Medium Resolution Imaging Spectrometer Images Research Articles

Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

A novel interpolation method for InSAR atmospheric wet delay correction

The accuracy and capability of Differential interferometric Synthetic Aperture Radar (DInSAR) depends on the phase errors. In particular, errors associated with Atmospheric Wet Delay (AWD) should be reduced to ensure reliable results from the interferometric process. This paper addresses a new method for AWD estimation based on MEdium Resolution Imaging Spectrometer (MERIS) water vapor image which is used as an auxiliary data to correct AWD effects on ENVIronment SATellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) interferogram. We also explore the possibility of using MERIS data under cloudy conditions and we propose a novel method for the interpolation of water vapor in the presence of clouds using a hybrid technique we name Three Dimensional Inverse Distance Weighted (3D-IDW). It is shown that the proposed method succeeds to provide a quite realistic prediction of MERIS water vapor distribution on cloudy area. Obtained results show that 3D-IDW method succeeds to estimate IWV for each cloudy pixel with RMSE not exceeding 0.094 g/cm2 over area masked by 90% of cloud. The proposed method was tested on Mitidja region (north central Algeria), using a couple of Advanced Synthetic Aperture Radar (ASAR) and MERIS images on the 65 descending track. The results demonstrates an improvement of 15% in the standard deviation of interferogram after ADW correction. Finally, the obtained surface deformation reveals the presence of subsiding districts which may be linked to seasonal water level fluctuation and overdrafting groundwater confirming the results of previous study. Index termsDifferential Interferometry Synthetic Aperture Radar (DInSAR), Atmospheric Wet Delay (AWD), water vapor, interpolation, Inverse Distance Weighted (IDW), MERIS, subsidence.

Algorithm for Recovery of Integrated Water Vapor Content in the Atmosphere over Land Surfaces Based on Satellite Spectroradiometer Data

An algorithm is proposed for making charts of the distribution of water vapor in the atmosphere based on multispectral images of the earth by the Ocean and Land Color Instrument (OLCI) on board of the European research satellite Sentinel-3. The algorithm is based on multiple regression fits of the spectral brightness coefficients at the upper boundary of the atmosphere, the geometric parameters of the satellite location (solar and viewing angles), and the total water vapor content in the atmosphere. A regression equation is derived from experimental data on the variation in the optical characteristics of the atmosphere and underlying surface, together with Monte-Carlo calculations of the radiative transfer characteristics. The equation includes the brightness coefficients in the near IR channels of the OLCI for the absorption bands of water vapor and oxygen, as well as for the transparency windows of the atmosphere. Together these make it possible to eliminate the effect of the reflection spectrum of the underlying surface and air pressure on the accuracy of the measurements. The algorithm is tested using data from a prototype OLCI, the medium resolution imaging spectrometer (MERIS). A sample chart of the distribution of water vapor in the atmosphere over Eastern Europe is constructed without using subsatellite data and digital models of the surface relief. The water vapor contents in the atmosphere determined using MERIS images and data provided by earthbound measurements with the aerosol robotic network (AERONET) are compared with a mean square deviation of 1.24 kg/m2.

A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea

AbstractA new bio‐optical algorithm based on the green and red bands of the Medium Resolution Imaging Spectrometer (MERIS) is developed to differentiate the harmful algal blooms of Prorocentrum donghaiense Lu (P. donghaiense) from diatom blooms in the East China Sea (ECS). Specifically, a novel green‐red index (GRI), actually an indicator for a(510) of bloom waters, is retrieved from a semianalytical bio‐optical model based on the green and red bands of phytoplankton‐absorption and backscattering spectra. In addition, a MERIS‐based diatom index (DIMERIS) is derived by adjusting a Moderate Resolution Imaging Spectroradiometer (MODIS) diatom index algorithm to the MERIS bands. Finally, bloom types are effectively differentiated in the feature spaces of the green‐red index and DIMERIS. Compared with three previous MERIS‐based quasi‐analytical algorithm (QAA) algorithms and three existing classification methods, the proposed GRI and classification method have the best discrimination performance when using the MERIS data. Further validations of the algorithm by using several MERIS image series and near‐concurrent in situ observations indicate that our algorithm yields the best classification accuracy and thus can be used to reliably detect and classify P. donghaiense and diatom blooms in the ECS. This is the first time that the MERIS data have been used to identify bloom types in the ECS. Our algorithm can also be used for the successor of the MERIS, the Ocean and Land Color Instrument, which will aid the long‐term observation of species succession in the ECS.

