SeaWiFS data analysis and match-ups with in situ chlorophyll concentrations in Danish waters

For the year 1999 all Sea viewing Wide Field of view Sensor (SeaWiFS) scenes of the Danish waters from the North Sea to the Baltic Sea were browsed, and a total of 47 SeaWiFS scenes with reasonably low cloud cover and, therefore, potential in situ match-ups were found and processed. The in situ data used as match-ups were collected on routine monitoring cruises by Danish and Swedish environmental authorities. A few stations in the North Sea, Skagerak and the western Baltic Sea were sampled, while most stations were located in Kattegat and the inner Danish waters. A turbid water SeaWiFS atmospheric correction algorithm was applied, since the standard SeaWiFS algorithm for chlorophyll-a (CHL) has been shown to be fairly inaccurate in turbid coastal waters. This is due to both inaccurate atmospheric and to relatively high and variable abundance of yellow substance. The application of the turbid atmospheric correction substantially improved the SeaWiFS CHL estimates. Regressions between SeaWiFS estimates using the OC2 and OC4 algorithms used in the SeaDAS software (versions 3.3 and 4.0, respectively) and in situ CHL values were made as well, and regression with a number of other possible reflectance ratios with SeaWiFS channels. The best correlation was found to be R²=0.54 using a double-ratio algorithm using both R510/R555 and R443/R670, while the OC4v4 had the second best correlation of R²=0.39. Among other single ratios, the R510/R555 had the highest correlation with CHL, which was expected since this is also the ratio that OC4v4 most often switches to in the waters investigated here. The range of CHL concentrations in this study was rather limited (all but three points from 0.5–3?mg?m^?3) so there is a need for inclusion of more data to expand the concentration range. This should be possible using also data from 2000, 2001 and onwards and, hereafter, a more ‘stable’ empirical algorithm can be derived for the Danish waters.     

Sampling biases in MODIS and SeaWiFS ocean chlorophyll data

Although modern ocean color sensors, such as MODIS and SeaWiFS, are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a “truth field”, which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25° longitude by 0.67° latitude.The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and > 5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (< 3%) in the mid-latitudes (between − 40° and 40°). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid.High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is < 75° are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll.The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.Persistent clouds, characteristic in the North Pacific, also produced overestimates, again by selectively sampling only the high growth periods. In contrast, areas characterized by thick aerosols showed chlorophyll underestimates to nearly − 30% in basin monthly means. This was the result of selective sampling in lower aerosol thickness periods, which corresponded with lower phytoplankton growth periods.A combination of MODIS and SeaWiFS sampling was most effective at reducing mid-latitude biases due to inter-orbit gaps, sun glint, and sensor tilt changes. But these biases were low using a single sensor, suggesting multiple sensors had little effect in reducing global and regional monthly and annual mean biases.Ocean color data are an invaluable source of information about global biological processes. However, these results suggest that sampling errors need to be considered in applications involving global and regional mean chlorophyll biomasses as well as seasonal variability and regional trend analysis.     

SeaWiFS validation in European coastal waters using optical and bio-geochemical measurements

The National Aeronautics & Space Administration (NASA) Sea viewing Wide Field of view Sensor (SeaWiFS) began operational measurement of ocean colour in September 1997. Upgrades to the SeaWiFS data processing system (SeaDAS) have occurred frequently and the effects of these revisions on the remotely sensed estimates of chlorophyll-a concentration (chl-a) have been significant. Measurements of chl-a from research work in the Bay of Biscay and Gulf of Cadiz during 1998–1999 are used to validate the SeaWiFS chl-a product generated using the current version of SeaDAS (version 4.1). The validation data cover coastal and offshore waters, including those dominated by inorganic suspended sediment, and an intense dinoflagellate bloom where shipboard chl-a measurements exceeded 50?mg?m^?3. The standard SeaWiFS chlorophyll algorithm (OC4v4) generally performed well, but significantly over-estimated chl-a where inorganic suspended sediment was present. The algorithm is only applicable to chl-a values up to 64?mg?m^?3, which was less than chl-a at the centre of the bloom. A novel algorithm for chl-a, which first estimates the inherent optical properties of the water, was applied to the SeaWiFS measurements but failed on over 90% of the pixels, perhaps because SeaWiFS is under-estimating water reflectance at the extreme blue end of the visible spectrum.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

