Mapping inland water carbon content with Landsat 8 data

Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.     

Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements

Landsat TM data and field spectral measurements were used to evaluate chlorophyll‐a (Chl‐a) concentration levels and trophic states for three inland lakes in Northeast China. Chl‐a levels were estimated applying regression analysis in the study. The results obtained from the field reflectance spectra indicate that the ratio between the reflectance peak at 700 nm and the reflectance minimum at 670 nm provides a relatively stable correlation with Chl‐a concentration. Their determination of coefficients R ² is 0.69 for three lakes in the area. From Landsat TM data, the results show that the most successful Chl‐a was estimated from TM3/TM2 with R ² = 0.63 for the two lakes on 26 July 2004, from TM4/TM3 with R ² = 0.89 for the two lakes on 14 October 2004, and from the average of TM2, TM3 and TM4 with R ² = 0.72 for the three lakes tested on 13 July 2005. These results are applicable to estimate Chl‐a from satellite‐based observations in the area. We also evaluate the trophic states of the three lakes in the region by employing Shu's modified trophic state index (TSI_M) for the Chinese lakes' eutrophication assessment. Our study presents the TSI_M from different TM data with R ² more than 0.73. The study shows that satellite observations are effectively applied to estimate Chl‐a levels and trophic states for inland lakes in the area.     

Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land

An innovative method for the determination of aerosol optical thickness (AOT) and surface reflectance for operational use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible to near-infrared data is presented. This method is designed to obtain the atmospheric parameters needed in the correction of the image. This method is based on a simplified radiative transfer equation describing the relation between the ground surface reflectance, AOT and top-of-atmosphere reflectance. By exploiting the ASTER dual-angle view capabilities in band 3N (Nadir) and band 3B (Backwards), surface reflectance and AOT can be retrieved synchronously. Thus, it solves the problem of separating atmospheric radiance from the transmitted radiance of the surface to some extent. After applying this new atmospheric correction method to three areas of ASTER images, Beijing urban city, the Heihe River Basin and Hong Kong of China, ASTER surface reflectance products (AST07) were obtained. AOT values from in situ measurements of CIMEL Electronique 318 Sun Photometers or AERONET (AErosol RObotic NETwork) and surface reflectance in situ measured using an Analytical Spectral Device (ASD) Field Spec spectral radiometer are used for validation. AOT derived from the new method is consistent with in situ station measurements from CIMEL Electronique 318 Sun Photometer and level 2.0 data from AERONET, with correlation coefficient (R ²) of 0.98 and root mean square error of 0.05, whereas Multi-angle Imaging Spectroradiometer AOT products underestimate AERONET AOT and Moderate-Resolution Imaging Spectroradiometer AOT products overestimate AERONET AOT in these regions. More encouraging is the comparison between the corrected surface reflectance, AST07 and ASD measurements. Root mean square error of AST07 and retrieved surface reflectance are as follows: band 1 (556 nm) = 0.04 and 0.05; band 2 (661 nm) = 0.036 and 0.035; band 3 (807 nm) = 0.056 and 0.038, which suggests that compared with AST07 in bands 2 and 3, retrieved surface reflectance has better agreement with measured reflectance from ASD.     

Effect of optically active substances and atmospheric correction schemes on remote-sensing reflectance at a coastal site off Kochi

