Estimation Of Chlorophyll-a Concentration Research Articles

First attempt to derive chlorophyll-a using natural fluorescence in Northern South China Sea

In this study, an algorithm is developed to retrieve chlorophyll-a concentration in northern South China Sea from natural fluorescence based on moderate resolution imaging spectroradiometer-Terra data. The fluorescence algorithm performs with high accuracy in our study region with R 2 = 0.88 and root mean square error (RMSE) = 16%. This region-specific model was compared with previous fluorescence algorithms for other coastal waters. The discrepancies between measured and estimated chlorophyll-a concentrations are attributed to nutrient supply, solar irradiance, phytoplankton community, time difference between sea truth and satellite observations, etc. The algorithm can be effective for regular and synoptic monitoring of chlorophyll-a concentrations, especially in coastal regions, at high temporal and spatial resolutions.

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

We present here results that strongly support the use of MERIS-based NIR-red algorithms as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. The study was carried out as one of the steps in testing the potential of the universal applicability of previously developed NIR-red algorithms, which were earlier calibrated using a limited set of MERIS imagery and in situ data from the Azov Sea and the Taganrog Bay, Russia, and data that were synthetically generated using a radiative transfer model. We used an extensive set of MERIS imagery and in situ data collected over a period of three years in the Azov Sea and the Taganrog Bay for this validation task. We found that the two-band and three-band NIR-red algorithms gave consistently highly accurate estimates of chl-a concentration, with a mean absolute error of 4.32mgm−3 and 4.71mgm−3, respectively, and a root mean square error as low as 5.92mgm−3, for data with chl-a concentrations ranging from 1.09mgm−3 to 107.82mgm−3. This obviates the need for case-specific reparameterization of the algorithms, as long as the specific absorption coefficient of phytoplankton in the water does not change drastically, and presents a strong case for the use of NIR-red algorithms as standard algorithms that can be routinely applied for near-real-time quantitative monitoring of chl-a concentration in the Azov Sea and the Taganrog Bay, and potentially elsewhere, which will be a real boon to ecologists, natural resource managers and environmental decision-makers.

Chlorophyll-a concentration estimation with field spectra of summer water-body in Lake Qiandao

依据2010年8月的实测数据构建了千岛湖水体夏季叶绿素a浓度的实测光谱数据估算模型,并进行了验证.利用ASD FieldSpec3野外光谱仪获取高光谱数据,计算水体离水辐亮度和遥感反射率.通过寻找反演水体叶绿素a浓度的高光谱敏感波段,采用单波段相关分析、波段比值、微分光谱、三波段模型、BP人工神经网络等多种算法进行比较分析,结果表明:叶绿素a浓度与单波段光谱反射率的相关性不大;596 nm和489 nm波长处反射率比值、545 nm处光谱一阶微分与叶绿素a浓度均呈较显著相关,估测模型决定系数R²分别为0.782、0.590,RMSE分别为0.89、1.98μg/L;三波段模型的反演结果优于传统的波段比值和一阶微分法,R²为0.838,RMSE为0.71μg/L;神经网络模型大大提高了叶绿素a浓度的反演精度,R²高达0.942,RMSE为0.63μg/L.本研究为今后在千岛湖水域的夏季相邻月份进行叶绿素a浓度大范围遥感反演研究奠定了基础.;Based on the in situ data collected in August 2010, hyperspectral data models estimating summer chlorophyll-a concentration in Lake Qiandao are presented. A large quantity of hyperspectral reflectance data and water quality data of the typical area of the lake were obtained. Hyperspectral data were measured using ASD FieldSpec3, and were calculated for water-leaving radiance and reflectance of water. Different methods including band ratio model, the first derivative model, three-band-model and BP neural network model were used to estimate chlorophyll-a concentration. Results showed that single band reflectance model gave the worst estimation on chlorophyll-a concentration. Band ratio model with the ratio of reflectance 596 nm/489 nm and the first derivative model of reflectance near 545 nm gave better results with high determination coefficients of 0.782 and 0.590, respectively. By comparison, the three-band-model had higher estimation precision (coefficient of 0.838) than the band ratio model and the first derivative model. BP neural network model performed best with a high determination coefficient of 0.942. The root mean square error between measured and estimated chlorophyll-a concentrations using the four models was 0.89, 1.98, 0.71 and 0.63 μg/L, respectively. Therefore, three-band-model and BP neural network model was recommended to estimate chlorophyll-a concentration with remote sensing data for large area of Lake Qiandao in the summer.