Estimation of Water Quality Parameters in Lake Erie from MERIS Using Linear Mixed Effect Models

Linear Mixed Effect (LME) models are applied to the CoastColour atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) reflectance, L2R full resolution product, to derive chlorophyll-a (chl-a) concentration and Secchi disk depth (SDD) in Lake Erie, which is considered as a Case II water (i.e., turbid and productive). A LME model considers the correlation that exists in the field measurements which have been performed repeatedly in space and time. In this study, models are developed based on the relation between the logarithmic scale of the water quality parameters and band ratios: B07:665 nm to B09:708.75 nm for log10chl-a and B06:620 nm to B04:510 nm for log10SDD. Cross validation is performed on the models. The results show good performance of the models, with Root Mean Square Errors (RMSE) and Mean Bias Errors (MBE) of 0.31 and 0.018 for log10chl-a, and 0.19 and 0.006 for log10SDD, respectively. The models are then applied to a time series of MERIS images acquired over Lake Erie from 2004–2012 to investigate the spatial and temporal variations of the water quality parameters. Produced maps reveal distinct monthly patterns for different regions of Lake Erie that are in agreement with known biogeochemical properties of the lake. The Detroit River and Maumee River carry sediments and nutrients to the shallow western basin. Hence, the shallow western basin of Lake Erie experiences the most intense algal blooms and the highest turbidity compared to the other sections of the lake. Maumee Bay, Sandusky Bay, Rondeau Bay and Long Point Bay are estimated to have prolonged intense algal bloom.

Assessment of remotely sensed chlorophyll- a concentration in Guanabara Bay, Brazil

The Guanabara Bay (GB) is an estuarine system in the metropolitan region of Rio de Janeiro (Brazil), with a surface area of ∼346 km2 threatened by anthropogenic pressure. Remote sensing can provide frequent data for studies and monitoring of water quality parameters, such as chlorophyll-a concentration (Chl-a). Different combination of Medium Resolution Imaging Spectrometer (MERIS) remote sensing reflectance band ratios were used to estimate Chl-a. Standard algorithms such as Ocean Color 3-band, Ocean Color-4 band, fluorescence line height, and maximum chlorophyll index were also tested. The MERIS Chl-a estimates were statistically compared with a dataset of in situChl-a (2002 to 2012). Good correlations were obtained with the use of green, red, and near-infrared bands. The best performing algorithm was based on the red (665 nm) and green (560 nm) band ratio, named “RG3” algorithm (r2=0.71, chl-a=62,565*x1.6118). The RG3 was applied to a time series of MERIS images (2003- to 2012). The GB has a high temporal and spatial variability of Chl-a, with highest values found in the wet season (October to March) and in some of the most internal regions of the estuary. Lowest concentrations are found in the central circulation channel due to the flushing of ocean water masses promoted by pumping tide.

COMPARISON OF C2WP BOUREAL LAKES PROCESSOR AND REGIONAL WATER PROCESSOR ALGORITHM ON EXTRACTING ESTIMATED TOTAL SUSPENDED SOLID DATA OF LAKE SENTANI

Lake Sentani is one of fifteen Indonesian National Priority Lakes. In the Jurnal Biologi Papua, Surbakti (2011) said that Lake Sentani is an euthropic lake. MEdium Resolution Imaging Spectrometer (MERIS) is one of a few sensors at Envisat Satellite which is used to monitor water-quality conditions. At BEAM VISAT, MERIS image can be processed using 3 algorithms to extract water-quality. Those algorithms are Case-2 Water Processor (C2WP) Euthropic Lakes, Case-2 Water Processor (C2WP) Boureal Lakes, and Regional Water Processor Water Processor (WP). In this research, we compared the performance of C2WP Boureal and Regional WP on extracting Total Suspended Solid (TSS) data. Extracting TSS data using C2WP Euthropic were used as data validation because Lake Sentani was an euthropic lake.

