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1.
Remote sensing techniques can offer powerful tools for measuring concentrations of chlorophyll-a (chl-a), which is an important proxy for water quality. However, remote estimates of chl-a can be difficult in water bodies that have high levels of total suspended matter (TSM). In this study, we examined the applicability of the synthetic chlorophyll index (SCI) and a parameter relevant to chlorophyll pigments (Hchl) used in conjunction with remote-sensing data to predict chl-a concentrations (Cchl-a) in Taihu Lake, a highly turbid hypereutrophic lake in eastern China. We sampled water quality and surface spectral properties at 250 field stations throughout the lake over five sampling periods spanning 2 years. Because data acquired at 31 stations could not be used due to equipment failure or blue-green algal blooms, we used data acquired at the remaining 219 stations. We then randomly selected parts of the spectral properties data (N = 164) to calibrate bands used in the SCI algorithm and established cubic polynomial models to estimate Cchl-a with SCI and Hchl as the independent variables. We evaluated the accuracy of these models using data from the remaining 55 stations that were not used for calibration. Our results showed the following trends: (1) the parameter of Hchl performed better than SCI in estimating Cchla in Taihu Lake; (2) Hchl showed optimal performance in winter, average performance in spring, and poor performance in summer and autumn; (3) Hchl was appropriate for the NAP-dominant waters with high CTSM and low Cchl-a, but was not suitable for organism-dominant waters with low CTSM; and (4) in short, Hchl had limited usability in turbid and eutrophic waters.  相似文献   

2.
An extensive in situ data set in the Bohai Sea of China was collected to assess radiometric properties and concentrations of ocean constituents derived from Medium Resolution Imaging Spectrometer (MERIS). The data collected include spectral normalized water-leaving radiance Lwn(λ) and concentrations of suspended particulate matter (SPM) and chlorophyll a (Chl-a). A strict spatio-temporal match-up method was adopted in view of the complexity and variability of the turbid coastal area, resulting in 13, 48 and 18 match-ups for MERIS Lwn(λ), SPM and Chl-a estimates, respectively. For MERIS Lwn(λ), the match-ups showed mean absolute percentage differences (APD) of 17%-20% in the 412, 443, 620 and 665 nm bands, whereas Lwn(λ) at bands from 490 and 560 nm had better APD of 15-16%. The band ratio of Lwn(490) to Lwn(560) of the satellite data was in good agreement with in situ observations with an APD of 4%. MERIS SPM and Chl-a products overestimated the in situ values, with the APD of approximately 50% and 60%, respectively. When match-up criteria were relaxed, the assessment results degraded systematically. Hence, in turbid coastal areas where temporal variability and spatial heterogeneity of bio-optical properties may be pronounced as the result of terrestrial influences and local dynamics, the strict spatio-temporal match-up is recommended.  相似文献   

3.
ABSTRACT

Hyperspectral remote sensing can capture the complicated and variable characteristics of inland waters; thus, it is suited for the water quality assessment of Case-2 waters, and it has the potential to attain high estimation accuracy. In the present study, four improved models adapted from published approaches (three-band index, ΔΦ, BNDBI and TCARI) were investigated to estimate chlorophyll-a (chl-a) for the case of Dianshan Lake, China. Calibration and validation were provided from in situ measured chl-a and field hyperspectral measurements. The improved three-band (ITB) model, ΔΦ model, and BNDBI model yielded satisfactory results and enabled the estimation of chl-a for inland Case-2 waters with coefficients of determination (R2) reaching 0.75, 0.76, and 0.86, respectively. In particular, the TCARI/OSAVI model presented the highest accuracy (R2 = 0.94) compared to the other models. All of the results provide strong evidence that the hyperspectral models presented in this paper are promising and applicable to estimate chl-a in eutrophic inland Case-2 waters.  相似文献   

4.
Remote sensing of chlorophyll-a is challenging in water containing inorganic suspended sediments (i.e. non-volatile suspended solids, NVSS) and coloured dissolved organic matter (CDOM). The effects of NVSS and CDOM on empirical remote-sensing estimates of chlorophyll-a in inland waters have not been determined on a broad spatial and temporal scale. This study evaluated these effects using a long-term (1989–2012) data set that included chlorophyll-a, NVSS, and CDOM from 39 reservoirs across Missouri (USA). Model comparisons indicated that the machine-learning algorithm BRT (boosted regression trees, validation Nash–Sutcliffe coefficient = 0.350) was better than linear regression (validation Nash–Sutcliffe coefficient = 0.214) for chlorophyll-a estimate using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery. Only a small proportion of BRT model residuals could be explained by sediments or CDOM, and the observed trends in BRT residuals were different from the theoretical effects expected from NVSS and CDOM. Our results also indicated a small systematic bias by the BRT model, but it was not likely caused by NVSS or CDOM.  相似文献   

