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Subsumed under the category of ocean colour (OC) data fusion tools, a new approach is developed to efficiently use the merits of two OC satellite sensors differing in their spatial and spectral resolution characteristics. The tool permits to combine high spectral but lower spatial resolution optical data from one satellite sensor with higher spatial resolution but lower spectral resolution data from the other one into an image possessing simultaneously both high spectral and high spatial resolution qualities.The developed algorithm employs the artificial intelligence tool: emulated/artificial neuron networks (AANs).The developed ANN algorithm performance and efficiency are demonstrated for Lake Michigan. The fusion was effected making use of multiband data from Sentinel-2 Multispectral Instrument (MSI) and MODIS-Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. In this version MODIS-Aqua sensor is chosen as an analog of the Sentinel 3 OLCI, whose spectrometric and atmospheric corrected data are yet unavailable.The multi-sensor (MS) optical-optical fusion results have persuasively demonstrated the efficiency of the approach and its applicability to studies of natural water bodies of different optical complexity. It can be utilized in combination with any biogeochemical retrieval algorithms.In the case of retrieving water quality parameters (WQP) in optically shallow aquatic environments, the employment of the fusion tool developed is particularly promising as the bottom reflectance properties are frequently highly heterogeneous. Indeed, in such cases, remote sensing optical data acquired at simultaneously high spatial and spectral resolution are certainly more advantageous as compared to that acquired separately by two different sensors operating either at high spatial (but low spectral) or high spectral (but low spatial) resolution.For the retrieval of WQP in optically shallow waters (OSW) a special algorithm called Biooptical Retrieval Algorithm (BOREALI) - OSW was applied to study the eastern coastal zone of Lake Michigan. The application of both the OC fusion tool and our BOREALI-OSW algorithm permitted to document both interannual dynamics in WQPs as well as bottom substrate spatial heterogeneity in the target OSW area of Lake Michigan.

Comprehensive investigations of biogeochemical processes in marine ecosystems by means of remote sensing become significantly more efficient provided many satellite images from various sources are synergistically employed.

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.

With the exception of a few areas, Lake Michigan (LM) is an oligotrophic clear water body. It is predominantly in its littoral zone where ecology-relevant processes unfold due to a variety of natural and anthropogenic forcings arising from the watershed. However, the bottom influence there is strong enough to contaminate the at-satellite signal, thus impeding the remote sensing of water quality parameters within the coastal zone.A new bio-optical retrieval algorithm, based on a forward radiation transfer model, LM specific hydro-optical model and the multivariate optimization technique is developed for operational retrieval from satellite data of water quality parameters in lakes optically shallow areas. Application of the developed operational tool to processing MODIS-Aqua data (matching up the location and timing of in situ CPA and radiometric measurements) has convincingly shown its advantage over the OC4 performance in lacustrine optically shallow waters at all sampling stations.

A new operational non-satellite-specific algorithm for the simultaneous retrieval from satellite data of phytoplankton chlorophyll content (chl), suspended minerals (sm), and dissolved organics (doc) in both clear and turbid waters is presented. It contains an array of neural networks providing input for the Levenberg–Marquardt multivariate optimization procedure as the final retrieval tool. With a given accuracy threshold, the developed algorithm is sufficiently robust for data with noise up to 15% for certain hydro-optical conditions. To avoid inadequate retrieval results, the algorithm identifies and eventually discards the pixels with inadequate atmospheric correction and/or water optical properties incompatible with the applied hydro-optical model. The validity of the developed algorithm was tested for Lake Michigan. Two dedicated field campaigns in the vicinity of the Kalamazoo River mouth have been conducted concurrently or quasi-concurrently with SeaWiFS and MODIS overpasses. In addition, some archival shipborne measurements of mostly chl and occasionally sm and doc were employed to validate the facility of the algorithm. Notwithstanding the aforementioned shipborne data limitations, the conducted comparison of the ground truth and retrieved data on the water quality parameters in Lake Michigan is strongly indicative of the algorithm's operational efficiency.

The development of water quality retrieval algorithms is discussed in terms of causal dependence of the upwelling spectral radiance upon the water composition. Unlike clear marine/oceanic waters, for which linear regression retrieval relationships are valid, inland and coastal zone water masses with a high degree of optical complexity necessitate the development of more subtle retrieval approaches. At the basis of such approaches are models considering the conjoint optical impact of several co-existing aquatic components on the water leaving radiance spectral distribution. Such a model for Lake Ladoga is described. Monte Carlo simulations have been performed to analyse the spectral and angular variations of the upwelling radiance scattered by the water column into the atmosphere. Optimal conditions for conducting water quality remote sensing in case of natural waters of various optical complexity are explored and relevant recommendations are formulated.

The challenges to society and the impact of harmful algal blooms (HABs) on society justify intensive study and comprehensive monitoring of such events. When designing a monitoring strategy, several goals might be pursued, including early warning. Because HAB events are generally spatially extensive and heterogeneous as well as dynamic, satellite Earth observation is considered an important component of a monitoring system, provided that remote-sensing techniques are suited to this end.