Towards a practical remote-sensing model of suspended sediment concentrations in turbid waters using MERIS measurements

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.

An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense—the radiance and the spectrum—the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS.

An approach for estimating absorption and backscattering coefficients from MERIS in the Bohai Sea

Distribution of absorption and backscattering coefficients (a(560) and bb(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAABS) was developed for estimating a(560) and bb(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAABS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and bb(550), respectively. The SAABS model was also used to retrieve a(560) and bb(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and bb(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAABS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.

MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009

The Medium Resolution Imaging Spectrometer (MERIS) was used to investigate the spatial and temporal dynamics of chlorophyll-a (chl-a) in Erhai Lake, the second largest freshwater lake in the Yunnan province of China. Six chl-a retrieval models, including four Basic ERS & Envisat (A)ATSR and Meris Toolbox (BEAM) software-incorporated algorithms and MERIS three-band and two-band models, were validated to find the best fit to extract chl-a concentration in Erhai Lake. With a chl-a range of 5–15 mg m–3, the Lakes/Eutrophic method showed the best performance. The algorithm was then applied to eight-year cloud-free MERIS images between 2003 and 2009, with seasonal and inter-annual variability analysed. Long-term chl-a distributions of Erhai Lake revealed significant seasonal and inter-annual variability. The mean chl-a of the south lake was higher in summer (14.3 mg m–3) than in spring (10.1 mg m–3), while generally lower chl-a was found in the north lake with a mean chl-a of 6.4 mg m–3 in spring and 9.0 mg m–3 in summer, respectively. An increasing trend was found between 2006 and 2009, and the increasing rate was 12.9% for annual chl-a of the entire lake. While chl-a seasonality was attributed to the seasonal changes of the local temperature, the inter-annual variation was possibly linked to the discharged wastewater from Dali City. This work could provide critical information for decision-makers to manage Erhai Lake’s aquatic ecosystems.

Analysis of natural background and dredging-induced changes in TSM concentration from MERIS images near commercial harbours in the Estonian coastal sea

Medium Resolution Imaging Spectrometer (MERIS) products with 300 m resolution from 2006 to 2011 were used to evaluate the local background of total suspended matter (TSM) in the vicinity of commercial harbours located along the Estonian coastline in the Baltic Sea. The difference between background TSM maps (mainly influenced by spring bloom, cyanobacterial bloom, resuspension, and river inflow) and dredging period mean maps was used for the estimation of dredging-induced turbidity at the time of dredging operations. Validation of Case II Regional (C2 R) and Free University of Berlin (FUB) MERIS processors with point measurements showed that both processors represent the changes in TSM concentration adequately. C2 R processors showed better statistics (R2 = 0.61, root mean square error = 0.82 mg l–1, SD = 0.77 mg l–1, mean bias = –0.28 mg l–1) compared to the FUB processor. Analysis of monthly mean TSM maps revealed that the variability of TSM concentration, showing the resilience level of the local ecosystem, is very different along the Estonian coastline – varying between 0.75 and 2.60 mg l–1 near the Port of Tallinn, located in the Gulf of Finland, and between 10.04 and 24.23 mg l–1 near the Port of Pärnu, located in the Gulf of Riga. The viability of the method for dredging impact detection was tested by evaluating the dredging-induced turbidity on monthly mean TSM maps for the dredging period in autumn 2008 in Pakri Bay, which is an environmentally sensitive area. A threshold TSM concentration value of >2.26 mg l–1 difference from background TSM was defined as a criterion for dredging impact detection for Pakri Bay. The area of dredging-induced turbidity was between 0.56 and 1.25 km2 and did not reach the environmentally sensitive NATURA 2000 region adjoining Paldiski South Harbour.

Lake topography and wind waves determining seasonal-spatial dynamics of total suspended matter in turbid Lake Taihu, China: assessment using long-term high-resolution MERIS data.