5.
Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ1)− 1 − Rrs(λ2)− 1][Rrs(λ4)− 1 − Rrs(λ3)− 1]− 1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs− 1(660) − Rrs− 1(692)]Rrs(740) and the four-band algorithm of [Rrs− 1(662) − Rrs− 1(693)][Rrs− 1(740) − Rrs− 1(705)]− 1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters.  相似文献   

6.
Timely mapping of underwater topography over turbid coastal waters is very important to navigation. Such a task is ideally accomplished through moderate- resolution imaging spectroradiometer (MODIS) satellite data that have a temporal resolution measured in hours. In this article we propose a simple regression method for retrieving bathymetry from MODIS bands. It only requires concurrently collected total suspended solids and water depth samples at limited spots, without considering the downward attenuation coefficient, surface reflectance or bottom reflectance. Regression analysis of the observed spot depth (D) against individual bands and their transformation enables an empirical model to be established. The model in which band 3 (M 3) is the exploratory variable is the most accurate, with an r value (correlation coefficient) of only 0.654. Correction of this model by the concentration level of suspended solids using bands 2 (M 2) and 5 (M 5) improves the prediction accuracy from 3.26 to 1.52 m, or from 39% to 24%. The best model takes the form of D?=?–7.833?+?0.0326M 3?/?(M 2 M 5) (r?=?0.815, n?=?3318). Application of this model to the MODIS imagery led to the generation of a bathymetric map over the 15 000 km2 study area. Assessed against four profiles, the retrieved bathymetry has a mean absolute accuracy of around 2 m or a relative inaccuracy of 10% to 18%. The remotely sensed bathymetry contains many minor relief features absent from its in situ-surveyed counterpart. It is concluded that this proposed simple method can produce reasonably accurate results without the need to consider atmospheric effects or bottom reflectance over the range of 5–20 m. However, it may not work so well in clear oceanic Case I waters.  相似文献   

7.
Remote-sensing data can be useful for investigating the bio-optical properties of the ocean. Among these bio-optical properties, chlorophyll-a content is of great importance. The standard NASA empirical ocean-colour (OC) algorithms are used widely to estimate global chlorophyll-a content. Despite their simplicity and effectiveness, these regression-based models have two shortcomings that we investigate here: (1) the general form of the models is a fourth-order polynomial that results in multicollinearity, and (2) the models have the same parameters for all ocean regions (i.e. they use global approaches). To resolve the first issue, we use partial least squares (PLS), which allows for an orthogonal transformation such that the covariance between the transformed independent variables and the dependent variable is maximized. To investigate the second issue, we use geographically weighted regression (GWR) to reveal the spatial variation of estimated parameters, demonstrating how the global model underperforms in some locations. GWR results show that model coefficients vary substantially between eastern and western portions of the same ocean basin. By including sea-surface temperature (SST) as an additional independent variable in the PLS model, we also develop a new approach that provides additional explanatory power and makes the global estimation of chlorophyll-a content more valid.  相似文献   

8.
The diffuse attenuation coefficient, Kd(λ), is an important water optical property. Detection of Kd(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogeochemical processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical pre-existing Kd(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new Kd(490) models were developed for these waters by means of the support vector machine approach. The obtained results showed that the two pre-existing Kd(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ~30%. More importantly, among the new models, type-specific models generally outperformed the aggregated model. For water classified as Type 1 + Type 2, the type-specific model produced validation errors with MAPE = 16.8% and RMSE = 0.98 m?1. For water classified as Type 3, the MAPE and RMSE of the type-specific model were found to be 18.8% and 1.85 m?1, respectively. The findings in this study demonstrate that water classification (prior to algorithm development) is needed for the development of excellent Kd(490) retrieval algorithms, and the type-specific models thus developed are an important supplement to existing Kd(490) retrieval models for highly turbid inland waters.  相似文献   