Mapping Potential Fishing Grounds in Lake Malawi Using AVHRR and MODIS Satellite Imagery

This paper discusses a procedure that was developed to delineate potential fishing grounds in Lake Malawi using data on chlorophyll-a concentration derived from Moderate-resolution Imaging Spectroradiometer (MODIS/AQUA) in combination with lake surface temperature (LST) data obtained from Advanced Very High Resolution Radiometer (AVHRR) and MODIS/Terra satellite sensors. The paper draws from findings of studies [1,2] on development of algorithms for estimating chlorophyll-a and lake surface temperature in Lake Malawi from satellite imagery, respectively. To estimate chlorophyll concentration (a proxy for phytoplankton) in Lake Malawi using data from MODIS satellite imagery, in situ measurements of chlorophyll concentration were conducted at three selected sampling stations over the southeastern arm of Lake Malawi concurrent with satellite image acquisitions. These were regressed on chlorophyll-a concentration values obtained from Ocean Color (MODIS/AQUA) Data using SeaWIFS Data Analysis System (SeaDAS) software. From this, an equation for estimating chlorophyll-a concentration in Lake Malawi from MODIS satellite imagery was developed and used for mapping the spatial distribution of chlorophyll-a concentration in the lake. Since Lake Malawi is an oligotrophic lake, with an average value of chlorophyll concentration of 1 μg/L, areas in the lake with relatively high chlorophyll-a concentration were identified as potential locations for the development of the fishery industry. Estimation of lake surface temperature using satellite imagery involved two main activities. Firstly, in situ measurements of lake surface temperature were conducted at the three selected sampling stations over Lake Malawi concurrent with satellite image acquisitions. The second activity involved downloading and processing AVHRR and MODIS/Terra satellite imagery. AVHRR data covered the period September 1997 to February 1998 whereas MODIS/Terra data covered the period May to November, 2006. Both MODIS Land Surface Temperature (MOD11A1) and Ocean Color Sea Surface Temperature (SST) were downloaded from EOS Gateway website and processed into lake surface temperature. Two glass thermometers were used to measure temperature directly from the lake surface at a depth of 0 - 7.0 cm (i.e., skin temperature) and the average of the two readings was recorded as the lake surface temperature at a particular sampling station. Observed temperatures were regressed on remotely sensed data. ER Mapper was employed in drawing maps showing the distribution of lake surface temperature using the regression equation that was developed. Upwelling and downwelling zones were demarcated from lake surface temperature maps. Upwelling zones were identified as areas with a high potential for the development of the fishery industry because of their association with primary productivity. Using a simple overlay technique, data from both the spatial and temporal distribution of chlorophyll-a and lake surface temperature were used to delineate potential fishing grounds in Lake Malawi. The zone extending from Salima up to the northern part of Nkhotakota and the area on the northeastern tip of Lake Malawi were identified as areas of high primary productivity and therefore potential fishing grounds. These areas generally exhibit persistent cool surface waters, indicative of upwelling; and have relatively abundant phytoplankton.

