Ocean chlorophyll retrieval algorithms

The fact that the presence of chlorophyll influences the optical properties which otherwise called as change in the ocean color, makes us to use the sensor that can detect the ocean color for chlorophyll evaluation. It was recognised that colorimetric estimates of chlorophyll could be most difficult in coastal waters where the interferences by yellow substances and suspended sediments would be considerable. Nevertheless in nearshore and offshore waters the colorimetric estimates of chlorophyll from the radiance data is possible. A comparison of chlorophyll retrieval algorithms is made and their applicability is evaluated. Except in specific areas like mouths of estuaries, coastal zones influenced by river runoff etc. the empirical models can help to estimate total phytoplankton pigments from ratios formed from the upwelling radiances at 440, 520, 550 and 670 nanometers. From these results it is clear that our ability to relate pigment characteristics is limited by the less quantity of good quality bio-optical data on which any model can be developed and implemented.     

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.     

Investigations of the Krasnoyarsk Reservoir waters based on the multispectral satellite data

In this work historical investigations and modern results of classification of the Krasnoyarsk Reservoir are presented. The paper presents results of studying the dynamics of phytopigments and other optically active components, using multispectral satellite data. Several approaches to interpreting satellite data for optically complex inland water bodies are offered. Based on results of historical investigations it is shown that the spatial distribution of phytoplankton in the reservoir stems back to the time of its formation. Color index in the red spectral region (CIR) is introduced. A relationship between the color index and chlorophyll concentration is investigated. The CIR, derived from the AVHRR data, has been found to be related to chlorophyll concentration. Based on MODIS data, the waters of the Krasnoyarsk Reservoir have been classified in accordance with their optical spectral variability, using the technique of unsupervised IsoData classification. An empirical relationship between multispectral MODIS data and the ground-truth measurements of chlorophyll concentration has been found.     

Spatial variability of the ocean color field in CZCS imagery

The mesoscale ocean color field has been investigated by means of a statistical approach to the analysis of Coastal Zone Color Scanner imagery. A quantitative assessment of ocean color was provided by deriving large scale, high resolution images of the diffuse attenuation coefficient at 490 nm, K(490), for several open ocean areas in the North Atlantic and North Pacific (around 30°, 40° and 60° N). Linear trends, power spectra and structure functions were determined for one-dimensional transects extracted from the images. These results can be interpreted as a composite statistical signature of the oceanic regions examined. In light of the relationship between ocean optical properties and concentration of dissolved and suspended materials in the water, the statistical parameters obtained provide an insight into the relative intensity and scales of patchiness of water constituents, and of the planktonic environment.     

Comparison of global chlorophyll concentrations using MODIS data

Chlorophyll concentrations derived from satellite borne ocean color sensors data provide an idea of the distribution of phytoplanktons across the oceans which help us in understanding the spatial and temporal dynamics of phytoplanktons. The changes in the patterns of distribution and abundance of the planktons have significant impact on the entire ecosystem and play a key role in the global carbon cycle. In this paper, we have analyzed annual and seasonal chlorophyll concentrations retrieved from MODIS data for the periods March 2000–October 2003, which reveal the spatial and seasonal distribution of chlorophyll concentrations across the global oceans. Chlorophyll concentrations anomaly indicate that chlorophyll concentrations in almost all ocean regions responded similarly.     

Enhanced Landsat and HCMM imagery for mineral exploration in contrasting areas of subtropical humid and semi-arid terrain

The role of multispectral and thermal imagery in mineral exploration is evaluated for two areas with favourable geological conditions within contrasting physical environments — one in the semi-arid low tree and shrub savannas of western Queensland and northern South Australia, the other in the humid subtropical forest zone of western Yunnan, China.For both areas Landsat imagery was used to identify structures and lithologies favourable for mineralization and locate ironstones/gossans indicative of mineralized bedrock. HCMM imagery was used alongside Landsat imagery to identify former and ephemeral drainage patterns important for the interpretation of geochemical data over covered ground in Australia. Interpretations were made of enhanced colour composites, outputs of MSS band ratios and colour rotated images generated from both NASA film products and CCTs. These were checked by field studies during which plant, soil and rock chip samples for subsequent metals analyses were collected from areas of anomalous vegetation and from barren ironstones. Some rock chip samples from Australia contained concentrations of copper or lead or small amounts of other metals. In China geobotanical anomalies over ironstones from which soil and rock samples yielded copper, lead, zinc, tin and silver suggest the presence of a major belt of potential multi-metal mineralization.     

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.     

The role of image processing in mineral exploration: Why stop at Landsat?

Digital image processing procedures can be applied to data other than Landsat. Registered images of ancillary data such as may be derived from airborne magnetic and radiometric surveys provide new insights into existing data and extend the usefulness of satellite imagery. In times of increasing costs and difficulty of exploration better information handling is at a premium. It should be remembered that the field geologist is the ultimate end-user of most geological information, and as such the data should be easily useable and interpretable by him.     

