Barriers to adopting satellite remote sensing for water quality management

Schaeffer, Blake A.; Schaeffer, Kelly; Keith, Darryl J.; Lunetta, Ross S.; Conmy, Robyn N.; Gould, Richard W.

doi:10.1080/01431161.2013.823524

Cited by 181 publications

(114 citation statements)

References 34 publications

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“…Frequency maps of OWTs can be generated and are useful for understanding the distributions of the dominant water types for a given lake, leading to a first order indication of the types of AC schemes and retrieval algorithms that may be needed for processing, e.g., [3]. There is much interest in using remote sensing to support reporting for the European Water Framework Directive and U.S. Clean Water Act [53,54]. Frequent OWT maps can provide an insight into the state and seasonal patterns that occur in lakes.…”

Section: Discussionmentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

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Section: Discussionmentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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“…Both instruments have much finer spatial resolutions, ranging from 10 to 60 m, than that of the previous "ocean color" satellite missions (typically by a factor 10 to 100). Such a high spatial resolution is of great interest for understanding and monitoring the inland and coastal aquatic systems from passive remote sensing at local, regional and global scales (Schaeffer et al, 2013;Palmer et al, 2015). However, since the modeling of the top-of-atmosphere radiation remains a challenging task for decameter-scale pixels in comparison to kilometer-scale pixels, a special attention should be paid to the performance of inverse algorithms, especially for the atmospheric correction step.…”

Section: Introductionmentioning

confidence: 99%

Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands

Harmel

Chami

Tormos

et al. 2018

Remote Sensing of Environment

134

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correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands. Remote Sensing of Environment, Elsevier, 2018, 204, pp.308-321 A B S T R A C TRemote sensing of inland and sea waters depends on the quality of the retrieval of the water-leaving radiance from the top-of-atmosphere measurements. The water-leaving radiance can be difficult to observe due to the reflection of direct sunlight on the air-water interface (sunglint) in the direction of the satellite field of view. The viewing geometry of Sentinel-2 satellite (European Space Agency) makes it vulnerable to sunglint contamination. In this paper, an original method is proposed to correct Sentinel-2-like imagery for sunglint contamination. The sunglint contribution is first estimated from the shortwave-infrared (SWIR) part of the spectrum and then extrapolated toward the near-infrared and visible bands. The spectral variation of the sunglint signal is thus revisited for a wide spectral range (from 350 to 2500 nm). The bidirectional reflectance distribution function related to the sunglint is shown to vary by > 28% from the SWIR to the blue bands of Sentinel-2. The application of the proposed algorithm on actual Sentinel-2 data demonstrates that sunglint patterns are satisfactorily removed over the entire images whatever the altitude of the observed target. Comparison with in situ data of water-leaving radiances (AERONET-OC) showed that our proposed algorithm significantly improves the correlation between satellite and in situ data by 55% (i.e., from R 2 = 0.56 to R 2 = 0.87). In addition, the discrepancies between satellite and in situ measurements are reduced by 60%. It is also shown that the aerosol data provided by the Copernicus Atmosphere Monitoring Service (CAMS) can be safely used within the proposed algorithm to correct the Sentinel-2-like satellite data for both sunglint and atmospheric radiances. Improvements of the proposed method potentially rely on simultaneous retrievals of the aerosol optical properties. The proposed method is applicable to any satellite sensor which is able to measure in SWIR spectral bands over aquatic environments.

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“…Most monitoring schemes are based on selective sampling during summer or installation of measurement buoys [7]. Deploying these sampling strategies is labour, time and cost intensive [8]; yet, it may not allow for the detection of changes which occur on varying temporal and spatial scales [9].…”

Section: Introductionmentioning

confidence: 99%

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

et al. 2016

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Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr −1 ). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (a CDOM (440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and a CDOM (440) were modelled in optically shallow water. In deep water, SPM and a CDOM (440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m −1 ) showed an underestimation of S2-A derived a CDOM (440) (mean: 0.14 m −1 ); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m −1 vs. 0.019 m −1 ). Chlorophyll-a concentrations (~1 mg·m −3 ) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.

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Barriers to adopting satellite remote sensing for water quality management

Cited by 181 publications

References 34 publications

An Optical Classification Tool for Global Lake Waters

An Optical Classification Tool for Global Lake Waters

Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

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