Anton Korosov

Verification and Application of a Bio-optical Algorithm for Lake Michigan Using SeaWiFS: a 7-year Inter-annual Analysis

ABSTRACT In this paper we utilize 7 years of SeaWiFS satellite data to obtain seasonal and inter-annual time histories of the major water color-producing agents (CPAs), phytoplankton chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals (sm) for Lake Michigan. We first present validation of the Great Lakes specific algorithm followed by correlations of the CPAs with coincident environmental observations. Special attention is paid to the satellite observations of the extensive episodic event of sediment resuspension and calcium carbonate precipitation out of the water. We then compare the obtained time history of the CPAs spatial and temporal distributions throughout the lake to environmental observations such as air and water temperature, wind speed and direction, significant wave height, atmospheric precipitation, river runoff, and cloud and lake ice cover. Variability of the onset, duration, and spatial extent of both episodic events and seasonal phenomena are documented from the SeaWiFS time series data, and high correlations with relevant environmental driving factors are established. The relationships between the CPAs retrieved from satellite data and environmental observations are then used to speculate on the future of Lake Michigan under a set of climate change scenarios.

Semi-empirical Algorithm for the Retrieval of Ecology-Relevant Water Constituents in Various Aquatic Environments

An advanced operational semi-empirical algorithm for processing satellite remote sensing data in the visible region is described. Based on the Levenberg-Marquardt multivariate optimization procedure, the algorithm is developed for retrieving major water colour producing agents: chlorophyll-a, suspended minerals and dissolved organics. Two assurance units incorporated by the algorithm are intended to flag pixels with inaccurate atmospheric correction and specific hydro-optical properties not covered by the applied hydro-optical model. The hydro-optical model is a set of spectral cross-sections of absorption and backscattering of the colour producing agents. The combination of the optimization procedure and a replaceable hydro-optical model makes the developed algorithm not specific to a particular satellite sensor or a water body. The algorithm performance efficiency is amply illustrated for SeaWiFS, MODIS and MERIS images over a variety of water bodies.

Monitoring the spreading of the Amazon freshwater plume by MODIS, SMOS, Aquarius, and TOPAZ

A synergistic tool for studying the Amazon River plume dynamics based on a novel algorithm for deriving sea surface salinity (SSS) from MODIS reflectance data together with SSS data from the SMOS and Aquarius satellites and the TOPAZ data assimilation system is proposed. The new algorithm is based on a neural network to relate spectral remote sensing reflectance measured by MODIS with SSS measured by SMOS in the Amazon River plume. The algorithm is validated against independent in situ data and is found to be valid in the range of SSS from 29 to 35 psu, for the period of highest rates of Amazon River discharge with RMSE = 0.79 psu and r2 = 0.84. Monthly SSS fields were reconstructed from the MODIS data for late summers from 2002 to 2012 at a 10 km resolution and compared to surface currents and SSS derived from the TOPAZ reanalysis system. The two data sets reveal striking agreement, suggesting that the TOPAZ system could be used for a detailed study of the Amazon River plume dynamics. Both the position and speed of the North Brazilian Current as well as the spreading of the Amazon River plume are monitored. In particular, a recurrent mechanism was observed for the spreading of the rivers plumes, notably that the fresh water is usually advected toward the Caribbean Sea by the North Brazilian Current but get diverted into the tropical Atlantic when North Brazilian Current Rings are shed.

A new area-specific bio-optical algorithm for the Bay of Biscay and assessment of its potential for SeaWiFS and MODIS/Aqua data merging

Based on a feed-forward and error-back-propagated neural network (NN), a new bio-optical algorithm is developed for the Bay of Biscay. It is designed as a set of NNs individually dedicated to the retrieval of the phytoplankton chlorophyll (chl), and total suspended matter (tsm) from Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data. The retrieved versus in situ measured concentrations of chl and tsm correlation coefficients for chl proved to be ∼0.8 (SeaWiFS) and 0.72 (MODIS), and for tsm 0.71 (SeaWiFS) and 0.74 (MODIS). The developed NN-based bio-optical algorithms are employed to assess the compatibility of SeaWiFS and MODIS data on chl and tsm in the coastal zone of the Bay of Biscay (case 2 waters). The value of the ratio between the concentration of chl and tsm derived from the same-day SeaWiFS and MODIS data (the overflight time difference, Δt is ≤2.5 hours) has in most cases values of approximately 1, however, in specific cases it varies appreciably. These results indicate that, unlike the reportedly very successful cases of merging of SeaWiFS and MODIS data on chl in open ocean waters (case 1 waters), the merging of chl (and tsm) data from these sensors collected over case 2 waters needs to be supervised at a level of a few pixels. At the same time, when averaged over the entire coastal zone of the Bay of Biscay, the retrieved monthly mean chl and tsm concentrations from SeaWiFS and MODIS practically coincide throughout the years (2002–2004) of contemporaneous operation of these two satellite sensors. Thus, even in the case of such dynamic and optically complex case 2 waters that are inherent in the Bay of Biscay, the potentials for ocean colour data merging are very good. The merging efficiency is assessed and illustrated via documenting the spatio-temporal dynamics of bottom sediment re-suspension in the bay occurring in winter – the season of heaviest cloudiness over the bay.