Multiple comprehensive in situ bio-optical investigations were conducted from 2005 to 2010 and covered a large variability of total suspended matter (TSM) in Lake Taihu to calibrate and validate a TSM concentration estimation model based on Medium Resolution Imaging Spectrometer (MERIS) data. The estimation model of the TSM concentration in Lake Taihu was developed using top-of-atmosphere (TOA) radiance of MERIS image data at band 9 in combination with a regional empirical atmospheric correction model, which was strongly correlated with the in situ TSM concentration (r 2 = 0.720, p<0.001, and n = 73). The relative root mean square error (RRMSE) and mean relative error (MRE) were 36.9% and 31.6%, respectively, based on an independent validation dataset that produced reliable estimations of the TSM concentration. The developed algorithm was applied to 50 MERIS images from 2003 to 2011 to obtain a high spatial and temporal heterogeneity of TSM concentrations in Lake Taihu. Seasonally, the highest and lowest TSM concentrations were found in spring and autumn, respectively. Spatially, TSM concentrations were high in the southern part and center of the lake and low in Xukou Bay, East Lake Taihu. The lake topography, including the water depth and distance from the shore, had a significant effect on the TSM spatial distribution. A significant correlation was found between the daily average wind speed and TSM concentration (r 2 = 0.685, p<0.001, and n = 50), suggesting a critical role of wind speed in the TSM variations in Lake Taihu. In addition, a low TSM concentration was linked to the appearance of submerged aquatic vegetation (SAV). Therefore, TSM dynamics were controlled by the lake topography, wind-driven sediment resuspension and SAV distribution.

Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data

Freshwater resources are deteriorating rapidly due to human activities and climate change. Remote sensing techniques have shown potential for monitoring water quality in shallow inland lakes, especially in data-scarce areas. The purpose of this study was to determine the spectral diffuse attenuation coefficient (Kd(λ)) of the water column, in order to map the euphotic depth (Zeu) of Lake Naivasha, Kenya using the Medium Resolution Imaging Spectrometer (MERIS). Intensive in situ radiometric and limnological data collection was undertaken at Lake Naivasha. Atmospheric correction was done on the MERIS images using MERIS Neural Network algorithms, Case 2 Waters (C2R) and Eutrophic Lakes processors and the bright pixel atmospheric correction algorithm (BPAC). The Eutrophic Lakes processor gave the most accurate atmospherically corrected remote sensing reflectances at 490nm compared to the other processors, with mean absolute percentage error (MAPE) of 47% and a root mean square error (RMSE) 43%, with BPAC giving negative reflectances in the blue spectral range. In situ Kd and Zeu models were calibrated and validated using above- and under-water radiometric measurements, and tested on the Neural Network atmospheric correction processed MERIS images. The Eutrophic Lakes estimates were the most accurate, with an RMSE of 0.26m−1 and MAPE of 18% for Kd(490) and RMSE of 0.17m and MAPE of 14% for Zeu. The Zeu maps produced from MERIS images clearly show the variation of euphotic depth both in space and time. These results indicate the suitability of MERIS to monitor euphotic depth and other water quality parameters of shallow inland water bodies.

Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data

Especially for a shallow and extremely turbid lake, light availability in the water column can determine the euphotic zone, and the horizontal and vertical distribution of algae species. Light attenuation is traditionally quantified as the diffuse attenuation coefficient of the photosynthetically available radiation (Kd(PAR)). Global coverage of Kd(PAR) at high spatial and temporal resolution can be provided by satellite measurements, and these data can be used to improve our understanding of the physical, chemical and biological processes in Lake Taihu, a large shallow lake in China, of considerable importance as a source of drinking water for several large cities.The primary dominated contributor to Kd(PAR) was determined firstly using linear regression. There was a significant positive correlation between Kd(PAR) and concentrations of total suspended matter (TSM). The determination coefficient between Kd(PAR) and TSM (R2=0.91) was significantly higher than that between Kd(PAR) and chlorophyll-a concentrations (Chl-a) (R2=0.11), and between Kd(PAR) and absorption of chromophoric dissolved organic matter (CDOM) (R2=0.01). Our results therefore could demonstrate that TSM usually plays a dominant role in the attenuation of light in Lake Taihu.A retrieval model of Kd(PAR) values for Lake Taihu was subsequently developed using top-of-atmosphere (TOA) radiance of Medium Resolution Imaging Spectrometer (MERIS) image data at band 10, which was strongly correlated with in situ Kd(PAR) (R2=0.74, p<0.005, N=48). In contrast, the atmospheric corrected reflectance of MERIS image data was not strongly correlated with in situ Kd(PAR). Thus, atmospheric correction was not a prerequisite for estimations of Kd(PAR) in this highly turbid and hyper-eutrophic lake, and a simple empirical model of the Kd(PAR) estimation for Lake Taihu was effective.With the simple model, the seasonal and spatial distributions of Kd(PAR) in Lake Taihu were studied using the MERIS measurements from 2003 to 2010. Our results show distinct seasonal, spatial, and wind driven, Kd(PAR) values in Lake Taihu. The highest and the lowest Kd(PAR) values were found in summer and in winter, respectively. The increase of Kd(PAR) in summer can be attributed to the phytoplankton blooms, and wind-driven sediment resuspension. Spatially, the Kd(PAR) values were high in the southern part and the lake center, and low Kd(PAR) in East Lake Taihu. Based on the MERIS derived Kd(PAR) values, a significant correlation was found between Kd(PAR) and wind speeds, suggesting a critical role of wind speeds in the Kd(PAR) variations in Lake Taihu.

