In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

Kotta, Jonne; Remm, Kalle; Vahtmäe, Ele; Kutser, Tiit; Orav‐Kotta, Helen

doi:10.1117/1.jrs.8.083634

Cited by 25 publications

(31 citation statements)

References 42 publications

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“…This is reasonable as benthic vegetation has high reflectance in NIR part of spectrum [43,44] and there is a decrease in water absorbance at 810 nm making the bottom signal detectable in this spectral region. All reflectance measurements of this study were carried out in optically deep waters with no bottom contribution.…”

Section: Discussionmentioning

confidence: 79%

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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Section: Discussionmentioning

confidence: 79%

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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“…The substrate's reflectance spectra collected over the years from different parts of the Baltic Sea were taken from our spectral library presented in Ref. 58. The same substrate's spectra were also used for the evaluation of the performance of WCC.…”

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confidence: 99%

How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters?

Vahtmäe

Paavel

Kutser

2020

J. Appl. Rem. Sens.

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The Baltic Sea represents an optically complex case 2 water type, where high concentrations of water column constituents limit acquisition of benthic information. Different preprocessing steps were applied to the hyperspectral compact airborne spectrographic imager (CASI) image to extract as much useful benthic information as possible. Atmospheric correction, minimum noise fraction transform, and sun-glint correction were performed to acquire water surface reflectance data. Additionally, a water column removal procedure was applied to acquire bottom reflectance data. Although retrieved CASI water surface reflectance spectra generally matched the magnitudes and shapes of in situ measured spectra, then the applied water column correction algorithm did not yield accurate bottom reflectance spectra. Therefore, both benthic habitat and bathymetry maps were retrieved from the CASI sea surface image data set. An image-based supervised classification method produced a good quality benthic habitat map from the shallow Pakri study area (depth < 3.0 m) with an overall accuracy of 80%. A site-specific algorithm was developed for the bathymetry retrieval utilizing a green-yellow CASI band ratio. Validation of the bathymetry map for depths shallower than 4.0 m revealed an R 2 value of 0.88 and a root-mean-square error of 0.32 m. The assessment of the benthic substrate detectability limits in the Baltic Sea revealed that, at the wavelength of deepest light penetration (near 570 nm), the depth restriction for CASI benthic substrate detection was 7.6 m for sand, 5.0 m for green macroalgae, 3.0 m for higher order vegetation, and 3.1 m for brown macroalgae. The depth limit to which bathymetric mapping is practical in our study site was estimated to be around 3.5 to 4.0 m. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…The basic mechanism behind this relationship is that the primary pigment in plants (i.e., chlorophyll) absorbs light the most in the blue regions of the visible light spectrum, and therefore, areas with higher plant cover are characterized by lower reflectances at this spectral range (e.g., Anderson & Barrett, ; Haxo & Blinks, ). Brown and red algae are known to have clear reflectance peaks between 600 and 650 nm (Kotta et al., ). However, the Sentinel‐A satellite does not have appropriate detection wavelength bands to separate such reflectance peaks.…”

Section: Discussionmentioning

confidence: 99%

“…and 650 nm (Kotta et al, 2014). However, the Sentinel-A satellite does not have appropriate detection wavelength bands to separate such reflectance peaks.…”

Section: Discussionmentioning

confidence: 99%

Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula

Kotta

Valdivia

Kutser

et al. 2018

Ecology and Evolution

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Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite‐based remote sensing, when used jointly with in situ ground‐truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel‐based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground‐truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.

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In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability

Cited by 25 publications

References 42 publications

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

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

How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters?

Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula

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