Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean,China

In this work, five ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite (Terra MODIS), Moderate Resolution Imaging Spectroradiometer aboard the Aqua satellite (Aqua MODIS), Medium Range Imaging Spectrometer aboard the Environmental Satellite (Envisat MERIS), Medium Resolution Spectral Imager aboard the FY-3 satellite (FY-3 MERSI), and Geostationary Ocean Colour Imager (GOCI), were selected to examine the compatibility of an algorithm proposed for suspended particulate matter (SPM) retrieval and concordance of satellite products retrieved from different ocean-colour sensors. The results could effectively increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off-orbit. Using in situ measurements from 17 cruise campaigns between 2004 and 2012, the SPM retrieval algorithm was recalibrated so as to be universal and adapted for multi-sensor retrievals. An inter-comparison of multi-sensor-derived products showed that GOCI-derived SPM and Envisat MERIS-derived SPM had the best fitting on a 1:1 scatterplot, with a statistic regression slope of 0.9617 and an intercept of 0.0041 (in units of g l^–1), respectively. SPM products derived from three sensors with nearly synchronous transit, Envisat MERIS, Terra MODIS, and FY-3 MERSI, exhibited excellent accordance with mean differences of 0.056, 0.057, and 0.013 g l^–1 in three field fixed stations, respectively, in the Yangtze estuary. Terra MODIS-derived SPM with GOCI-derived SPM, except in the high SPM waters of Hangzhou Bay, and Aqua MODIS-derived SPM with GOCI-derived SPM, except in the moderate SPM waters of the South Branch and south of the Subei Coast, showed a good correspondence. Meanwhile, synchronous multi-sensor-derived SPM with concurrent in situ SPM time series observed in fixed field stations mostly displayed a good correspondence. Results suggest that the algorithm is feasible and compatible for SPM retrieval by multiple sensors.     

Comparison of MERIS,MODIS, SeaWiFS-derived particulate organic carbon,and in situ measurements in the South China Sea

Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) particulate organic carbon (POC) concentration products for the South China Sea (SCS) were compared with in situ data collected from October 2007 to December 2013. Spectral remote-sensing reflectance (R_rs,λ) was also measured to help understand POC algorithm performance. A strict comparison of the satellite-derived POC and in situ measurements showed that MERIS, MODIS, and SeaWiFS underestimated in situ values by 29.1, 11.7, and 31.5%, respectively. Similar results were obtained with a relaxed matching criterion. Through analysis of the causes of product uncertainty, the results suggested that satellite retrieval of R_rs,λ and the global POC algorithm both have an impact on inversion accuracy. However, the formulation of the POC algorithm seems to be more critical. When a regional algorithm was developed to obtain satellite-derived POC, both the strict and relaxed comparison results showed significant improvement, but for coastal waters, both algorithms had larger errors. Other factors affecting the comparison are also discussed.     

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.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Evaluation of RapidEye data for mapping algal blooms in inland waters

The high spatial resolution multispectral imaging sensor onboard RapidEye (RE) has a red-edge band centred at 710 nm, which can be used to produce a product equivalent to the Maximum Chlorophyll Index (MCI) that was developed to detect algal blooms with Medium Resolution Imaging Spectrometer (MERIS) data. The RapidEye system, with five satellites, offers a greater repeat frequency than other high-resolution satellites. In this study, we compared RapidEye and MERIS derived MCI products for the Harris Chain of Lakes in central Florida, USA, to determine if RapidEye can produce an equivalent product similar to MERIS. Data from two RapidEye satellites (RapidEye-2 and RapidEye-5) were used. Band-by-band matchups used RapidEye Top of the Atmosphere (TOA) reflectance and MERIS ρ_s (reflectance corrected only for Raleigh scattering and molecular absorption). The RapidEye TOA reflectance data differed from MERIS, but when the bands were calibrated to the MERIS, the MCI products matched between the two RapidEye satellites and the MERIS MCI. Estimated chlorophyll-a concentrations using a relationship established for Lake Erie matched in situ chlorophyll-a concentrations with a median error of 1.09 mg m^?3. The results indicate that RapidEye is useful for this purpose, which also suggests that other high-resolution satellites with similar red-edge bands may also provide MCI-type products that would allow estimation of chlorophyll-a. RapidEye provides a context for applying future constellation of small satellites for monitoring water quality issues. Lake water quality managers and environmental agencies could effectively use such high-resolution products to assess and manage algal bloom events.     

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ～10 mm before correction to ～5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.     