The interaction of the solar and heat radiation with the atmosphere and the earth’s surface is the subject of this book. It is useful not only for students and professors but for wide circle of scientists involved in environmental studies. The book contains descriptions of computer studying programs concerning different topics of courses on the remote sensing and radiative transfer. Thus it is not a textbook with a methodically presented subject of courses, but it includes only the basic ground for the comprehension of key topics of courses and provides the accomplishment of practical works using specially elaborated computer programs. The certain eclecticism of the book is explained with these circumstances. We need to point out that practical works have been elaborated by authors independently that influences the style of different chapters and design of computer codes. The first chapter is mutual, Chaps. 2–9 is written by Anatoly Kuznetsov and Olga Seroukhova, part of the Chaps. 9, 11 and 12 – by IrinaMelnikova, and Chaps. 10, 13–15 – by Alexander Vasilyev, and Chaps. 16–18 by Dmitry Pozdnyakov. Themes of practical works reflect main sections of mentioned courses of lectures. The packet of computer programs is added to this book for the calculation of solar and heat radiation characteristics on the base of initial parameters of the atmosphere and surface (direct problems) and for retrieval optical parameters of the atmosphere and surface from radiation data (observed or simulated). Programs provide the dialog with the user and include graphics of results in some cases. For accomplishment of the practical work the student has to prepare initial data, to fulfill calculations with the corresponding program, to plot demanded graphics of calculated values dependencies on initial parameters and prepare the report with the

Large scale limnological applications of active optical remote sensing techniques are described. An airborne Nd:YAG laser with frequency doubling and tripling facilities is used. Algorithms for inferring concentrations in phytoplankton chlorophyll (CHL) and dissolved organic matter (DOM) are developed for inland water bodies. Through the analysis of specific features of retrieved 2-D fields of CHL and DOM, it is possible to characterize the surficial manifestations of various fundamental thermohydrodynamic phenomena in lakes and water storage reservoirs, such as upwellings, cores, stream patterns, etc.

This chapter presents recent advances in remote sensing of the White Sea from satellite Earth observation (EO) sensors. Section 6.1 presents the methods for quantitative remote sensing of water quality in marine environments such as the White Sea. Section 6.2 describes the patterns of thermo-hydrodynamic processes and fields derived from the NOAA Advanced Very-High Resolution Radiometer (AVHRR) data. Section 6.3 discusses remote sensing of the White Sea using active (radar) and passive microwave sensors. Finally, Section 6.4 presents studies of the White Sea ice cover using passive microwave sensors, which are used to characterize the spatial variability of sea ice over the past two decades.

Some field measurements of the downwelling solar irradiance above the water surface and the upwelling subsurface solar radiance as well as in situ determinations of phytoplanktonic chlorophyll, total suspended sediments and dissolved organic matter concentrations were performed in Saginaw Bay, Lake Huron, during July and August 1998. The radiometric experiments were carried out at the wavelengths specific for SeaWiFS (412, 443, 490, 510, 555, 665 nm) and accompanied by the observations of weather conditions (wind force and direction, wave height, cloudiness nature and degree) and the Sun elevation. The task of the present study was to solve the forward and inverse problems: to calculate the upwelling radiance using hydrooptical model and the analytical expression for the volume reflectance coefficient, to compare it with in situ data and to retrieve the concentrations of the optically active components (OAC). In the absence of the hydrooptical model for Saginaw Bay forward modeling was conducted using hydrooptical models of other temperate lakes. The retrievals of chlorophyll, suspended minerals (SM) and dissolved organic carbon (DOC) were calculated with the help of the multivariate optimization technique (MOT) and neural network approach.

Development of water quality retrieval algorithms is discussed in terms of causal dependence of the upwelling spectral radiance upon the water composition. Unlike clean marine/oceanic waters for which linear regression retrieval relationships are valid, inland and coastal zone water masses with high degree of optical complexity necessitate the development of nonparametric retrieval approaches. At the basis of these techniques are models considering the optical competitiveness of several coexisting aquatic components. Such models for Lakes Ladoga and Ontario are described and compared. Monte Carlo simulations have been performed to analyze the spectral and angular variations of the upwelling radiance scattered by the water column out into the atmosphere. Analysis of optical conditions for running remote sounding natural waters of various optical complexity is carried out. Relevant recommendations are formulated.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Based on a previously developed hydro-optical model and models simulating solar light absorption, elastic scattering, water Raman scattering, phytoplankton chlorophyll and dissolved organics fluorescence, as well as the light reflection at the bottom and the incident radiation spectral distribution, numerical simulations have been conducted to attain an adequate insight into water color formation. On this foundation, the authors assess the impact of the above mechanisms of light interactions in natural aquatic media on the water-leaving radiance spectral composition. The authors develop an approach for the simultaneous retrieval of both the concentration vector of major optically active components coexisting in natural water bodies and the bottom depth given the bottom spectral albedo.

Based on the previously developed hydrooptical models and combination of the Levenberg-Marquardt multivariate optimization method and neural network emulation technique, a fast operating tool has been developed to process visible satellite imagery taken over turbid waters. Assessed is the efficiency of the developed tool under conditions of noise contaminated input data. Determined are the numbers of sensor's spectral channels in the visible and the ranges of concentrations of major water quality constituents assuring the admissible errors of retrieval results. The processed MODIS image of Lake Michigan given herein exemplifies the feasibility/efficiency of the developed tool.