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1.
Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.  相似文献   

2.
Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~ 80 in situ Remote Sensing Reflectance, (Rrs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~ 20 SeaWiFS Rrs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log10-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla > 2 mg m−3 and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations > 1 mg m−3 and under-estimated Chla at values < 0.5 mg m−3. The error in Carder Chla also co-varied with TSM. The algorithms were inter-compared using > 5500 SeaWiFS Rrs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2 mg m−3. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM > 25 g m−3. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~ 5 mg m−3) in October and November and were 0.08 mg m−3 further offshore increasing to 0.2 mg m−3 during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2 mg m−3 on the east coast of the Bay.  相似文献   

3.
Accurate remote assessment of phytoplankton chlorophyll a (chla) concentration is particularly challenging in turbid, productive waters. Recently a conceptual model containing reflectance in three spectral bands in the red and near infra-red range of the spectrum was suggested for retrieving chla concentrations in turbid productive waters; it was calibrated and validated in lakes and reservoirs in Nebraska and Iowa. The objective of this paper is to evaluate the performance of this three band model as well as its special case, the two-band model to estimate chla concentration in Chesapeake Bay, as representative of estuarine Case II waters, and to assess the accuracy of chla retrieval. To evaluate the model performance, dual spectroradiometers were used to measure subsurface spectral radiance reflectance in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained widely variable chla (9 to 77.4 mg/m3) and total suspended solids (7-65 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 440 nm was 0.20 to 2.50 m− 1; Secchi disk transparency ranged from 0.28 to 1.5 m. The two- and three-band models were spectrally tuned to select the spectral bands for most accurate chla estimation. Strong linear relationships were established between analytically measured chla and both the three-band model [R− 1(675)-R− 1(695)] × R(730) and the two-band model R(720)/R(670), where R(λ) is reflectance at wavelength λ. The three-band model accounted for 81% of variation in chla and allowed estimation of chla with a root mean square error (RMSE) of less than 7.9 mg/m3, whereas the two-band model accounted for 79% of chla variability and RMSE of chla estimation was below 8.4 mg/m3. The three-band model with MERIS spectral bands allows accurate chla estimation with RMSE below 9.1 mg/m3. Two-band model with SeaWiFS bands and MODIS 667 nm and 748 nm bands can estimate chla with RMSE below 11 mg/m3. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of this algorithm for chla retrieval in turbid, productive estuarine waters.  相似文献   

4.
Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [Rrs? 1(λ1) ? Rrs? 1(λ2)] × Rrs(λ3) where Rrs(λi) is the remote-sensing reflectance at the wavelength λi, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model Rrs? 1(λ1) × Rrs(λ3), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ1 = 662–672, λ3 = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r2 > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r2 > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.  相似文献   

5.
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 Rrs 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.  相似文献   

6.
Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chlFOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (Rrs(λ)) measurements. Additional shipboard and airborne Rrs(λ) data were also analysed to evaluate consistency of proposed chlFOD-Rrs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chlFOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive Rrs combination (Rrs(509)/Rrs(553)) to detect chlFOD variability. Conversely, OC2a (Rrs(412)/Rrs(553)) had the lowest performance to derive chlFOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chlFOD values could be universal for Alaskan coastal waters. However, chlFOD-Rrs(λ) relationships must be calibrated locally for a given season.  相似文献   

7.
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   

8.
This paper considers the uncertainties that arise in estimating the concentration of suspended minerals by optical remote sensing in waters which contain unknown concentrations of other optically significant constituents. Relationships between suspended mineral concentrations and remote sensing reflectance were calculated by radiative transfer modelling using representative specific inherent optical properties (SIOPs) for phytoplankton (CHL), suspended mineral particles of terrigenous origin (MSSter) and coloured dissolved organic matter (CDOM) that were derived from measurements at 173 stations in UK shelf seas. When only suspended minerals were present, remote sensing reflectance (Rrs) was related to MSSter by a family of saturation curves whose shape depended strongly on wavelength. However the addition of CHL and CDOM made this relationship considerably more complex. Polynomial expressions were therefore derived for the maximum and minimum values of MMSter consistent with a given Rrs667 in the presence of independently varying concentrations of CHL and CDOM. For CHL ranging from 0 to 10 mg m−3 and CDOM from 0 to 1 m−1, for example, an Rrs667 observation 0.01 sr−1 could corresponded to MSSter values between 7 and 12 g m−3. The presence of biogenic minerals in the form of diatom frustules, MSSdia had little influence on the accuracy of MSSter retrieval. The degree of variability in the relationship between MSSter and Rrs667 predicted by the model was confirmed by measurements of radiometric profiles and mineral concentrations at 110 Irish Sea stations. Uncertainties in the remote sensing of MSSter in coastal waters are more appropriately indicated by upper and lower limits set according to the likely ranges of other optically significant constituents than by percentage errors. Moreover, the influence of these constituents should be eliminated before variations in the relationship between MSSter and Rrs are attributed to qualitative changes in mineral particle characteristics.  相似文献   

9.
Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m−3 and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m−3 and 0 to 25 mg m−3 for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m−3 for chlorophyll-a concentrations from 0 to 100 mg m−3 and 1.2 mg m−3 for chlorophyll-a concentrations from 0 to 25 mg m−3. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.  相似文献   

10.
Empirical relationships between the sea surface partial pressure of carbon dioxide (pCO2), sea surface chlorophyll-a concentration (Chl-a), and sea surface temperature (SST), were derived from shipboard pCO2 measurements in sea water and atmosphere, in-situ Chl-a, and SST data along cruise tracks between Zhongshan Station in East Antarctica and Changcheng Station on the Antarctic Peninsula in December 1999, January 2000, December 2004 and January 2005 during the CHINARE XVI and XXI campaigns. These relationships were then applied to datasets of remotely sensed Chl-a and SST to estimate the monthly air-sea carbon flux and the uptake of atmospheric CO2 in the southern Atlantic and Indian Ocean. The results show significant spatial and temporal variability of carbon flux in the southern Atlantic and Indian Ocean. The monthly uptakes of atmospheric CO2 in the region from 50°S to the ice edge between 60°W and 80°E are − 0.00355 GtC, − 0.00573 GtC in December 1999 and January 2000, and − 0.00361 GtC, − 0.00525 GtC in December 2004 and January 2005, respectively.  相似文献   

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