On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS

Studying abundance and distributions of floating macroalgae such as pelagic Sargassum calls for long-term continuous and consistent observations from multiple satellite sensors. Previous studies mainly relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS). As a follow-on sensor, the Visible Infrared Imager Radiometer Suite (VIIRS) also has the appropriate spectral bands to detect and quantify floating macroalgae. Based on previous works on MODIS, this study presents an improved procedure to extract floating algae pixels from VIIRS Alternative Floating Algae Index (AFAI) imagery, with image filtering used to suppress noise and adjusted thresholds used to mask sun glint, clouds, and cloud shadows. The overall extraction accuracy is about 85%. Simultaneous daily observations from MODIS and VIIRS over the Central West Atlantic (CWA) show consistent spatial patterns, but VIIRS estimations of the algae coverage (in km²) are consistently lower than MODIS (around – 19% mean relative difference or MRD), possibly due to lower sensitivity of the VIIRS near-infrared (NIR) bands than the corresponding MODIS bands. Similarly, at monthly scale VIIRS also shows lower coverage than MODIS, and their difference (around – 29% MRD) is larger than the difference between MODIS-Aqua and MODIS-Terra estimates (around – 14% MRD). Despite these differences, the spatial and temporal patterns between VIIRS and MODIS observed algae distributions match very well at all spatial and temporal scales. These results suggest that VIIRS can provide continuous and consistent observations of floating algae distributions and abundance from MODIS as long as their differences are accounted for, thus assuring continuity in the future. Furthermore, once Sargassum biomass per unit Sargassum area is determined from field measurements, conversion of these area estimates to Sargassum biomass is straightforward.     

A regional algorithm for separating light absorption by chlorophyll-a and coloured detrital matter in the Black Sea,using 480–560 nm bands from ocean colour scanners

The aim of this study is to modify the regional algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium-spectral Resolution Imaging Spectrometer (MERIS) bands using newly available data of seasonal and spatial variability of light absorption by all optically active components in the Black Sea, and to obtain a merged product based on data retrieved from all the colour scanners that have operated since September 1997. Comparison of chlorophyll-a concentration (chl-a) simulated by the standard National Aeronautics and Space Administration (NASA) algorithm with in situ chl-a measurements showed that the NASA algorithm provided incorrect chl-a assessment of Black Sea shelf and deep-sea waters during spring?summer. Originally the standard NASA algorithm could be applied if there was a high correlation between light absorption by phytoplankton (a_ph) and that by coloured dissolved and suspended organic matter (a_CDM), which is not the case in the Black Sea. Consequently, development of the correct regional chl-a algorithm requires splitting of light absorption into a_ph and a_CDM. This issue has been resolved by the proposed regional algorithm developed for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) using remote-sensing reflectance in three (as minimum) spectral bands from 480 to 560 nm. Operation of the SeaWiFS and MERIS colour scanners ceased in December 2010 and April 2012, respectively, while the MODIS scanner is still working on the Terra and Aqua satellites. In this research, level 2 data (products of standard atmosphere correction at three bands filtered by masks/flags) of SeaWiFS, MODIS (on Terra and Aqua satellites), and MERIS scanners were retrieved for their mission lifetime. The regional algorithm was validated independently for each scanner, based on the adequacy of the algorithm-derived chl-a and a_CDM to in situ-measured data for the same day. The results suggest a satisfactory accuracy of the modified regional algorithm.     

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.     

Validation of MERIS LAI and FAPAR products for winter wheat-sown test fields in North-East Bulgaria

Progress in deriving land surface biophysical parameters in a spatially explicit manner using remotely sensed data has greatly enhanced our ability to model ecosystem processes and monitor crop development. A multitude of satellite sensors and algorithms have been used to generate ready-to-use maps of various biophysical parameters. Validation of these products for different vegetation types is needed to assess their reliability and consistency. While most of the current satellite biophysical products have spatial resolution of one kilometre, a recent effort utilizing data from the Medium Resolution Imaging Spectrometer (MERIS) provided leaf area index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and other canopy parameters in a resolution as fine as 300 m over the European continent. This resolution would be more appropriate for application at the regional scale, particularly for crop monitoring. This higher-resolution MERIS product has been evaluated in a limited number of studies to date. This article aims to validate LAI and FAPAR from the MERIS 10-day composite BioPar BP-10 product over winter wheat fields in northeast Bulgaria. The ground measurements of LAI and FAPAR were up-scaled and 30 m resolution reference raster layers were created using empirical relationships with Landsat TM (RMSE = 0.06 and RMSE = 0.22 for FAPAR and LAI, respectively). MERIS FAPAR and LAI were found to have significant correlation with FAPAR and LAI from the reference raster layers (R² = 0.84 and R² = 0.78, respectively). When MERIS Green LAI was calculated (incorporating the fraction of vegetation and brown vegetation cover from the BioPar BP-10 product), better correspondence with LAI values from the reference raster layer was achieved, with RMSE and bias reduced by 30–35%. The results from this study confirm the findings of previous validations showing that MERIS Green LAI tends to overestimate LAI values lower than 1. As a conclusion of the study, the BioPar BP-10 product was found to provide reliable estimates of FAPAR and acceptably accurate estimates of LAI for winter wheat crops in North-East Bulgaria.     