SeaWiFS data interpretation in a coastal area in the Bay of Biscay

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument, launched onboard the Orbimage 2 satellite, is composed of an optical scanner with eight channels that are used to interpret the ocean colour, and has been operational since September 1997. SeaWiFS data were received by the Dundee Satellite Receiving Station and processed by the Plymouth Marine Laboratory with a slight time-lag. In situ measurements of reflectance, salinity, seston and chlorophyll a were analysed during the Biomet surveys to gain a better knowledge of the dynamics of the Gironde turbid plume during this period. The results showed logarithmic relationships between the SeaWiFS normalized water-leaving radiances (n L w ) at 490 and 555 nm (n L w (490) and n L w (555)), and the suspended particle matter concentrations. The relationship between the n L w (555) radiances and these concentrations is used to map the coastal terrigenous turbidities. But the chlorophyll a concentrations calculated from SeaWiFS are overestimated in the turbid waters. The n L w (490)/n L w (555) ratio decreases with increasing turbidity and with increasing chlorophyll a concentration. To distinguish the chlorophyll a in turbid waters, the n L w (490) radiances are calculated from n L w (555) considering only the effect of terrigenous turbidity. Then, the n L w (490) SeaWiFS image is compared to the calculated n L w (490), to reveal the 'negative' areas caused by the chlorophyll a and yellow substance absorption.     

Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS

A technique is presented for estimating suspended sediment concentrations of turbid coastal waters with remotely sensed multi-spectral data. The method improves upon many standard techniques, since it incorporates analyses of multiple wavelength bands (four for Sea-viewing Wide Field of view Sensor (SeaWiFS)) and a nonlinear calibration, which produce highly accurate results (expected errors are approximately ±10%). Further, potential errors produced by erroneous atmospheric calibration in excessively turbid waters and influences of dissolved organic materials, chlorophyll pigments and atmospheric aerosols are limited by a dark pixel subtraction and removal of the violet to blue wavelength bands. Results are presented for the Santa Barbara Channel, California where suspended sediment concentrations ranged from 0–200+ mg?l^?1 (±20?mg?l^?1) immediately after large river runoff events. The largest plumes were observed 10–30?km off the coast and occurred immediately following large El Niño winter floods.     

Mapping coloured dissolved organic matter concentration in coastal waters

Optical properties of the Baltic Sea are dominated by coloured dissolved organic matter (CDOM). High concentration of CDOM is probably one of the reasons why standard chlorophyll-retrieval algorithms fail badly in the Baltic Sea. Our aim was to test can CDOM be mapped by remote sensing instruments in coastal waters of relatively CDOM-rich environments like the Baltic Sea. The results show that sensors with high radiometric resolution, such as Advanced Land Imager (ALI), can be used for mapping CDOM in a wide concentration range. The ALI image also showed that optical properties of coastal waters are extremely variable. CDOM concentration may vary 4–5-fold in two adjacent 30 m pixels. This indicates a need for relatively high spatial resolution (30 m or less) remote sensing data in monitoring coastal environments.     

Use of satellite images of chlorophyll fluorescence to monitor the spring bloom in coastal waters

This article demonstrates a successful application of fluorescence line height (FLH) images from the Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagers and provides a strong argument for making more widespread use of FLH in monitoring surface phytoplankton in coastal waters. In the present example, MERIS and MODIS FLH images show the start of the spring bloom in coastal waters of the Strait of Georgia in British Columbia, Canada. The images clearly show a recurring pattern in five of the eight years from 2003 to 2010 covered by MERIS, which suggests seeding of the early spring bloom from narrow coastal inlets. Such seeding has been suggested before, but never observed. FLH images show the blooms more clearly than images of surface chlorophyll based on the ratios of water-leaving radiances in the blue and green spectral range (440–560 nm). FLH images used here have been derived with no atmospheric correction. Alternative products based on the blue/green ratio require atmospheric correction, which is difficult in coastal areas. Such products also tend to be more significantly confused with other constituents of coastal waters.     

A semi-supervised fuzzy clustering algorithm applied to gene expression data

Over the last decade there has been an increasing interest in semi-supervised clustering. Several studies have suggested that even a small amount of supervised information can significantly improve the results of unsupervised learning. One popular method of incorporating partial supervised information is through pair-wise constraints indicating whether a certain pair of patterns should belong to the same (Must-link) or different (Dont-link) clusters. In this study we propose a novel semi-supervised fuzzy clustering algorithm (SSFCA). The supervised information is incorporated via a method quantifying Must-link and/or Dont-link constraints. Additionally, we present an extension of SSFCA that allows the algorithm to automatically detect the number of clusters in the data. We apply SSFCA to the intrinsic problem of gene expression profiles clustering. The advantageous properties of fuzzy logic, inherited to SSFCA, allow genes to belong to more than one group, revealing this way more profound information concerning their multiple functioning roles. Finally, we investigate the incorporation of prior biological knowledge arriving from Gene Ontology in the process of selecting pair-wise constraints. Simulations on artificial and real life datasets proved that the proposed SSFCA significantly outperformed other standard and semi-supervised clustering methods.     