고해상도 위성영상을 이용한 낙동강 유역의 클로로필-a 농도 추정

본 연구에서는 Moderate Imaging Spectroradiometer(MODIS), Sea-viewing Wide Fieldfo-view Sensor(SeaWiFS), Medium Resolution Imaging Spectrometer(MERIS) 등의 광역관측 위성 영상을 이용한 해수나 연안수의 클로로필 농도 분석을 통해 가능성이 확인되었던 밴드 비를 이용한 비교적 간단한 추정 모델을 수체의 크기와 폭이 현저히 작고 탁도가 있는 하천에 대해 클로로필- a농도값을 추정하고자 고해상도 위성영상에 Two-band 및 Three-band reflectance 모델을 적용하여 가능성을 파악하였다. 특히 RapidEye 영상을 이용하여 일반적으로 탁도가 있는 수체에 대해 Red와 NIR 영역을 활용하는 이들 모델에 Red-edge(RE) 밴드를 적용하였다. Red와 NIR을 이용한 Two-band Reflectance 모델은 계산식의 결정계수 R2 값이 0.38로 유의성 없는 결과를 나타내었다. 그러나 RapidEye의 Red-edge (RE) 파장대를 이용한 Red-RE Two-band 모델과 Red-RE-NIR Three-band 모델을 이용한 계산식에 대해서는, 2차함수에 의한 Three-band 모델의 결과는 Red-RE Two-band 모델의 결과와 통계적인 값이 거의 유사하였고 Two-band와 3차함수에 의한 Three-band 모델 추정식은 각각 0.66, 0.73 의 R2값을 나타내어 Red-edge 밴드의 적용 가능성을 보였고, 실측치와의 Root Mean Square Error (RMSE)는 24.8, 22.4 mg m-3, Relative Percent Difference(RPD)는 각각 1.30, 1.29로 1.5 이하의 대략적인 추정(Approximate Prediction) 수준을 나타내었다. 고해상도 위성영상에 Red-RE-NIR Three-band 모델을 적용한 계산식을 이용해 대략적인 추정이지만 가장 유의한 수준의 클로로필- a농도를 추정할 수 있었다. 영상에서 추정된 클로로필 -a분포를 비교하였을 때 3차함수에 의한 Three-band 모델 추정식이 Two-band 모델에 비해 낮은 값의 분포를 보였다. 향후 하천의 스펙트럼을 실측하여 파장별 부유물질, 유기물과의 상관성 및 클로로필 농도와의 간섭 정도를 시뮬레이션하여 보정식을 산출·적용한다면 탁도가 다소 높은 하천에서의 클로로필 -a농도 계산식의 정확도를 더욱 높일 수 있을 것으로 기대된다.

Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay: a comparison of remote sensing algorithms

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.

Monitoring estuarine water quality using satellite imagery. The Mondego river estuary (Portugal) as a case study

Monitoring water bodies is a task not only scientifically relevant but also legally binding in several countries (e.g. Water Framework Directive and the European Marine Strategy Framework Directive). Ecosystem health assessment projects are greatly enhanced by the use of remote sensing technologies. In the present study, a small Portuguese estuary (Mondego river estuary), was used as a case study to assess the feasibility of measuring several key indicators using satellite imagery.The feasibility of using MODIS land/cloud bands to evaluate Secchi Disk Depth (SD) was addressed. The ratio of remote sensing reflectance at 469 and 555 nm was the best predictor for this parameter (R2 = 0.9678; p < 0.05).Landsat-7 ETM+ was, in turn, used to estimate Chlorophyll-a concentration (OC), Turbidity and Salinity. Reflectance values were compared with concurrent in situ data recorded by an automatic sampling buoy. ETM+ data was also used to develop a local water color index, which takes advantage of the relative differences in the reflectance values of the three bands in the visible range of the spectrum.Overall, remote sensing data yielded robust estimates of the selected indicators, favoring its future operational use in the study area.

Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl River Estuary, China

Accurate assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid waters by means of remote sensing was challenging due to the optical complexity of turbid waters. Recently, a conceptual model containing reflectance in three spectral bands in the red and near-infrared range of the spectrum was suggested for retrieving Chla concentrations in turbid productive waters. The objective of this paper was to evaluate the performance of this three-band model to estimate Chla concentration in the Pearl River Estuary (PRE), China. Reflectance spectra of surface water and water samples were collected concurrently. The samples contained variable Chla (4.80–92.60 mg/m 3) and total suspended solids (0.4–55.2 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 400 nm was 0.40–1.41 m −1; turbidity ranged from 4 to 25 NTU (Nephelometric Turbidity Units). The three-band model was spectrally calibrated by iterative and least-square linear regression methods to select the optimal spectral bands for the most accurate Chla estimation. Strong linear relationships ( R 2 = 0.81 , RMSE=1.4 mg/m 3, N = 32 ) were established between measured Chla and the levels obtained from the calibrated three-band model [ R − 1 ( 684 ) – R − 1 ( 690 ) ] × R ( 718 ) , where R ( λ ) was the reflectance at wavelength λ . The calibrated three-band model was independently validated ( R 2 = 0.9521 , RMSE=6.44 mg/m 3, N = 16 ) and applied to retrieve Chla concentrations from the calibrated EO-1 Hyperion reflectance data in the PRE on December 21, 2006. The EO-1 Hyperion-derived Chla concentrations were further validated using synchronous in situ data collected on the same day ( R 2 = 0.64 , RMSE=2 mg/m 3, N = 9 ). The spatial tendency of Chla distribution mapping by Hyperion showed gradually increased concentrations of Chla farther from the river mouths (although decreasing from east to west), which were disturbed by the combination of river outlets and tidal current in Lingding Bay of the PRE. This observation conformed to previous observations and studies, and could reasonably be explained by geographical changes. Also, results indicated that the slope of the three-band regression line decreased as the Chla concentration increased, resulting in the first sensitive band of the three-band model to move towards short wavelengths. These findings validated the rationale behind the conceptual model and demonstrated the robustness of this algorithm for Chla retrieval from in situ data and the Hyperion satellite sensor in turbid estuarine waters of the PRE, China.

Air-Sea CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes on the Scotian Shelf: seasonal to multi-annual variability

Abstract. We develop an algorithm to compute pCO2 in the Scotian Shelf region (NW Atlantic) from satellite-based estimates of chlorophyll-a concentration, sea-surface temperature, and observed wind speed. This algorithm is based on a high-resolution time-series of pCO2 observations from an autonomous mooring. At the mooring location (44.3° N and 63.3° W), the surface waters act as a source of CO2 to the atmosphere over the annual scale, with an outgassing of −1.1 mol C m−2 yr−1 in 2007/2008. A hindcast of air-sea CO2 fluxes from 1999 to 2008 reveals significant variability both spatially and from year to year. Over the decade, the shelf-wide annual air-sea fluxes range from an outgassing of −1.70 mol C m−2 yr−1 in 2002, to −0.02 mol C m−2 yr−1 in 2006. There is a gradient in the air-sea CO2 flux between the northeastern Cabot Strait region which acts as a net sink of CO2 with an annual uptake of 0.50 to 1.00 mol C m−2 yr−1, and the southwestern Gulf of Maine region which acts as a source ranging from −0.80 to −2.50 mol C m−2 yr−1. There is a decline, or a negative trend, in the air-sea pCO2 gradient of 23 μatm over the decade, which can be explained by a cooling of 1.3 °C over the same period. Regional conditions govern spatial, seasonal, and interannual variability on the Scotian Shelf, while multi-annual trends appear to be influenced by larger scale processes.

Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands

Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3=[Rrs(-1)(665)-Rrs(-1)(708)]×Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl]>1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.

Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary

Satellite estimation of chlorophyll-a (Chl-a) concentration in the Changjiang Estuary is challenging for ocean-colour retrieval algorithms. The Changjiang Estuary is characterized by suspended-sediment-dominated waters in the mouth and optically complex case 2 waters offshore of the mouth. Satellite ocean-colour products show that high-sediment loads of estuarine waters can cause overestimations or invalid flags of Chl-a concentration. A synthetic chlorophyll index (SCI) was designed for extracting chlorophyll information and for minimizing the influence of sediments on the remote-sensing reflectance spectrum. An SCI algorithm, a quadratic polynomial function of the SCI versus Chl-a concentration, was applied to the estimation of Chl-a concentration from Medium Resolution Imaging Spectrometer (MERIS) images. The overestimation of Chl-a concentration was corrected. The SCI algorithm has applications for MERIS estimation of Chl-a concentration in turbid waters with a moderate to high suspended-sediment concentration.

