETs)—histogram equalization (HE) and manual enhancement (ME) were ap- plied on the series of Landsat images for comparative analysis and assessing ability of ETs to recognize seagrass meadows within the subtidal and intertidal coastal waters of the Sungai Pulai estuary, Johor Straits, Malaysia. With a view to find rela- tions between Mean Sea Level Tide Heights (MSLTHs) and results of ETs, actual 33 multi-date (1989–2014) images with a wide range of MSLTH regimes (−0.281 to 0.234 m) during image acquisition time, were processed by applying ETs. The ME substan- tially improved image quality compared to the HE, enabled detection of Seluyong seagrass meadows in intertidal mudflat, Mer- ambong, Tanjung Adang Darat, Tanjung Adang Laut shoals in the subtidal areas. Seagrass meadows were ‘easy-to-recognize’ without noticeable variations due to MSLTH differences from the enhanced images acquired during extreme lowest spring tide height, −0.218 m and above until MSLTH at −0.085 m; found ‘difficult-to-recognize’ at full extent between −0.067 to −0.003 m and ‘not-recognizable’ above MSLTH. ETs would be ineffective if applied to images acquired higher than MSLTH (0.007 to 0.234 m). The proposed ET is found to provide a consistent and quantitative areal cover for seagrass mapping and understand past changes from multi-date image analyses.
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Hazardous substances in fjords and coastal waters

Hazardous substances in fjords and coastal waters

The Sørfjord area has a considerable number of fruit orchards. Earlier use and the persistence of DDT and leaching from contaminated soil is probably the main reason for the observed high concentrations of ppDDE in the Sørfjord area. It must however be noted that the use of DDT products have been prohibited in Norway since 1970. Green et al. (2004a) concluded that the source of ppDDE was uncertain. Analyses of supplementary stations between Kvalnes and Krossanes in 1999 indicated that there could be several sources (Green et al. 2001). A more intensive investigation in 2002 with seven sampling stations confirmed that there were two main areas with high concentrations north of Kvalnes and near Urdheim south of Krossanes (Green et al. 2004a). Skei et al. (2005) concluded that the variations in concentrations of ΣDDT and the ratio between p,p’-DDT/p,p’DDE (insecticide vs. metabolite) in blue mussel from Byrkjenes and Krossanes corresponds with periods with much precipitation and is most likely a result of wash-out from sources on shore. Botnen and Johansen (2006) set out passive samplers (SPMD- and PCC-18 samplers) at 12 locations along the Sørfjord to sample for DDT and its derivates in sea water. Blue mussel and sediments were also taken at some stations. The results indicated that further and more detailed surveys should be undertaken along the west side of the Sørfjord between Måge and Jåstad, and that replanting of old orchards might release DDT through erosion. Concentrations of ΣDDT in blue mussel in the Sørfjord in 2008-2011 showed up to Class V (extremely polluted) at Utne (Ruus et al. 2009, 2010a, 2011, 2012a). There was high variability in the concentrations of ΣDDT in replicate samples from Utne, indicating that the station is affected by DDT-compounds in varying degree, dependent on local conditions. The highest
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Hazardous substances in fjords and coastal waters

Hazardous substances in fjords and coastal waters

Dioxins have been included in Klif’s Pollution Index for blue mussel since 2002 (cf. chapter 4.4). No significant trends were observed for dioxins in blue mussel. Blue mussel from three stations in the Grenlandsfjord were extremely polluted with dioxins both in 2008 and 2009. Consumption advice has been issued for fish and shellfish in the Grenlandsfjord area due to the high concentrations of organochlorines including dioxins. It can be noted that environmental status is classified according to environmental quality criteria (based on ecotoxicological marginal values or presumed background levels) and must not be confused with limit values for human consumption and associated advices issued by the Norwegian Food Safety Authorities (Mattilsynet). Monitoring of contaminants in organisms from Grenland showed that the dioxins content in blue mussel is far over expected high background level, and this has not changed systematically since 1995 (Bakke et al. 2010). Results presented by Bakke et al. (2010) also indicated that dioxin concentrations have shown a tendency to increase outside the Frierfjord during the period 2002 to 2009 (examples dioxins in the
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Optical types of inland and coastal waters

Optical types of inland and coastal waters

16 Taihu Lake Laboratory Ecosystem Research Station, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, PR China Abstract Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is neces- sary 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. How- ever, 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 opti- cal water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n 5 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.
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