9.
Ashman  R. 《IT Professional》2004,6(4):40-44
Software development estimates are inaccurate and overly optimistic estimates are major contributors to project failure, despite the fact that every completed project is a rich source of information about performance and estimation. Modern development processes promote risk management, the realization of architecture first, the decomposition of the project into iterations, and the assignment of requirements to these iterations. When a project adopts these forms of best practice, it achieves a high degree of technical control and easier management. One difficult project management task is to accurately determine the effort required to complete the project. This article discusses a use-case-based estimation model for determining project effort. This technique calls for looking at the relationship between estimated and actual data to improve future estimates. Using a simple set of metrics, it is possible to generate a credible model for project estimation. The model described here works best in an iterative development process, allowing comparisons between successive iterations.  相似文献   

10.
Over the past few years, the increased spectral and spatial resolution of remote sensing equipment has promoted the investigation of new techniques for inland and coastal water monitoring. The availability of new high-resolution data has allowed improvements in models based on the radiative transfer theory for assessing optical water quality parameters. In this study, we fine-tuned a physical model for the highly turbid Venice lagoon waters and developed an inversion technique based on a two-step optimization procedure appropriate for hyperspectral data processing to retrieve water constituent concentrations from remote data. In the first step, the solution of a linearized analytical formulation of the radiative transfer equations was found. In the second step, this solution was used to provide the initial values in a non-linear least squares-based method. This effort represents a first step in the construction of a feasible and timely methodology for Venice lagoon water quality monitoring by remote sensing, especially in view of the existing experimental hyperspectral satellite (Hyperion) and the future missions such as PRISMA, EnMap and HyspIRI. The optical properties of the water constituents were assessed on the basis of sea/lagoon campaigns and data from the literature. The water light field was shaped by an analytical formulation of radiative transfer equations and the application of numerical simulations (Hydrolight software). Once the optical properties of the Venice lagoon bio-optical model were validated, the inverse procedure was applied to local radiometric spectra to retrieve concentrations of chlorophyll, colored dissolved organic matter and tripton. The inverse procedure was validated by comparing these concentrations with those measured in the laboratory from in situ water samples, then it was applied to airborne (CASI and MIVIS) and satellite (Hyperion) sensors to derive water constituent concentration maps. The consistent results encourage the use of this procedure using future missions satellite (PRISMA, EnMap and HyspIRI).  相似文献   

11.
Ocean colour imagery is used increasingly as a tool to assess water quality via chlorophyll-a concentration (chl-a) estimations in European waters. The Bay of Biscay is affected by major river discharges, which alter the constituents of the marine waters. Chlorophyll-a algorithms, designed for use at global scales, are less accurate due to the variability of optically active in-water constituents. Hence, regionally parameterized empirical algorithms are necessary. The main objective of the present study was to develop a regional algorithm to retrieve chl-a in surface water using in situ R rs, for a subsequent application to Medium Resolution Imaging Spectrometer (MERIS) satellite images. To address this objective, a platform was developed initially and a measurement procedure adapted for the field HR4000CG Spectrometer. Subsequently, the procedure was tested during a survey over the south-eastern Bay of Biscay (North-East Atlantic Ocean), to establish a MERIS chl-a algorithm for the area, by comparing different global remote sensing chl-a algorithms, with band ratios. Results validated with the jackknife resampling procedure show a satisfactory relationship between the R rs(510)/R r s(560) and chl-a (R 2 jac?=?0.681). This ratio is better correlated to chl-a than those obtained with established chl-a remote sensing algorithms. High content in coloured dissolved organic matter (CDOM > 0.4 m?1) and suspended particulate matter (SPM > 2.8 mg l?1) influenced this relationship, with yellow substances having a stronger effect.  相似文献   

12.
The influence of the optical properties of inorganic suspended solids (ISS) on in-water algorithms was evaluated using an optical model in highly turbid coastal water, whose ISS concentration reached several hundred grams per cubic metre. The measurements were conducted in the upper Gulf of Thailand. The backscattering coefficient of the ISS was calculated using the Lorenz–Mie scattering theory. On the basis of the measurement, the ISS size distribution was parameterized as a function of ISS concentration, and both the spherical and non-spherical particle shape models were evaluated. For ISS concentrations of 10 g m?3, an estimate of the chlorophyll-a (chl-a) concentration within a factor of 2 on a logarithmic scale is possible in a [chl-a] range of 4–30 mg m?3. The differential coefficient of remote sensing reflectance was calculated to evaluate its respective sensitivities for chl-a and ISS concentrations. The use of radiometric data at 670 nm (700–900 nm) is valid for in-water algorithms used to estimate chl-a (ISS) concentration in highly turbid coastal waters.  相似文献   