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

应用于大数据的Trie树排序算法

针对在数据量动态增加的场景下现有的排序算法管理数据导致算法性能大大降低的问题,提出一种16-bit Trie树排序算法.借助邻居节点上存储的链节点指针完成排序,它不仅可以边构建边排序,且引入动态数组可以提高该算法的空间效率.仿真结果表明,传统Trie树支持数据动态更新,但通过遍历Trie树的方式完成排序耗时较多,快速排...     

流行学习算法应用于基因芯片数据分析

基因芯片是近年发展起来的生物技术,其数据典型特征是基因数多而样本少,因此必须及时采取有效方法来处理这些以指数级增长的数据.流行学习算法在高维数据方面有着广泛应用,但在基因芯片数据分析的应用还比较少.为了能得到在基因芯片数据分析中更好的处理方法,文章应用三种非线性降维提取海量基因芯片数据的特征,然后利用支持向量机作为分类器,判断样本的类属.实验结果表明,通过LLE特征提取之后,能获得与原基因芯片更为接近的成分,类属判断结果更为准确,为基因芯片数据分析提供了一定的科学指导.     

Validation of SeaWiFS chlorophyll a concentrations in the Southern Ocean: A revisit

Surface chlorophyll a concentrations (C_a, mg m^− 3) in the Southern Ocean estimated from SeaWiFS satellite data have been reported in the literature to be significantly lower than those measured from in situ water samples using fluorometric methods. However, we found that high-resolution (∼ 1 km²/pixel) daily SeaWiFS C_a (C_a^SWF) data (SeaDAS4.8, OC4v4 algorithm) was an accurate measure of in situ C_a during January-February of 1998-2002 if concurrent in situ data measured by HPLC (C_a^HPLC) instead of fluorometric (C_a^Fluor) measurements were used as ground truth. Our analyses indicate that C_a^Fluor is 2.48 ± 2.23 (n = 647) times greater than C_a^HPLC between 0.05 and 1.5 mg m^− 3 and that the percentage overestimation of in situ C_a by fluorometric measurements increases with decreasing concentrations. The ratio of C_a^SWF/C_a^HPLC is 1.12 ± 0.91 (n = 96), whereas the ratio of C_a^SWF/C_a^Fluor is 0.55 ± 0.63 (n = 307). Furthermore, there is no significant bias in C_a^SWF (12% and − 0.07 in linear and log-transformed C_a, respectively) when C_a^HPLC is used as ground truth instead of C_a^Fluor. The high C_a^Fluor/C_a^HPLC ratio may be attributed to the relatively low concentrations of chlorophyll b (C_b/C_a = 0.023 ± 0.034, n = 482) and relatively high concentrations of chlorophyll c (C_c/C_a = 0.25 ± 0.59, n = 482) in the phytoplankton pigment composition when compared to values from other regions. Because more than 90% of the waters in the study area, as well as in the entire Southern Ocean (south of 60° S), have C_a^SWF between 0.05 and 1.5 mg m^− 3, we consider that the SeaWiFS performance of C_a retrieval is satisfactory and for this C_a range there is no need to further develop a “regional” bio-optical algorithm to account for the previous SeaWiFS “underestimation”.     

Continuous measurements of chlorophyll-a concentration in the Pacific Ocean by shipborne laser fluorometer and radiometer: Comparison with SeaWiFS data

The results of comparisons of chlorphyll-a concentrations, measured using different methods in various regions of the Pacific, are presented. The measurements were made simultaneously from a moving ship using both a laser fluorometer and a spectrometer for measurements of the spectral distribution of the upwelling radiation. Two-band and multispectral algorithms were used to derive the pigment concentration from the upwelling radiation. The chlorophyll a concentrations measured by the shipborne instruments are compared with data obtained by SeaWiFS (the Sea-viewing Wide Field-of-View Sensor).     