Synthesis of geophysical data with space-acquired imagery: A review

Geophysical data obtained from ground and airborne platforms have been used in the development of regional geologic models for many years. Space-acquired data and imagery have a shorter but similar history of applications. All these data may be synthesized either manually or digitally. Manual synthesis methods consist of overlaying and comparing maps, whereas digital synthesis methods consist of computer storage and analysis of registered digital data sets.A data base may include topographic, geologic, soils, aeromagnetic, gravity, radiometric, electromagnetic and geochemical data, and Landsat, Seasat, and Heat Capacity Mapping Mission (HCMM) images, all of which can be evaluated individually or compared in multiple layers (overlays).Stereographic models, useful in the correlation and interpretation of geophysical data, have been created from Landsat images by using aeromagnetic, gravity, geochemical, or topographic values to offset Landsat pixels, thus introducing parallax and permitting stereoscopic viewing.Statistical correlation has been used to determine the applicability of specific data sets to the development of geologic or exploration models. Various arithmetic functions have proven useful in developing models from such data sets.     

Multiple sensor geocoded data

The problem of integrating remotely sensed images acquired from different sensor systems is primarily associated with their registration into a common reference projection. This registration problem has to be considered not only between the image data themselves but also with other data already existing in various geographical data bases.The advent of the second generation of earth observation satellites is emphasizing this situation by providing imagery in different geometric configurations. Images will be acquired from many sensors at different spatial resolutions (LANDSAT MSS and TM, SPOT HRV MLA and PLA) on different dates and modes (panchromatic, multispectral, nadir and off-nadir).In order to provide image data in a geometric format compatible with the Canadian National Topographic System, Canada is developing a Multi-Observational Satellite Image Correction System (MOSAICS) that will provide geocoded data resampled into a common reference projection. This paper presents the characteristics of the geocoded products with examples from multiple sensors and different dates, and addresses the processing requirements of the satellite ground station. It also illustrates the use of digital processing techniques to derive digital elevation information from geocoded stereo imagery.     

Application of CZCS data to productivity and water quality studies in the Northern Adriatic Sea

The Northern Adriatic Sea is a shallow ocean basin with considerable fresh water input and presents some unique water-optical features realted to:- suspended matter input caused by rivers and resuspension of bottom sediments;- phytoplankton production because of nutrient input from rivers and bottom;- dissolved organic matter (yellow substance) production by decomposition processes of the organic material.All these features influence the spectral reflectance of the water and make the algorithms for CZCS data interpretation quite different from those used in open ocean (type I) water. Therefore, special test-site specific algorithms must be derived from in-situ measurements of water-optical parameters and the water constituents. The results from such measurements are presented together with the algorithms and the evaluated CZCS data obtained from them. It is then discussed how the experimental and evaluated satellite data can be used to evaluate standing crop and productivity.     

Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery

In this work a technique for cloud detection and classification from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region.     

Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas

Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology.     

Estimation of sediment settling velocity in estuarine and coastal waters using optical remote sensing data

A robust method has been developed for estimating sediment settling velocity (w_s) from high resolution optical remote sensing data in estuarine, coastal and harbor waters. This method estimates settling velocity as a function of the drag coefficient (C_d), Reynolds number (Re), grain size (D₅₀), specific gravity (Δ_SG) and grain shape (in terms of the Corey Shape Factor – CSF). These parameters were derived from the particulate inherent optical properties such as backscattering (b_bp), beam attenuation (c_p), suspended sediment concentration and turbidity using Landsat 8 OLI and HICO data. Preliminary results for the Gulf of Cambay in the eastern Arabian Sea and Yangtze river estuary in the East China Sea, showed that satellite-retrieved settling velocities (m?s^?1) varied from very low values in clear oceanic waters, intermediate values in coastal waters, to very high values in river plumes and sediment-laden coastal waters. The remote sensing retrievals of sediment properties and their settling velocities were generally consistent with the field and laboratory results, which indicate that the proposed methodology will have important implications in various coastal engineering, environmental and management studies.     

Prospecting in glaciated terrain-integrating airborne and Landsat MSS

The effects of a glacially enriched zone of trace elements on soils and vegetation in the Thetford Mines area of Quebec were investigated using ground information plus digital Multispectral Scanner (MSS) data from airborne and Landsat sensors. The enriched zone was developed during the last glaciation when a southeastward flowing glacier eroded and dispersed an ultrabasic outcrop that had anomalous levels of Ni, Cu, Co, Cr, Mg and Fe.The dispersal train of enriched trace elements was detectable over an area at least 70 × 15 km ‘down-ice’ from the outcrop. In this zone total Ni concentrations in the soil ranged from background levels of 10 ppm to levels in excess of 1800 ppm. The dominant tree species, $\text{Abies}$$\text{balsamea}$ (balsam fir) and $\text{Picea glauca}$ (white spruce) reflect the soil anomaly with higher concentrations of trace elements in their tissue and lower concentrations of chlorophyll.An unsupervised enhancement of Landsat imagery showed that a tonal discontinuity was caused by a vegetation segregation related to the heavy metal enrichment soils. A detailed study based on Landsat MSS data was able to establish regional patterns of chlorophyll production by certain plant species closely related to the ultrabasic dispersal train. Multi-channel airborne MSS data confirmed the Landsat soil-plant patterns.     