Remote sensing of chlorophyll-a concentrations of the Pearl River Estuary from MODIS land bands

Accurate remote assessment of phytoplankton chlorophyll-a concentration is particularly challenging in estuary areas. The object of this paper is to calibrate the two Moderate Resolution Imaging Spectroradiometer (MODIS) land bands (band 1 and band 2) for chlorophyll-a concentration remote sensing in Pearl River Estuary, and to assess the accuracy of chlorophyll-a retrieval. A nonlinear two-band model was established between the ratio of band 2 to band 1 and the in situ measurements of chlorophyll-a concentration. The model can estimate chlorophyll-a concentrations with root mean square errors below 4.59 mg m−3, and the relative error of the estimated chlorophyll-a concentrations is generally less than 30%, with chlorophyll-a concentrations in the range 5 to 60 mg m−3. In addition, the influence of the chromophoric dissolved organic matter and total suspended matter are considered, and comparisons of the estimates with in situ data for the control point are also given. The model is then employed to determine the chlorophyll-a concentration distribution for Pearl River Estuary and adjacent coastal areas from MODIS data. The findings in the paper demonstrate that the MODIS land band can be used for chlorophyll-a concentration retrieval in turbid, productive estuarine waters.

Optical properties of Peter the Great Bay waters compared with satellite ocean colour data

The shipboard measurements of optical and hydrological properties of seawater were used for development of regional bio-optical algorithms of waters of Peter the Great Bay. The shipboard measurements of chlorophyll-a and dissolved organic matter (DOM) concentrations were obtained by flow-through laser fluorometer (LF-3) (Institute for Automation and Control Processes, Vladivostok, Russia) and Sea-Bird Profiling CTD SBE-19plus (Conductivity, Temperature and Depth Sea-Bird Electronics, Sea-Bird Electronics, Inc., Washington, USA) equipped with WetStar fluorometers (WET Labs, Oregon, USA). The measurements were optically weighted according to light attenuation by depth. Ocean colour Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua data were used for our comparative analysis. Chlorophyll-a concentrations from ocean colour data were retrieved by OC3M, Garver-Siegel-Maritorena (GSM) and Carder algorithms. Relationships of the shipboard and satellite sensor data were analysed comparatively in our work. The Carder model gives the most general results of estimation of chlorophyll-a concentrations for all observed water cases. OC3M and GSM algorithms show good performance in case I water but they have large errors in the Amur Bay waters because of a large impact of the Razdolnaya river and Vladivostok city anthropogenic activity. Two regional chlorophyll-a algorithms were offered and they improve significantly the accuracy of chlorophyll-a concentration estimation. The first one is based on empirical regression between chlorophyll-a concentration and remotely sensed reflectance ratio. The second one uses satellite-derived values of sun-induced chlorophyll-a fluorescence when chlorophyll-a concentrations are higher than 3 mg m−3. The last one gives the best results for all water cases and provides an approach for separation between DOM produced by phytoplankton and DOM not connected with phytoplankton activity. This allows a regional DOM algorithm to be made.

Satellite estimation of chlorophyll-a concentration and phytoplankton primary production in the Sea of Azov

Satellite estimation of chlorophyll-a concentration and phytoplankton primary production in the Sea of Azov

Spatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series

Lake Tanganyika, the second largest freshwater ecosystem in Africa, is characterised by a significant heterogeneity in phytoplankton concentration linked to its particular hydrodynamics. To gather a proper understanding of primary production, it is necessary to consider spatial and temporal dynamics throughout the lake. In the present work, daily MODIS-AQUA satellite measurements were used to estimate chlorophyll-a concentrations and the diffuse attenuation coefficient (K490) for surface waters. The spatial regionalisation of Lake Tanganyika, based on Empirical Orthogonal Functions of the chlorophyll-a dataset (July 2002–November 2005), allowed for the separation of the lake in 11 spatially coherent and co-varying regions, with 2 delocalised coastal regions. Temporal patterns of chlorophyll-a showed significant differences between regions. Estimation of the daily primary production in each region indicates that the dry season is more productive than the wet season in all regions with few exceptions. Whole-lake daily primary productivity calculated on an annual basis (2003) was 646 ± 142 mg C m − 2 day − 1 . Comparing our estimation to previous studies, photosynthetic production in Lake Tanganyika appears to be presently lower (about 15%), which is consistent with other studies which used phytoplankton biovolume and changes of δ 13 C in the lake sediments. The decrease in lake productivity in recent decades may be associated to changes in climate conditions.