13.
A new empirical index, termed the normalized suspended sediment index (NSSI), is proposed to predict total suspended sediment (TSS) concentrations in inland turbid waters using Medium Resolution Imaging Spectrometer (MERIS) full-resolution (FR) 300 m data. The algorithm is based on the normalized difference between two MERIS spectral bands, 560 and 760 nm. NSSI shows its potential in application to our study region – Poyang Lake – the largest freshwater lake in China. An exponential function (R2 = 0.90, p < 0.01) accurately explained the variance in the in situ data and showed better performance for the TSS range 10–524 mg l?1. The algorithm was then validated with TSS estimates using an atmospheric-corrected MERIS FR image. The validation showed that the NSSI algorithm was a more robust TSS algorithm than the band-ratio algorithms. Findings of this research imply that NSSI can be successfully used on MERIS images to obtain TSS in Poyang Lake. This work provided a practical remote-sensing approach to estimate TSS in the optically and hydrologically complex Poyang Lake and the method can be easily extended to other similar waters.  相似文献   

14.
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(λi) is the remote-sensing reflectance at wavelength λi, 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-amea)) = 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-amea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.  相似文献   

15.
In very shallow waters, active sensing determinations of bathymetry are often expensive and unwieldy. Sea depth estimation using passive remote-sensing methods is an attractive alternative, especially using cheap multispectral imagery with high spatial resolution. Three models for the determination of bathymetry from multispectral imagery were utilized with new eight-band images from DigitalGlobe's Worldview-2 satellite platform. All three were trained with electronic navigational chart data and evaluated for accuracy in Singapore's turbid shallow coastal waters. These waters are characterized by high turbidity, suspended sediment, and vehicle traffic. Of the three models, a linear band algorithm performed best, with a root-mean-square error (RMSE) of 0.48 m. A look-up table classification provided a precision of 0.64 m, but was limited by a training set that did not fully represent variance in water column and benthic properties. Possibly owing to the domination of particle backscatter over pigment absorption in these turbid waters, a linear ratio algorithm did not perform as well as the linear band algorithm, achieving an RMSE of only 0.56 m. Analysis found that the usual relationship between ratios of low-absorption to high-absorption bands and depth does not hold as well for these waters, likely due to backscatter dominating leaving-water signals, masking relative absorption effects. High turbidity, with a Secchi disk depth of 1.9 m, limited analysis to shallow reefs and coastline and likely impacted the sensitivity of the bathymetric algorithms. A larger validation data set containing water quality and benthic data is required for further investigation to determine specific sources of error.  相似文献   

16.
Remotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R2 > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRImod, R510R570 nm) performed better than the original PRI (PRI, R531R570 nm) with R2 improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRImod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.  相似文献   

17.
Remote estimation of water constituent concentrations in case II waters has been a great challenge, primarily due to the complex interactions among the phytoplankton, tripton, colored dissolved organic matter (CDOM) and pure water. Semi-analytical algorithms for estimating constituent concentrations are effective and easy to implement, but two challenges remain. First, a dataset without a sampling bias is needed to calibrate estimation models; and second, the semi-analytical indices were developed based on several specific assumptions that may not be universally applicable. In this study, a semi-analytical model-optimizing and look-up-table (SAMO-LUT) method was proposed to address these two challenges. The SAMO-LUT method is based on three previous semi-analytical models to estimate chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. Three datasets (i.e., noise-free simulation data, in situ data and Medium Resolution Imaging Spectrometer (MERIS) satellite data) were collected to validate the performance of the proposed method. The results show that the SAMO-LUT method yields error-free results for the ideal simulation dataset; and is able also to accurately estimate the water constituent concentrations with an average bias (mean normalized bias, MNB) lower than 9% and relative random uncertainty (normalized root mean square error, NRMS) lower than 34% even for in situ and MERIS data. These results demonstrate the potential of the proposed algorithm to accurately monitor inland and coastal waters based on satellite observations.  相似文献   

18.