Seasonality of microphytobenthos revealed by remote-sensing in a South European estuary

The spatio-temporal variation of microphytobenthos (MPB) at the scale of a large estuary (Tagus estuary, Portugal) was studied using a combination of field and satellite remote sensing data during 2003. This is the first attempt to use remote sensing to study MPB in an ecosystem with a Mediterranean-like climate. Satellite pour l'Observation de la Terre (SPOT) and Medium Resolution Imaging Spectrometer (MERIS) images were used to map benthic microalgae through the application of a Normalized Difference Vegetation index (NDVI). A significant relationship between in-situ benthic chlorophyll a measurements and SPOT NDVI values was used to derive a map for biomass spatial distribution.At the scale of the whole intertidal area, NDVI time-series from 2003 revealed that MPB showed clear temporal variations, with lower values observed in summer compared to winter. This seasonal trend was found both in the SPOT and MERIS images and may be the result of extreme high temperatures that inhibit MPB growth. The main MPB biofilms were spatially stable through time at a large scale. Maximum NDVI values during the winter were found in the high shore with decreasing NDVI values towards the low shore. MPB light limitation at the lowest bathymetries is likely to occur in winter due to the high turbidity of Tagus estuary.The biomass spatial distribution map, obtained for January 2003, indicated low values ranging from 0 to 20mgChl am−2 for the lower shores, while in the upper shore biomass varied between 60 and 80mg Chlam−2. This study suggests striking differences in MPB seasonal patterns between the northern and southern European estuaries and stresses the need for ecophysiological approaches to investigate the role of thermo- and photo-inhibition as structuring factors for MPB biomass distribution.

An intercomparison of Satellite Burned Area Maps derived from MODIS, MERIS, SPOT-VEGETATION, and ATSR images. An application to the August 2006 Galicia (Spain) forest fires

