Imaging Spectrometry of Water

Dekker, A.G.; Brando, Vittorio; Anstee, Janet; Pinnel, Nicole; Kutser, Tiit; Hoogenboom, Erin J.; Peters, Steef; Pasterkamp, R.; Vos, R.J.; Olbert, C.; Malthus, Tim J. M.

doi:10.1007/0-306-47578-2_11

Cited by 17 publications

(21 citation statements)

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“…As suggested by Dekker et al [21], sensors NEΔ R rs (Δ) E were used to assess suitability to accurately retrieve water constituents. According to the low NEΔ R rs (Δ) E values OLI and MODIS R rs data were spectrally inverted to assess the concentrations of water quality parameters.…”

Section: Methodsmentioning

confidence: 99%

“…According to the low NEΔ R rs (Δ) E values OLI and MODIS R rs data were spectrally inverted to assess the concentrations of water quality parameters. The RapidEye radiometric sensitivity (NEΔ R rs (Δ) E = 0.221%) is not suitable for mapping small variations of water constituents that occur in the study area [21]. MODIS was not deemed suitable for the shallow waters analysis as due to the coarse spatial resolution the shallow waters occur mostly in the land-water mixed pixels.…”

Section: Methodsmentioning

confidence: 99%

“…Verpoorter et al [18] used Landsat imagery to produce a global inventory of lakes: it contains geographic and morphometric information for ∼117 million lakes with a surface area larger than 0.01 km 2 . Then, water quality of lakes from Landsat has been investigated worldwide; e.g., in Asia [19,20], in Europe [21–23], in North America [24,25], in Africa [26]. Retrospective analyses with Landsat imagery were performed by Dekker et al [27] for benthic cover change detection in a shallow tidal Australian lake and by Lobo et al [28] for mapping the total suspended solids of the Tapajós River (Brazil) from 1973 to 2013.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Multi-Resolution Satellite Sensors for Assessing Water Quality and Bottom Depth of Lake Garda

Giardino

Bresciani

Cazzaniga

et al. 2014

Sensors

105

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In this study we evaluate the capabilities of three satellite sensors for assessing water composition and bottom depth in Lake Garda, Italy. A consistent physics-based processing chain was applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-8 Operational Land Imager (OLI) and RapidEye. Images gathered on 10 June 2014 were corrected for the atmospheric effects with the 6SV code. The computed remote sensing reflectance (Rrs) from MODIS and OLI were converted into water quality parameters by adopting a spectral inversion procedure based on a bio-optical model calibrated with optical properties of the lake. The same spectral inversion procedure was applied to RapidEye and to OLI data to map bottom depth. In situ measurements of Rrs and of concentrations of water quality parameters collected in five locations were used to evaluate the models. The bottom depth maps from OLI and RapidEye showed similar gradients up to 7 m (r = 0.72). The results indicate that: (1) the spatial and radiometric resolutions of OLI enabled mapping water constituents and bottom properties; (2) MODIS was appropriate for assessing water quality in the pelagic areas at a coarser spatial resolution; and (3) RapidEye had the capability to retrieve bottom depth at high spatial resolution. Future work should evaluate the performance of the three sensors in different bio-optical conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Multi-Resolution Satellite Sensors for Assessing Water Quality and Bottom Depth of Lake Garda

Giardino

Bresciani

Cazzaniga

et al. 2014

Sensors

105

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“…The initial premise of watercolor remote sensing was to determine optical water quality variables such as chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient, and water-leaving radiance spectra [7]. The optical properties of water depend on many factors, e.g., suspended organic and inorganic particles and dissolved substances.…”

Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Hafeez¹,

Wong²,

Abbas³

et al. 2019

Monitoring of Marine Pollution

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Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters.

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“…In this study, the term “bloom” specifically refers to the latter case where cyanobacteria cells form surface scums or aggregate near the surface through physical movement. In such cases, reflectance in the near‐infrared (NIR) wavelengths is enhanced, and can appear similarly to those from land vegetation (i.e., red‐edge reflectance) (Dekker et al ), making it possible to quantify blooms. In other words, the term “bloom” in this study is defined in a practical sense where NIR reflectance in the 740–900 nm range is elevated due to surface scums or near‐surface high cyanobacteria concentrations.…”

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confidence: 99%

Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations

Visser

et al. 2018

Limnology & Oceanography

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Using frequent and long‐term measurements (eight times per day, 2011 to present) from a geostationary satellite sensor (Geostationary Ocean Color Imager, GOCI), this study investigates diurnal changes of cyanobacterium Microcystis aeruginosa blooms (near‐surface high concentrations or surface scums) in Taihu Lake, from which vertical migration patterns could be inferred. After proper atmospheric correction, a cyanobacterial index algorithm is used to quantify equivalent surface cyanobacterial density (σ, 0–100%) at both pixel and synoptic scales from each cloud‐free image, followed by analysis of diurnal changing patterns of σ at both scales. Three typical diurnal changing patterns are identified from all images, which show distinctive and different seasonality from the long‐term statistics. Spatial distributions of the “hotspot” regions where diurnal changes are most often observed have also been established. While the seasonality of the three patterns appears to be a result of seasonality in both temperature and light availability, large blooms only occur 1 d after major wind events. Based on several lines of evidence, we hypothesize that the diurnal changes of such observed surface bloom patterns are likely a result of vertical migration rather than horizontal dissipation/aggregation of cyanobacteria. The mean migration speeds inferred from either a simple model or a radiative transfer model (< 0.03 cm s−1 or < 1 m h−1) are consistent with those reported earlier from laboratory measurements for certain cyanobacteria colony sizes. Complete understanding of the three types of diurnal patterns and direct validation of the hypothesis, however, require further investigations from field measurements.

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Imaging Spectrometry of Water

Cited by 17 publications

References 0 publications

Evaluation of Multi-Resolution Satellite Sensors for Assessing Water Quality and Bottom Depth of Lake Garda

Evaluation of Multi-Resolution Satellite Sensors for Assessing Water Quality and Bottom Depth of Lake Garda

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations

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