Based on a previously developed and thoroughly validated hydrooptical model, numerical simulations of the spectral composition of water leaving radiance are presented. These simulations take into account absorption, elastic scattering, water Raman (inelastic) scattering as well as the fluorescence of chlorophyll ( chl ) and dissolved organics ( doc ). The results obtained for forward modelling were also used for the inverse problem: retrieval of water quality parameters from water volume reflectance ( R ) spectra. The Levenberg-Marquardt multivariate optimization procedure was used for this purpose. Unlike water Raman scattering, the chl and doc fluorescence has an impact on R, so the retrieval results can change substantially for waters rich in chl or doc . Suspended minerals ( sm ) suppress both the chl and doc fluorescence influence on R . The retrieval results indicate that chl can be accurately assessed if the concentration of sm is not low and the doc concentration is < 2 mgCl -1 . For waters devoid of doc, the concentration of chl can be accurately retrieved even if the sm concentration is very low. Retrieval errors prove to be strongly dependent on the fluorescence yield value of both chl and doc .  相似文献   

19.
The use of satellites to monitor the color of the ocean requires effective removal of the atmospheric signal. This can be performed by extrapolating the aerosol optical properties in the visible from the near-infrared (NIR) spectral region assuming that the seawater is totally absorbant in this latter part of the spectrum. However, the non-negligible water-leaving radiance in the NIR which is characteristic of turbid waters may lead to an overestimate of the atmospheric radiance in the whole visible spectrum with increasing severity at shorter wavelengths. This may result in significant errors, if not complete failure, of various algorithms for the retrieval of chlorophyll-a concentration, inherent optical properties and biogeochemical parameters of surface waters.This paper presents results of an inter-comparison study of three methods that compensate for NIR water-leaving radiances and that are based on very different hypothesis: 1) the standard SeaWiFS algorithm (Stumpf et al., 2003; Bailey et al., 2010) based on a bio-optical model and an iterative process; 2) the algorithm developed by Ruddick et al. (2000) based on the spatial homogeneity of the NIR ratios of the aerosol and water-leaving radiances; and 3) the algorithm of Kuchinke et al. (2009) based on a fully coupled atmosphere-ocean spectral optimization inversion. They are compared using normalized water-leaving radiance nLw in the visible. The reference source for comparison is ground-based measurements from three AERONET-Ocean Color sites, one in the Adriatic Sea and two in the East Coast of USA.Based on the matchup exercise, the best overall estimates of the nLw are obtained with the latest SeaWiFS standard algorithm version with relative error varying from 14.97% to 35.27% for λ = 490 nm and λ = 670 nm respectively. The least accurate estimates are given by the algorithm of Ruddick, the relative errors being between 16.36% and 42.92% for λ = 490 nm and λ = 412 nm, respectively. The algorithm of Kuchinke appears to be the most accurate algorithm at 412 nm (30.02%), 510 (15.54%) and 670 nm (32.32%) using its default optimization and bio-optical model coefficient settings.Similar conclusions are obtained for the aerosol optical properties (aerosol optical thickness τ(865) and the Ångström exponent, α(510, 865)). Those parameters are retrieved more accurately with the SeaWiFS standard algorithm (relative error of 33% and 54.15% for τ(865) and α(510, 865)).A detailed analysis of the hypotheses of the methods is given for explaining the differences between the algorithms. The determination of the aerosol parameters is critical for the algorithm of Ruddick et al. (2000) while the bio-optical model is critical for the algorithm of Stumpf et al. (2003) utilized in the standard SeaWiFS atmospheric correction and both aerosol and bio-optical model for the coupled atmospheric-ocean algorithm of Kuchinke. The Kuchinke algorithm presents model aerosol-size distributions that differ from real aerosol-size distribution pertaining to the measurements. In conclusion, the results show that for the given atmospheric and oceanic conditions of this study, the SeaWiFS atmospheric correction algorithm is most appropriate for estimating the marine and aerosol parameters in the given turbid waters regions.  相似文献   

20.
A simple mathematical model of laser drilling is proposed. Assuming axi-symmetry of the process around the axis of the laser beam, a one-dimensional formulation is obtained after cross-sectional averaging. The novelty of the approach relies on the fact that even after dimension reduction, the shape of the hole can still be described. The model is derived, implemented and validated for drilling using lasers with intensities in the GW/cm2 range and microsecond pulses.  相似文献   

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