Aim of study: The following paper presents an inter-comparison of three global products: MCD45A1 (MODIS - MODerate resolution Imaging Spectrometer - Burned Area Product), L3JRC (Terrestrial Ecosystem Monitoring Global Burnt Area Product), and GLOBCARBON Burnt Area Estimate (BAE) Product; and three local products, two of them based on MODIS data and the other one based on MERIS (MEdium Resolution Imaging Spectrometer) data.Area of study: The study was applied to the Galician forest fires occurred in 2006.Materials and Methods: Materials used involved the three already mentioned global products together with two MODIS and one MERIS reflectance images, and MODIS thermal anomalies. The algorithm we used, which is based on the determination of thresholds values on infrared bands, allowed the identification of burned pixels. The determination of such threshold values was based on the maximum spatial correlation between MODIS thermal anomalies, and infrared reflectance values. This methodology was applied to MODIS and MERIS reflectance bands, and to the NBR (Normalized Burn Ratio). Burned area validation was evaluated using burned area polygons as derived from an AWiFS (Advanced Wide Field Sensor) image of 60m pixel size.Main results: Best results were reached when using the MERIS infrared bands, followed by the MODIS infrared bands. Worst results were reached when using the MCD45A1 product, which clearly overestimated; and when using the L3JRC product, which clearly underestimated.Research highlights: Since the efficiency of the performance of the available burned area products is highly variable, much work is needed in terms of comparison among the available sensors, the burned area mapping algorithms and the resulting products.Keywords: forest fires; MODIS; MERIS; MCD45A1; L3JRC; GLOBCARBON-BAE; SPOT-VEGETATION; ATSR.Abbreviations used: ATSR: Along Scanning Radiometer; AVHRR: Advanced Very High Resolution Radiometer; AWiFS: Advanced Wide Field Sensor; EOS: Earth Observation System; ESA: European Space Agency; GBA2000: Global Burnt Area 2000; GLOBCARBON-BAE: GLOBCARBON Burnt Area Estimate Product; L3JRC: Terrestrial Ecosystem Monitoring Global Burnt Area Product; MCD45A1: MODIS Burned Area Product; MERIS: MEdium Resolution Imaging Spectrometer; MOD09GA: Terra MODIS Surface Reflectance Daily L2G Global 500 m; MOD09GQ: Terra MODIS Surface Reflectance Daily L2G Global 250 m; MODIS: MODerate resolution Imaging Spectrometer; NBR: Normalized Burn Ratio; NDVI: Normalized Difference Vegetation Index; NIR: near-infrared; SPOT: Satellite Pour l’Observation de la Terre; SWIR: short-wave infrared; UTM: Universal Transverse Mercator.

Remote sensing retrieval of total absorption coefficient in the Bohai Sea

Temporal and spatial patterns of inherent optical properties in the Bohai Sea are very complex. In this paper, we used 77 groups of field data of AOPs (apparent optical properties) and IOPs (inherent optical properties) collected in June, August, and September of 2005 in the Bohai Sea, to retrieve the spectral total absorption coefficient a(λ) with the quasi-analytical algorithm (QAA). For QAA implementation, different bands in the region 680–730 nm (in 5 nm intervals) were selected and compared, to determine the optimal band domain of the reference wavelength. On this basis, we proposed a new algorithm (QAA-Com), a combination of QAA-685 and QAA-715, according to turbidity characterized by a(440). The percentage difference of model retrievals in the visible domain was between 4.5%–45.1%, in average of 18.8% for a(λ). The QAA model was then applied to Medium Resolution Imaging Spectrometer (MERIS) radiometric products, which were temporally and spatially matched with in-situ optical measurements. Differences between MERIS retrievals and in-situ values were in the range 9.2%–27.8% for a(λ) in the visible domain. Major errors in satellite retrieval are attributable to uncertainties of QAA model parameters and in-situ measurements, as well as imperfect atmospheric correction of MERIS data by the European Space Agency (ESA). During a storm surge in April 2009, time series of MERIS images together with the QAA model were used to analyze spatial and temporal variability of the total absorption coefficient pattern in the Bohai Sea. It is necessary to collect more independent field data to improve this algorithm.

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

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

Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll- a concentration in turbid productive waters

We propose a normalized difference chlorophyll index (NDCI) to predict chlorophyll-a (chl- a) concentration from remote sensing data in estuarine and coastal turbid productive (case 2) waters. NDCI calibration and validation results derived from simulated and MEdium Resolution Imaging Spectrometer (MERIS) datasets show its potential application to widely varying water types and geographic regions. A quadratic function ( R 2 = 0.95, p < 0.0001) accurately explained the variance in the simulated data for a chl- a range of 1–60 mg m − 3 . Similarly a twofold calibration and validation of chl- a models using MERIS dataset, (chl- a range: 0.9–28.1 mg m − 3 ) yielded R 2 of 0.9, and RMSE of ~ 2 mg m − 3 respectively. NDCI was applied on images over the Chesapeake Bay and Delaware Bay, the Mobile Bay, and the Mississippi River delta region in the northern Gulf of Mexico. The newly developed algorithm was successful in predicting chl- a concentration with approximately 12% overall bias for all above study regions. Findings from this research imply that NDCI can be successfully used on MERIS images to quantitatively monitor chl- a in inland coastal and estuarine waters. In case of remote coastal waters with no ground truth data, NDCI can be used to detect algal bloom and qualitatively infer chl- a concentration ranges very similar to NDVI's application in terrestrial vegetation studies.