A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes

Kutser, Tiit; Herlevi, Antti; Kallio, Kari; Arst, Helgi

doi:10.1016/s0048-9697(00)00682-3

Cited by 79 publications

(35 citation statements)

References 21 publications

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“…and normalization bands at >600 nm (Kallio et al, 2001 The prominence of band-ratio algorithms for the individual retrieval of CHL in case 2 533 waters reported in this study, warrants however a note of caution. It has been suggested 534 that changes on phytoplankton assemblages, as due to climate change, may shift 535 phytoplankton composition in response to altered environmental forcing (e.g.…”

mentioning

confidence: 63%

Review of constituent retrieval in optically deep and complex waters from satellite imagery

Odermatt

Gitelson

Brando

et al. 2012

Remote Sensing of Environment

411

235

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We provide a comprehensive overview of water constituent retrieval algorithms and underlying definitions and models for optically deep and complex (i.e. case 2) waters using earth observation data. The performance of constituent retrieval algorithms is assessed based on matchup validation experiments published between January 2006 and May 2011. Validation practices range from singular vicarious calibration experiments to comparisons using extensive in situ time series. Band arithmetic and spectral inversion algorithms for all water types are classified using a method based scheme that supports the interpretation of algorithm validity ranges. Based on these ranges we discuss groups of similar algorithms in view of their strengths and weaknesses. Such quantitative literature analysis reveals clear application boundaries. With regard to chlorophyll retrieval, validation of blue-green band ratios in coastal waters is limited to oligotrophic, predominantly ocean waters, while red-NIR ratios apply only at more than 10 mg/m3. Spectral inversion techniques -although not validated to the same extent -are necessary to cover all other conditions. Suspended matter retrieval is the least critical, as long as the wavelengths used in empirical models are increased with concentrations. The retrieval of dissolved organic matter however remains relatively inaccurate and inconsistent, with large differences in the accuracy of comparable methods in similar validation experiments. We conclude that substantial progress has been made in understanding and improving retrieval of constituents in optically deep and complex waters, enabling specific solutions to almost any type of optically complex water. Further validation and intercomparison of spectral inversion procedures are however needed to learn if solutions with a larger validity range are feasible. Abstract 14We provide a comprehensive overview of water constituent retrieval algorithms and 15 underlying definitions and models for optically deep and complex (i.e. case 2) waters 16 using earth observation data. The performance of constituent retrieval algorithms is 17 assessed based on matchup validation experiments published between January 2006 and 18 May 2011. Validation practices range from singular vicarious calibration experiments to 19 comparisons using extensive in situ time series. Band arithmetic and spectral inversion 20 algorithms for all water types are classified using a method based scheme that supports 21 the interpretation of algorithm validity ranges. Based on these ranges we discuss groups 22 of similar algorithms in view of their strengths and weaknesses. Such quantitative 23 literature analysis reveals clear application boundaries. With regard to chlorophyll 24 retrieval, validation of blue-green band ratios in coastal waters is limited to 25 oligotrophic, predominantly ocean waters, while red-NIR ratios apply only at more than 26 10 mg/m 3 . Spectral inversion techniques -although not validated to the same extent -27 are necessary to cover all other...

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mentioning

confidence: 63%

Review of constituent retrieval in optically deep and complex waters from satellite imagery

Odermatt

Gitelson

Brando

et al. 2012

Remote Sensing of Environment

411

235

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“…Kutser et al (2001) described the interpretation of remotely sensed data from humic lakes as "practically impossible." Atmospheric effects (reflectance from aerosols) may contribute more to the signal received by airborne or satellite sensors than light reflected from humic lakes, and correction of reflectance spectra to remove this atmospheric interference is difficult.…”

Section: Discussionmentioning

confidence: 99%

Influence of Chlorophyll and Colored Dissolved Organic Matter (CDOM) on Lake Reflectance Spectra: Implications for Measuring Lake Properties by Remote Sensing

Menken

Brezonik

Bauer

2006

Lake and Reservoir Management

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Menken, K., P. Brezonik and M. Bauer. 2006. Influence of chlorophyll and colored dissolved organic matter (CDOM) on lake reflectance spectra: Implications for measuring lake properties by remote sensing. Lake and Reserv. Manage. 22(3):179-190.Light reflected from lake surfaces can convey much information about water quality, especially algal abundance, humic content, turbidity and suspended solids. Light reflectance from lakes is complicated, and detailed spectra are needed for analysis of controlling factors. We obtained detailed reflectance spectra from the water surfaces of 15 lakes in east-central Minnesota and found patterns related to chlorophyll a (chl a), turbidity and humic matter (colored dissolved organic matter, CDOM). Increasing chl a and turbidity generally resulted in higher reflectance across the visible and near-infrared spectrum. Increasing CDOM led to low reflectance, especially below ~500 nm. Spectra of lakes with high chl a were distinguishable from those of lakes low in chl a, and lakes with low or high CDOM had readily distinguishable spectra. Several optical characteristics of lake water can be estimated from reflectance intensities measured over narrow wavelength bands. The ratio of reflectance at 700 nm to that at 670 nm was the best predictor of chl a over a wide range of conditions, including high turbidity and CDOM. Several relationships involving reflectance at 412, 443, 488, and 551 nm, the wavelengths used to calculate oceanic chl a from MODIS satellite data, also yielded a high R 2 . The ratio of reflectance at 670 nm to 571 nm provided the best estimates of humic color despite the low absorbance of CDOM at these wavelengths. Relationships involving reflectance for all 15 lakes in the range 400-500 nm, where CDOM absorbs light, had low r 2 values; none was high enough for reliable estimates of lake color. For 10 lakes with low to medium chl a levels (≤10 mg m -3 ), regressions involving 412 and 443 nm yielded moderately good relationships. Airborne and satellite remote sensing thus might be used to identify lakes high in CDOM, and may provide reasonable estimates of humic color in lakes with low chl a levels.

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“…For example, single band algorithms, band ratios, more sophisticated colour indices or analytical methods for retrieving three or more water properties simultaneously have been used [29][30][31][32][33]. We chose to use peak height algorithms as the measured reflectance had only two peaks and very low signal at other wavelengths.…”

Section: Remote Sensing Algorithmsmentioning

confidence: 99%

Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

et al. 2016

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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 (R 2 = 0.77), total suspended matter (R 2 = 0.70), and suspended particulate organic matter (R 2 = 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.

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A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes

Cited by 79 publications

References 21 publications

Review of constituent retrieval in optically deep and complex waters from satellite imagery

Review of constituent retrieval in optically deep and complex waters from satellite imagery

Influence of Chlorophyll and Colored Dissolved Organic Matter (CDOM) on Lake Reflectance Spectra: Implications for Measuring Lake Properties by Remote Sensing

Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

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