The present study focused on understanding the variability of optically active substances (OASs) and their effect on spectral remote-sensing reflectance (R_rs). Furthermore, the effect of atmospheric correction schemes on the retrieval of chlorophyll-a (chl-a) from satellite data was also analysed. The OASs considered here are chl-a, coloured dissolved organic matter (CDOM), and total suspended matter (TSM). Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite was used for this study. The two atmospheric correction schemes considered were: multi-scattering with two-band model selection NIR correction (hereon referred as ‘A1’) and Management Unit of the North Sea Mathematical Models (MUMM) correction and MUMM NIR calculation (hereafter referred as ‘A2’). The default MODIS bio-optical algorithm (OC3M) was used for the retrieval of chl-a. Analysis of OASs showed that chl-a was the major light-absorbing component, with highly variable distribution (0.006–25.85 mg m^–3). Absorption due to CDOM at 440 nm (a_CDOM440) varied from 0.002 to 0.31 m^–1 whereas TSM varied from 0.005 to 33.44 mg l^–1. The highest concentration of chl-a was observed from August to November (i.e. end of the southwest monsoon and beginning of the northeast monsoon), which was attributed to coastal upwelling. The average value of a_CDOM440 was found to be lower than the global mean. A significant negative relationship between a_CDOM440 and salinity during the southwest monsoon indicated that much of the CDOM during this season was derived from river discharge. Spectral R_rs was found to be strongly linked to the variability in chl-a concentration, indicating that chl-a was the major light-absorbing component. Satellite-derived spectral R_rs was in good agreement with that in situ when chl-a concentration was lower than 5 mg m^–3. The validation of chl-a, derived from in situ R_rs, showed moderate performance (correlation coefficient, R² = 0.64; log₁₀(RMSE) = 0.434; absolute percentage difference (APD) = 43.6% and relative percentage difference (RPD) = 42.33%). However the accuracy of the algorithm was still within acceptable limits. The statistical analysis for atmospheric correction schemes showed improved mean ratio of measured to estimated chl-a (‘r’ = 1.6), log₁₀(RMSE) (0.49), APD (25.46%), and RPD (17.57%) in the case of A1 as compared with A2, whereas in the case of A2, R² (0.56), slope (0.26), and intercept (0.27) were better as compared with A1. The two atmospheric correction schemes did not show any significant statistical difference. However the default atmospheric correction scheme (A1) was found to be performing comparatively better probably due to the fact that the concentration of TSM and CDOM was much lower to overcome the impact of chl-a.     

Satellite-based water quality monitoring in Lake Vänern,Sweden

Lake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (R_rs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.

Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.

The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (Abs_F(420)) and turbidity as Abs_F(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.

Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, Abs_F(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l^?1, 0.17 m^?1, and 0.32 mg l^?1 in absolute values.

The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

Evaluation of atmospheric correction and high-resolution processing on SeaDAS-derived chlorophyll-a: an example from mid-latitude mesotrophic waters

Over the last 15 years, great effort has gone into the development of chlorophyll-a (chl-a) retrieval algorithms for case 2 waters, where variations in the water leaving radiance signal are not well correlated with concentrations of chl-a. In this study, we investigate the effectiveness of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived chl-a retrieval algorithms in the less productive coastal waters around Tasmania, Australia. Algorithms were evaluated using matches between satellite imagery and in-situ water samples (number of samples, n = 16–65) derived from a 604 sample data set collected over a 9-year period. Three aerosol correction models and three chl-a retrieval algorithms were evaluated using both standard and high-resolution processing procedures using the National Aeronatics and Space Adminstration’s SeaDAS software package. chl-a retrievals were evaluated in Bass Strait, where in-situ chl-a was less than 1 mg m^?3 and retrievals were less affected by coloured dissolved organic matter. chlor_a, the default SeaDAS chl-a product, with the Management unit of the North Sea Mathematical models aerosol correction algorithm performed best (root mean square error (RMSE) = 0.09 mg m^?3; mean absolute percentage error (MAPE) = 34%; coefficient of determination, R² = 0.75). The fluorescence line height algorithm using Rayleigh corrected top of atmosphere reflectances (RMSE = 0.11 mg m^?3, MAPE = 41%, R² = 0.61) may provide an alternative in waters where full atmospheric correction is problematic and the two-band red/near-infrared algorithm failed to provide a meaningful estimate of chl-a. High-resolution processing of MODIS imagery improved spatial resolution but reduced chl-a retrieval accuracy, reducing the agreement between measured and predicted levels by between 12% and 25% depending on the retrieval algorithm. The SeaDAS default chlor_a product proved superior to the alternatives in mid-latitude mesotrophic coastal waters with low chl-a concentrations. In addition, there appears little benefit in using MODIS high-resolution processing mode for chl-a retrievals.     

Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate

The maximum carboxylation rate (V_cmax) is a key photosynthetic parameter that is determined by the leaf biochemistry and environmental conditions. Numerous studies have shown that plant biochemical, physiological and structural parameters can be estimated from reflectance spectra. Therefore, it is reasonable to assume that V_cmax can be spectrally determined. Here, we investigate the potential of leaf reflectance spectra for retrieving the maximum carboxylation rate of leaves. Measurements of leaf reflectance, carbon dioxide (CO₂) response curves, leaf chlorophyll-a + b (chl-a + b) etc., were made on 80 crop, shrub and tree leaves. Then, the leaf V_cmax,25 was linked to leaf biochemistry and spectral reflectance. A reliable relationship, with a coefficient of determination (R²) value of 0.75, was found between the leaf chl-a + b content and V_cmax,25. The leaf V_cmax,25 values were also significantly correlated with chl-a + b-sensitive spectral indices with the highest R² value that was found being 0.83 for the ratio spectral index (RSI) using reflectances at 1089 nm and 695 nm. Finally, multiple stepwise regression (MSR) and a partial least-squares regression (PLSR) modelling approach were used to estimate V_cmax,25 from leaf reflectances. The results confirmed that V_cmax,25 can be reliably estimated from leaf reflectance spectra and give an R² value >0.80. These findings show that leaf chl-a + b can be used as a proxy for leaf V_cmax,25 and that leaf V_cmax,25 can be spectrally determined using leaf reflectance data.     

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.     

Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

The Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m^?2 µm^?1 sr^?1) data showed low correlation (coefficient of determination: R² ~0.46) with near-concurrent in situ Chl-a (mg m^?3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m^?3 or >2 mg m^?3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, ?0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R² of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.     

The integrated radiometric correction of optical remote sensing imageries

Abstract Environmental analysis, management and modelling require detailed and precise land‐use/land‐cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image‐based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A‐factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non‐corrected data.     

Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction

ABSTRACT

Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (EL_m) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an EL_m technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an EL_m technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and EL_m) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R²), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R² ≥ 0.75, RPD ≥ 2, and RPIQ ≥ 2.58 than single algorithms. HAC algorithms, developed using EL_m technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping.     

Using multiple radiometric correction images to estimate leaf area index

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R ² of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.     

Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.     

Validation of chlorophyll-α concentration maps from Aqua MODIS over the Gulf of Gabes (Tunisia): comparison between MedOC3 and OC3M bio-optical algorithms

Few studies have focused on the use of ocean colour remote sensors in the Gulf of Gabes (southeastern Tunisia). This work is the first study to evaluate the ocean colour chlorophyll-a product in this area. Chlorophyll-a concentrations were measured during oceanographic cruises performed off the Gulf of Gabes. These measurements were used to validate satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. First, two atmospheric correction procedures (standard and shortwave infrared) were tested to derive the remote-sensing reflectance, and then a comparison between two bio-optical (OC3M and MedOC3) algorithms were realized using the in situ measurements. Both atmospheric correction procedures gave similar results when applied to our study area indicating that most pixels were non-turbid. The comparison between bio-optical algorithms shows that using the regional bio-optical algorithm MedOC3 improves chlorophyll-a estimation in the Gulf of Gabes for the low values of this parameter.     

Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir

This research estimates phytoplankton pigment concentrations (chlorophyll‐a (chl‐a) and phycocyanin (PC)) from hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery. AISA images were acquired for a meso‐eutrophic reservoir in Central Indiana, USA. Concurrent with the airborne image acquisition, in situ water samples and reflectances were collected. The water samples were subsequently analysed for pigment concentrations, and in situ measured reflectance spectra were used for calibrating the AISA images. Spectral indices, derived from the AISA reflectance spectra, were regressed against the measured pigment concentrations to derive algorithms for estimating chl‐a and PC. The relationship between the pigment concentrations and the spectral indices were analysed and evaluated. The results indicate that the highest correlation occurred between chl‐a and a near‐infrared to red ratio (coefficient of determination R ²?=?0.78) and between PC and the reflectance trough at 628 nm (R ²?=?0.80). The relationship between PC and the reflectance at 628 nm provides an approach to the estimation of cyanobacteria concentration from hyperspectral imagery, which facilitates water‐quality authorities or management agencies in making well‐informed management decisions.     

Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura,Japan

Accurate atmospheric correction for turbid inland waters remains a significant challenge. Several atmospheric correction algorithms have been proposed to address this issue, but their performance is unclear in regard to Asian lakes, some of which have extremely high turbidity and different inherent optical properties from lakes in other continents. Here, four existing atmospheric correction algorithms were tested in Lake Kasumigaura, Japan (an extremely turbid inland lake), using in situ water-leaving reflectance and concurrently acquired medium resolution imaging spectrometer (MERIS) images. The four algorithms are (1) GWI (the standard Gordon and Wang algorithm with an iterative process and a bio-optical model) (2) MUMM (Management Unit of the North Sea Mathematical Models); (3) SCAPE-M (Self-Contained Atmospheric Parameters Estimation for MERIS Data) and (4) C2WP (Case-2 Water Processor). The results show that all four atmospheric correction algorithms have limitations in Lake Kasumigaura, even though SCAPE-M and MUMM gave acceptable accuracy for atmospheric correction in several cases (relative errors less than 30% for the 2006 and 2008 images). The poor performance occurred because the conditions in Lake Kasumigaura (i.e. the atmospheric state and/or turbidity) did not always meet the assumptions in each atmospheric correction algorithm (e.g. in 2010, the relative errors ranged from 42% to 83%). These results indicate that further improvements are necessary to address the issue of atmospheric correction for turbid inland waters such as Lake Kasumigaura, Japan.     

Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l^–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.     

Contrasting bio-optical characteristics of coastal water prior to and in the aftermath of a tropical super cyclone

Apparent and inherent optical properties were measured in the coastal region off Visakhapatnam (Bay of Bengal) during pre-monsoon season (January–May) of successive years, in-between of which the (October 2014) Hudhud super cyclone had landfall across. For 5 months after the cyclone in the region up to 50 m isobath, or 15 km from the coast and 6 months after the cyclone in the region up to 30 m isobath, or 5 km away, the particle back-scattering coefficient (b_bp) and remote-sensing reflectance (R_rs) were anomalously higher throughout the spectral region (400–700 nm) than during other times. The b_bp spectral slope coefficient γ is abnormally higher than predicted from the b_bp(680)–γ model relationship, and R_rs(685) is also abnormally higher than the significant R_rs(685)–chlorophyll-a (chl-a) relationship would predict. These exceptional properties are attributed to the presence of little absorbing and highly reflecting sub-micrometre-sized particles in fine suspension that leached from the coastal deposits emplaced in the study region from the nearby harbour during the cyclone. The optical properties recovered subsequently. In the period when the reflectance and scattering spectra are not recovered, the R_rs(685) is no longer a proxy of chl-a. Remote-sensing algorithms for phytoplankton abundance of the coastal region during this period need correction.     

Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha,Kenya: remote-sensing approach

Restoration of the ecosystem services and functions of lakes requires an understanding of the turbidity dynamics in order to arrive at informed environmental management decisions. The understanding of the spatio-temporal dynamics of turbidity requires frequent monitoring of the turbidity components such as chlorophyll-a concentration. In this study, we explored the use of Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) satellite data in studying the spatio-temporal changes in chlorophyll-a concentration in Lake Naivasha, a turbid tropical system. The temporal trend of chlorophyll-a concentration over the study period in the lake was also evaluated. The temporal trend assessment was achieved through the removal of periodic seasonal interference using Seasonal-Trend decomposition based on the LOESS (Local Regression) procedure. The resultant chlorophyll-a concentration maps derived from MODIS-Aqua satellite data give an indication of the monthly spatial variation in chlorophyll-a concentration from 2002 to 2012. The results of regression analyses between satellite-derived chlorophyll-a and in situ measurements reveal a high level of precision, but with a measureable bias with the satellite underestimating actual in situ measurements (R² = 0.65, P < 0.001). Although the actual values of the chlorophyll-a concentrations are underestimated, the significant relationship between satellite-derived chlorophyll-a and in situ measurements provides reliable information for studying spatial variations and temporal trends. In 2009 and 2010, it was difficult to detect chlorophyll-a from the MODIS-Aqua imagery, and this coincided with a period of the lowest water levels in Lake Naivasha. An inverse relationship between de-seasoned water level and chlorophyll-a concentration was evident. This study shows that MODIS-Aqua satellite data provide useful information on the spatio-temporal variations in Lake Naivasha, which is useful in establishing general trends that are more difficult to determine through routine ground measurements.