Temporal analysis of suspended solid in Tokyo Bay by Landsat data

The amount of suspended solid (S.S.) discharged from Tokyo metropolitan area is estimated by use of multi-temporal Landsat data from 1979 to 1980. The method to estimate S.S. was verified with sea truth data which was observed with Landsat passes covering Tokyo Bay. The radiometric information is normalized against seasonal change of sun illuminations and atmospheric conditions. Path radiance estimation methods were discussed, because its effects are very sensitive to the estimation of S.S. concentration by Landsat MSS data. Temporal changes of S.S. distribution was also interpreted.     

Latitudinal distribution of solar coronal active regions

We studied the cyclic evolution of the latitudinal distribution of solar coronal active regions based on daily images from SOHO EIT for the period 1995–2017. Fully automated software was used, which included the following steps: initial preparation of images in the data series, normalization of histograms and correction of limb brightening, segmentation of images using threshold intensity values obtained from their histograms, scanning of segmented images in heliographic coordinates and obtaining profiles of latitudinal distribution of coronal active regions for each image of the data series. From the output data, we obtained a temporary change in the latitudinal distribution profiles and the migration of activity centers on the solar disk. From the period of minimum activity to the next minimum in both hemispheres, activity centers begin to migrate from high latitudes towards the equator. At the same time, the general center of activity repeatedly changes the direction of migration. The latitudinal distribution of the so-called presence factor of coronal active regions closely resembles the magnetic butterfly diagram, which proves their direct causal relationships. Variations in the presence factor of coronal active regions are correlated with cyclic variations in the sunspot daily numbers.     

A novel method for destriping of OCM-2 data and radiometric performance analysis for improved ocean color data products

Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products.     

Long-term preservation of microbial ecosystems in permafrost.

It has been established that significant numbers (up to 10 million cells per gram of sample) of living microorganisms of various ecological and morphological groups have been preserved under permafrost conditions, at temperatures ranging from -9 to -13 degrees C and depths of up to 100 m, for thousands and sometimes millions of years. Preserved since the formation of permafrost in sand-clay sediments of the Pliocene-Quaternary period and in paleosols and peats buried among them, these cells art the only living organisms that have survived for a geologically significant period of time. The complexity of the microbial community preserved varies with the age of the permafrost. Eukaryotes are found only in Holocene sediments; while prokaryotes are found to greater ages, i.e., Pliocene and Pleistocene. The diversity of microorganisms decreases with increasing age of sediments, and as a result cocci and corynebacteria are predominant. Enzyme activity (catalase and hydrolytic enzymes) and photosynthetic pigments (chlorophyll and pheophytin have also been detected in permafrost sediments. These results permit us to outline some approaches to the search for traces of life in the permafrost of Martian sediments by borehole core sampling. It is in the deep horizons (and not on the planet surface), isolated by permafrost from the external conditions, that results similar to those obtained on Earth can be expected.     

Assessment of plant vitality detection through fluorescence and reflectance imagery

Recent studies of the vegetation fluorescence show that it can be successfully used as an intrinsic indicator of plant photosynthetic activity. With respect to the vegetation spectral reflectance, the chlorophyll (Chl) fluorescence is more specific as an observable of basic biophysical processes in the plant cells. Laser induced fluorescence is widely used in near range remote sensing, but it is not suitable for the global monitoring of vegetation. Decades of active fluorometry studies have collected useful information of leaf reaction to natural and anthropogenic stress. Still the passive fluorescence, the one that could be registered from satellite orbit has still to prove its advantage over widely used reflectance signature. The weakness of the signal and the lack of experience with passive fluorescence measurements require extensive technical, theoretical and experimental studies. New imaging fluorometres are to be designed for measuring steady state fluorescence in controlled and natural conditions.

In order to compare reflectance and steady state fluorescence sensitivity to stress impact, a set of experiments have been conducted under controlled illumination conditions in a bio-chamber, designed by the author’s team. The equipment allows plant vitality to be monitored both by passive fluorescence and spectral reflectance imaging. Different types of stress factors (heat and drought stress, acid impact) were investigated to demonstrate equipments ability in monitoring changes of fluorescence signal. Selected fluorescence images of foliage illustrate an early detection of plant dysfunction and the temporal and spatial spreading of the stress impact. Analysis shows that fluorescence imaging of green plants can be developed as a highly effective early warning remote sensing method, which could have application for an ecosystems’ monitoring along with high-spectral reflectance imagery.