Estimation of Chlorophyll-a Concentrations in the Pearl River Estuary Using In Situ Hyperspectral Data: A Case Study

Taking Pearl River Estuary (PRE), China as an example, we explored the potential of in situ hyperspectral data in estimating chlorophyll-a concentrations of turbid waters. Two cruises were conducted on August 21, 2006 and May 18, 2004 to collect the data of water quality and remote sensing reflectance (Rrs). The field surveys showed that: chlorophyll-a concentration ranged from 2.97μg/L to 49.97μg/L, and turbidity 13.6-128.9 NTU. The Rrs spectra were binned to 10 nm resolution, and then processed to be first-order and second-order derivatives. A linear algorithm is developed to estimate chlorophyll-a concentrations based on second-order derivative at 670 nm; its mean relative error of estimation is less than 58%, and the root mean square error is 6.69 μg/L, which is better than other popular algorithms for turbid waters, i.e., the ratio of Rrs at 700 nm and 670 nm. The Case-I algorithm of blue-green band ratio is also proved to be a failed application in PRE, and so does the algorithm of fluorescence line height (FLH), which is questionable for its application in waters with strong light scattering and absorption. All the above work was done without classification of cloud conditions. This suggests that the second-order derivative at 670 nm could be effective for estimation of chlorophyll-a concentrations in turbid waters, especially in situ.

Inversion of chlorophyll-a concentration in turbid water of Lake Taihu based on optimized multi-spectral combination

内陆水体叶绿素a浓度定量反演是水质遥感的热点与难点.本文基于对内陆水体叶绿素a、悬浮物、溶解有机物与水分子的光谱特征分析,从半分析生物光学模型出发,利用太湖实测的水面ASD高光谱遥感数据三波段组合,进行迭代优化,得到与叶绿素浓度密切相关而受悬浮物与黄色物质影响小的最优波段组合模型,反演精度较高,其决定系数和均方根误差分别为0.8358、3.816mg/m³,该方法可以有效地反演高浓度悬浮物主导光学特性的水体叶绿素a浓度.;Inversion of phytoplankton chlorophyll-a concentration of inland water body is hotspot and difficulty problem inwater quality remote sensing. This paper provides a method to resolve this problem. Basing on the characteristic spectralanalysis of chlorophyll-a, suspended matter, chromatic dissolved inorganic matter and pure water molecule in inland waterbody, the three-band model was spectrally tuned in accord with optical properties of Lake Taihu to optimize spectral bandscombination for accurate chlorophyll-a concentration estimation. The remarkable linear relationship was established betweenanalytically measured chlorophyll-a concentration and the three-band model. Depended on the favorable theory basis of themodel developed, this method achieved good result with high determination coefficient 0.8358 and low root-mean-squareerror (3.816 mg/m³), which was proved to be an useful tool to retrieval chlorophyll-a concentration in very turbid, hyper-eutrophicinland waters.

HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters

We present a generic innovative algorithm for remote sensing of coastal waters that can deal with a large range of concentrations of chlorophyll-a, SPM and CDOM and their inherent optical properties. The algorithm is based on the exact solutions of the HYDROLIGHT numerical radiative transfer model to support retrieval in optically complex waters with varying sensor wide swath viewing geometry. The algorithm estimates the concentrations by minimizing the difference between observed and modeled reflectance spectra. The use of a look-up table and polynomial interpolation greatly reduces computation time, allowing operational and near-real time processing of large sets of satellite imagery. Because the remote sensing reflectance was tabulated as a function of in-water light absorption and scattering, rather than actual constituents concentrations, the algorithm can be applied with any definition of the specific inherent optical properties of CHL, SPM and CDOM. A statistical measure for the goodness-of-fit and the formal standard errors in the fitted concentrations are provided, thus producing error maps with each thematic chlorophyll image, often lacking in most applications of innovative algorithms. The performance of the algorithm is demonstrated for multispectral observations of the North Sea, a shallow coastal sea with large concentration gradients in SPM (due to resuspension) and CDOM (from riverine influx). The standard errors of estimated chlorophyll-a concentrations ranged between 0.5 and 3 (mg m − 3 ) for mean concentrations between 2 and 20 (mg m − 3 ), quite acceptable results for these optically complex waters.

Estimation of chlorophyll-A concentration using an artificial neural network (ANN)-based algorithm with oceansat-I OCM data

An artificial neural network (ANN) based chlorophyll-a algorithm was developed to estimate chlorophyll-a concentration using OCEANSAT-I Ocean Colour Monitor (OCM) satellite-data. A multi-layer perceptron (MLP) type neural network was trained using simulated reflectances (~60,000 spectra) with known chlorophyll-a concentration, corresponding to the first five spectral bands of OCM. The correlation coefficient(r2) andRMSE for the log transformed training data was found to be 0.99 and 0.07, respectively. The performance of the developed ANN-based algorithm was tested with the global SeaWiFS Bio-optical Algorithm Mini Workshop (SeaBAM) data (~919 spectra), 0.86 and 0.13 were observed asr2 andRMSE for the test data set. The algorithm was further validated with thein-situ bio-optical data collected in the northeastern Arabian Sea (~215 spectra), ther2 andRMSE were observed as 0.87 and 0.12 for this regional data set. Chlorophyll-a images were generated by applying the weight and bias matrices obtained during the training, on the normalized water leaving radiances (nLW) obtained from the OCM data after atmospheric correction. The chlorophyll-a image generated using ANN based algorithm and global Ocean Chlorophyll-4 (OC4) algorithm was compared. Chlorophyll-a estimated using both the algorithms showed a good correlation for the open ocean regions. However, in the coastal waters the ANN algorithm estimated relatively smaller concentrations, when compared to OC4 estimated chlorophyll-a.

Evaluation of ADEOS-II GLI ocean color atmospheric correction using SIMBADA handheld radiometer data

The performance of the “version 2” Global Imager (GLI) standard atmospheric correction algorithm, which includes empirical absorptive aerosol correction and sun glint correction, was evaluated using data collected with handheld above-water SIMBADA radiometers during 23 cruises of opportunity (research vessels, merchant ships), mostly in the North Atlantic and European seas. A number of 100 match-up data sets of GLI-derived and SIMBADA-measured normalized water-leaving radiance (nLW) and aerosol optical thickness (AOT) were sorted out, using objective selection criteria, and analyzed. The Root-Mean-Square (RMS) difference between GLI and SIMBADA nLW was about 0.32 µW/cm2/nm/sr for the 412 nm band, showing improvement by 30% in RMS difference with respect to the conventional “version 1” GLI atmospheric correction algorithm, and the mean difference (or bias) was reduced significantly. For AOT, the RMS difference was 0.1 between GLI estimates and SIMBADA measurements and the bias was small (a few 0.01), but the Angstrom exponent was systematically underestimated, by 0.4 on average, suggesting a potential GLI calibration offset in the near infrared. The nLW differences were not correlated to AOT, although performance was best in very clear conditions (AOT less than 0.05 in the 865 nm band). Despite the relatively large scatter between estimated and measured nLW, the derived chlorophyll-a concentration estimates, applying the same ratio algorithm (GLI OC4V4) to GLI and SIMBADA, were consistent and highly correlated in the range of 0.05–2 µg/l. The large variability in chlorophyll-a concentration estimate for clear clean water areas (e.g. with the concentration range lower than about 0.05 µg/l) turns out to be due to the nature of the “band ratio” based in-water algorithm.