Lake Baikal ice: analysis of AVHRR imagery and simulation of under-ice phytoplankton bloom

Semovski, S.V.; Mogilev, N. Yu.; Sherstyankin, P. P.

doi:10.1016/s0924-7963(00)00063-4

Cited by 23 publications

(13 citation statements)

References 6 publications

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“…According to Equation (18), the weighted correlation coefficients are respectively 0.88, 0.77, and 0.82 for bands 3, 4, and 5. This means that the downscaled images lose from 12 to 23% of their original signal quality, which is acceptable since the MODIS band signal is 4 to 5 times higher than the corresponding bands of other sensors such as Land-Sat7/ETM or CZCS [19].…”

Section: Evaluation Of Downscaling the Modis Band Signalmentioning

confidence: 99%

“…Also, many other sensors were used successfully, such as Landsat TM to retrieve Chl-a and suspended solid concentrations in lake Kasumigaura using neural network technique [9] and to develop an algorithm for assessing phycocyanin concentrations in lake Erie in order to improve the understanding of temporal and spatial dynamics of cyanobacterial blooms. SeaWiFS data were also used to establish the seasonal distribution pattern and intensity of phytoplankton and terrigenous input [17], and AHVRR to point out the behavior of main taxonomic groups of Baikal lake phytoplankton in relation to ice conditions [18], or to assess other water quality parameters [19,20]. Recently, QuickBird and MERIS data were also used to assess cyanobacterial blooms based on their specific pigment (phycocyanin) in Lake Champlain [21].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

et al. 2012

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Abstract:The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg•m −3 ), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R 2 = 0.77).However, the performance of all models decreased when Chl-a was below 50 mg•m −3 . OPEN ACCESSRemote Sens. 2012, 4 2374

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Section: Evaluation Of Downscaling the Modis Band Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

et al. 2012

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“…The state of the lake ice cover and overlying snow determine the formation of different hydrophysical fields and influences spring bloom of diatoms and primary productivity (Granin et al 1999;Semovski et al 2000;Mackay et al 2003Mackay et al , 2005. Due to the high transparency of the Baikal ice, during late winter and early spring, when the lake is still ice-covered, intense development of phytoplankton, zooplankton and benthos starts under the ice.…”

Section: Lake Baikalmentioning

confidence: 99%

“…However, as for the Caspian and Aral seas the number of observations has sharply decreased since the mid-1980s. The lack of in situ information was compensated later on by the use of satellite data, such as Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites, Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, as well as some limited ERS Synthetic Aperture Radar (SAR) images (Semovski et al 2000;Semovski and Mogilev 2003). Since December 2002, a satellite receiving station in Irkutsk is acquiring optical imagery from the MODIS sensor (Irkutsk RICC website 2005).…”

Section: Lake Baikalmentioning

confidence: 99%

Ice and Snow Cover of Continental Water Bodies from Simultaneous Radar Altimetry and Radiometry Observations

et al. 2008

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We show how the studies of ice and snow cover of continental water bodies can benefit from the synergy of more than 15 years-long simultaneous active (radar altimeter) and passive (radiometer) observations from radar altimetric satellites (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On) and how this approach can be complemented by SSM/I passive microwave data to improve spatial and temporal coverage. Five largest Eurasian continental water bodies-Caspian and Aral seas, Baikal, Ladoga and Onega lakes are selected as examples. First we provide an overview of ice regime and history of ice studies for these seas and lakes. Then a summary of the existing state of the art of ice discrimination methodology from altimetric observations and SSM/I is given. The drawbacks and benefits of each type of sensor and particularities of radiometric properties for each of the chosen water bodies are discussed. Influence of sensor footprint size, ice SSM/I stands for Special Sensor Microwave/Imager. roughness and snow cover on satellite measurements is also addressed. A step-by-step ice discrimination approach based on a combined use of the data from the four altimetric missions and SSM/I is presented, as well as validation of this approach using in situ and independent satellite data in the visible range. The potential for measurement of snow depth on ice from passive microwave observations using both altimeters and SSM/I is addressed and a qualitative comparison of in situ snow depth observations and satellitederived estimates is made.

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“…Kelly (1997) estimates that between 4% and 11% of solar radiation is able to penetrate through clear ice, providing enough energy for algal growth in spring, while as little as 5 cm of snow cover on the ice can reduce solar transmission by a factor of 50; thus, when depths of surface snow are Ͼ10 cm, reduced light penetration is limiting for Baikal diatom growth (Granin et al 1999Jewson and Granin 2000). Moreover, Semovski et al (2000) outlined how, when surface snow layers melt, ''pipkrake,'' or needle ice, is produced, which allows more effective transmission of solar radiation, from 20% to 80% (Sherstyankin 1975in Semovski et al 2000. A combination of persistent snow cover and the reduced production of wet ice in specific regions of Baikal (related to increased snow cover) act to reduce levels of light transmission through the ice, thereby preventing large population growths of A. baicalensis as monitored in other regions.…”

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confidence: 99%

Assessing the potential for developing internal diatom‐based transfer functions for Lake Baikal

Mackay

Battarbee

Flower

et al. 2003

Limnology & Oceanography

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We explore possible quantitative relationships between diatom species and environmental data for Lake Baikal using multivariate techniques. Our approach differs from published studies in other regions (on training sets and transfer functions) because (1) although only one lake is examined, we use the internal lake gradients rather than gradients among lakes and (2) the majority of the dominant diatom taxa are endemic. Canonical correspondence analysis on 93 surface sediment diatom assemblages reveals that major taxa show distinct relationships with measured environmental variables. Five significant variables were identified: snow thickness on ice (which accumulates on the frozen lake surface between January and April/May), water depth, suspended matter, annual solar radiation, and mean July temperature of surface waters. The strongest relationship is with snow thickness, which influences light levels in the water below the ice. A diatom-based inference model has been developed to predict snow thickness on the ice using weighted averaging (WA) and WA with tolerance down-weighting (WA tol ) models. Results suggest that Lake Baikal diatom assemblages in surface sediments can be used to develop a robust model for estimating snow thickness across frozen Lake Baikal (r 2 jack ϭ 0.607, RMSEP ϭ 0.138 log cm). This model has potential applications for recent paleoclimate studies in this region of continental Eurasia.

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Lake Baikal ice: analysis of AVHRR imagery and simulation of under-ice phytoplankton bloom

Cited by 23 publications

References 6 publications

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

Ice and Snow Cover of Continental Water Bodies from Simultaneous Radar Altimetry and Radiometry Observations

Assessing the potential for developing internal diatom‐based transfer functions for Lake Baikal

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