Identification of suspended sediment in coastal waters using airborne thematic mapper data

Remotely sensed data were collected using an airborne Daedalus thematic mapper, flown at 4000 m, over Swansea Bay, northern Bristol Channel, U.K. The area is one of high tidal range and wave activity. Fifty-eight surface-water/sediment samples were collected from three vessels as concurrent ‘sea-truth’ data. Samples were analysed for suspended sediment concentration (SSC)

Regression equations are developed for SSC using data in the 450-520 nm (channel 2), 520-600 nm (channel 3), 605-625 nm (channel 4) and 630-690 nm (channel 5) spectral bands

Localized flow patterns are visible in the imagery and are identified using suspended material as the passive tracer. Such patterns cannot be identified in temporally averaged data collected from research vessels

It is concluded that airborne remote sensing can contribute to the understanding of the movement of water and sediments on continental shelves, when used in association with conventional oceanographic sampling techniques.     

Airborne remote sensing of chlorophyll content under cloudy sky as applied to the tropical waters in the Gulf of Guinea

A special radiometer has been constructed to perform aerial measurements of ocean color in order to obtain the chlorophyll content of ocean water. This radiometer measures the spectral albedo at four wavelengths (466, 525, 550 and 600 nm) and was used during fourteen flights over the Gulf of Guinea in June and July of 1975. All measurements were performed from 150 m altitude. The acquisition of measurements independent of cloud cover was made possible by the calculation of differences between albedos at different wavelengths. Differences between albedos were mapped after each flight, and showed the detailed structure of a thermal front due to coastal upwellings. A theoretical interpretation, based on computations of radiative transfer in the ocean, shows that the difference of albedos at 466 and 525 nm is very sensitive to chlorophyll content, whereas the difference of albedos at 550 and 600 nm is sensitive to the presence of light scattering particles.     

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover, it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically.     

Parameterization of the chlorophyll a-specific in vivo light absorption coefficient covering estuarine,coastal and oceanic waters

We evaluated models predicting the spectral chlorophyll-a (Chl a)-specific absorption coefficient (a* _ph (λ)) from Chl a concentration [Chl a] on the basis of 465 phytoplankton absorption spectra collected in estuarine, coastal and oceanic waters. A power model on ln-transformed data provided the best model fit compared to a power model on non-transformed data previously applied to parameterize the relationship between a* _ph (λ) and [Chl a]. The variation in a* _ph (λ) was parameterized over four orders of magnitude in [Chl a] (0.01-100 mg Chl a m^?3) producing a 13-fold range in a* _ph (0.19 to 0.015 m² mg^?1 Chl a) at 440 nm, the peak absorption of Chl a in the blue part of the spectrum. The variations in the modelled a* _ph spectra were within realistic predictions of a* _ph (λ) and the model satisfactorily reproduced the spectral flattening with increasing [Chl a]. The parameterization of a* _ph (λ) confirmed the indirect dependency of a* _ph (λ) on [Chl a] through co-variations between [Chl a] with pigment packaging and pigment composition. Although pigment packaging determined the spectral flattening, analysis of absorption ratios revealed a systematic change in pigment composition with profound influence on the variability of a* _ph in the 440 to 495 nm region. Modelled spectra deviated by approximately 20% from the measured spectra on average and model accuracy was independent of [Chl a]. Although the model cannot fully replace spectral measurements of phytoplankton absorption, it does permit realistic reconstructions of a* _ph (λ) from simple measurements of [Chl a] sampled in estuarine, coastal and oceanic waters.     

The genetic algorithm applied to haplotype data at the LDL receptor locus

Conventional statistical methods based upon single restriction fragment length polymorphisms often prove inadequate in studies of genetic variation. Cladistic analysis has been suggested as an alternative, but requires basic assumptions that usually cannot be met. We wanted to test whether it could be a workable approach to apply the genetic algorithm, an artificial intelligence method, to haplotype data. The genetic algorithm creates in-computer artificial 'individuals', all having 'genes' coding for solutions to a problem. The individuals are allowed to compete and 'mate', individuals with genes coding for better solutions mating more often. Genes coding for good solutions survive through generations of the genetic algorithm. At the end of the run, the best solutions can be extracted. We applied the genetic algorithm to data consisting of cholesterol values and haplotypes made up of seven restriction sites at the LDL receptor locus. The persons included were 114 FH (familial hypercholesterolemia) patients and 61 normals. The genetic algorithm found the restriction sites 1 (Sph1 in intron 6), 2 (StuI in exon 8), and 7 (ApaLI site in the 3' flanking region) were associated with high cholesterol levels. As a validity check we used runs of the genetic algorithm applied to 'artificial patients', i.e. artificially generated haplotypes linked to artificially generated cholesterol values. This demonstrated the genetic algorithm consistently found the appropriate haplotype. We conclude that the genetic algorithm may be a useful tool for studying genetic variation.