Satellite-data-based study of seasonal and spatial variations of water temperature and water quality parameters in Lake Ladoga

A satellite sensor nonspecific operational advanced algorithm is developed to simultaneously retrieve the concentrations of phytoplankton chlorophyll (chl), dissolved organics (doc) and suspended minerals (sm) in turbid and strongly absorbing natural waters (i.e., case II waters). Also, a new interpolation procedure is developed and used jointly with the advanced bio-optical and standard window-split algorithms to generate from SeaWiFS and AVHRR data the time series of spatial and temporal (seasonal and interannual) variations of chl, sm, doc and water surface temperature (T S) for the period 1998-2004 in Lake Ladoga, the largest European fresh water body. Obtained for the first time, the spaceborne fields of the above variables have revealed at an unprecedented time and space resolution some intrinsic features and interdependence of thermal and biogeochemical processes in the lake. Rates of thermobar displacement from the littoral zone to the central deep water area are quantified during periods of lake warming and cooling. From spring to mid-summer, the dynamics of phytoplankton biomass spatial distribution are evidenced to follow the retraction of the cold water zone bordered by the thermobar. Importantly, along with the thermobar dynamics, the zones of the most enhanced phytoplankton concentration are concurrently governed by the lake bathymetry, and thus gradually move from south to north along the eastern coast line. Brought with fluvial input, suspended minerals and allochthonous dissolved organics are not only restricted to the zones of major river deltas but also driven northward by coastal cyclonic currents prevailing in Lake Ladoga. The obtained space data allows the interplay of the above factors to be explicitly revealed and explains the observed interannual variations in the surficial expressions of biogeochemical processes inherent in Lake Ladoga.

A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models.

Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel

The intensity of a Sentinel-1 Terrain Observation with Progressive Scans synthetic aperture radar image is disturbed by additive thermal noise, particularly in the cross-polarization channel. Although the European Space Agency provides calibrated noise vectors for noise power subtraction, residual noise contributions are significant when considering the relatively narrow backscattering distribution of the cross-polarization channel. In this paper, we investigate the characteristics of noise and propose an efficient method for noise reduction based on a three-step correction process comprised of azimuth descalloping, noise scaling and interswath power balancing, and local residual noise power compensation. The core idea is to find the optimal correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over a homogeneous region and to combine them with the scalloping gain for a reconstruction of the complete 2-D noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The performance improvement in some applications by adopting the denoising procedure shows the effectiveness of the proposed method.

Visible and Infrared Remote Sensing of the White Sea Bio-Geo-Chemistry and Hydrology

was employed to monitor the surface expressions of some biotic and veloped to reconstruct the seasonal variations of the above substances in pixels occasionally masked by cloudiness. The developed software package provided a means to obtain the series of intra-annual spatial and temporal variations of chl, sm, doc and sea surface temperature throughout the WS from SeaWiFS and AVHRR, respectively. The observed variations are controlled by (a) the dynamics of water turbidity and opacity due to seasonal variations in the content of sm and doc driven by the river discharge varying influence, and (b) thermo-hydrodynamic processes encompassing water density currents, tides, upwellings, fronts, etc.

Investigations of the Water Body Biogeochemistry and Phytoplankton Biomass Variability in Time and Space

The Bay of Biscay is a gulf of the northeast Atlantic Ocean lying along the western coast of France from Brest southwards to the Spanish border, and the northern coast of Spain in the Spanish Basque Country. Its limits are defined as a line joining Cap Ortegal (43° 46′N, 7° 52′W) to Penmarch Point (47° 48′N, 4° 22′W).

Investigations of the Primary Production Dynamics in the Atlantic and Arctic Oceans

Solidly underpinned by abounding observations, it is presently a commonly shared opinion that, as a result of climate change, there is a greater degree of warming at high latitudes than at lower altitudes, which is predicted to become worse over the twenty-first century.