Satellite data cloud detection using deep learning supported by hyperspectral data

ABSTRACT

Deep learning methods can play an important role in satellite data cloud detection. The number and quality of training samples directly affect the accuracy of cloud detection based on deep learning. Therefore, selecting a large number of representative and high-quality training samples is a key step in cloud detection based on deep learning. For different satellite data sources, choosing sufficient and high-quality training samples has become an important factor limiting the application of deep learning in cloud detection. This paper presents a fast method for obtaining high-quality learning samples, which can be used for cloud detection of different satellite data with deep learning methods. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data, which have 224 continuous bands in the spectral range from 400–2500 nm, are used to provide cloud detection samples for different types of satellite data. Through visual interpretation, a sufficient number of cloud and clear sky pixels are selected from the AVIRIS data to construct a hyperspectral data sample library, which is used to simulate different satellite data (such as data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) satellites) as training samples. This approach avoids selecting training samples for different satellite sensors. Based on the Keras deep learning framework platform, a backpropagation (BP) neural network is employed for cloud detection from Landsat 8 OLI, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Terra MODIS data. The results are compared with cloud coverage results interpreted via artificial vision. The results demonstrate that the algorithm achieves good cloud detection results for the above data, and the overall accuracy is greater than 90%.     

Operational multi-sensor monitoring of turbidity for the entire Mekong Delta

An operational satellite-based approach was implemented to monitor turbidity and organic absorption in the Mekong river system. Using physics-based algorithms linked together in a fully automated processing chain, more than 300 Landsat Enhanced Thematic Mapper (ETM) scenes and 1000 MODIS scenes, representing five years of data, were used to produce standardized, quantitative time series of turbidity and organic absorption across Vietnam, Thailand, Cambodia, Laos, and China. To set up this system, the specific inherent optical properties (SIOPs) of the Mekong river system were determined through three separate field campaigns, laboratory analysis, and subsequent optical closure calculations. Following this, a range of satellite data types was tested using the derived Mekong-specific inherent optical properties, including Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m data, Landsat ETM, Medium Resolution Imaging Spectrometer (MERIS), Satellite Pour l’Observation de la Terre (SPOT) 5, RapidEye, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and QuickBird. The satellite-based turbidity estimates were coincident with available field data, and comparisons showed them to be in good agreement. Overall, the derived SIOPs were suitable for water-quality monitoring of the Mekong, and the MODIS, MERIS, Landsat, and RapidEye sensors were found to be the most radiometrically stable and thereby suitable for ongoing operational processing. The implemented system delivers consistent results across the different satellite sensors and over time, but is limited to where the spatial resolution of the sensor is still able to resolve the river width. The system is currently applicable for the entire Mekong river system, both for near-real-time monitoring and for analysis of historical data archive.     

Comparing MODIS and MERIS spectral shapes for cyanobacterial bloom detection

A spectral shape algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) imagery has detected cyanobacterial blooms, with extensive examples in Lake Erie. The detection algorithm uses an approximation of the second derivative as a measure of spectral shape around the 681 nm band S _2d(681). With the end of the MERIS mission on 8 April 2012, an analogue was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) to continue monitoring for these blooms. The MODIS analogue uses the standard ρ_s (Rayleigh-corrected reflectance) to determine S _2d(678), which is computationally equivalent to the negative of the MODIS fluorescent line height (FLH). A comparison was made of the two products from image pairs during a period of relatively severe blooms of cyanobacteria (2008–2011). When the MODIS bands do not saturate due to surface scums from high cyanobacteria biomass or conditions of glint or dense aerosols, the algorithms produce comparable results with a linear transform of the MODIS S _2d(678). The results indicate that MODIS can be used to monitor these blooms. Dense cyanobacteria blooms will produce negative FLH showing a limitation of FLH for bloom detection. The S _2d(678) offers a tool to support monitoring for dense algal blooms.     

On the potential of MODIS and MERIS for imaging chlorophyll fluorescence from space

The first images from MODIS (Moderate Resolution Imaging Spectroradiometer) are now being used to evaluate information on the in vivo fluorescence peak near 685?nm from chlorophyll-a, stimulated by sunlight. The Fluorescence Line Imager (FLI) airborne imaging spectrometer was used in the 1980s to demonstrate the mapping of this signal from an aircraft, showing that it gave significant rejection of confusing signals from atmospheric radiance. For imaging from space, a major limitation is sensor sensitivity, which tends to restrict imaging to relatively high concentrations under good solar illumination. Noise-equivalent chlorophyll concentrations for MODIS and MERIS (Medium Resolution Imaging Spectrometer) have been estimated as 0.07 and 0.1?mg?m^?3, respectively, under zenith Sun, clear sky conditions. Although MERIS has slightly poorer sensitivity, it has the advantage of flexible band placing and presence of a band at 709?nm in the baseline band-set that allows better definition of the continuum spectrum above which fluorescence is measured. This band should also allow detection of bright plankton blooms (red-tide events) through the peak radiance near 709?nm caused by a combination of in-water scattering and absorption.     

The inter-comparison of AATSR dual-view aerosol optical thickness retrievals with results from various algorithms and instruments

The Advanced Along-Track Scanning Radiometer (AATSR) dual-view (ATSR-DV) aerosol retrieval algorithm is evaluated for a single scene over Germany (49–53? N, 7–12? E) on 13 October 2005 by comparison of the aerosol optical thickness (AOT) at 550 nm with products from Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS), in addition to the Atmospheric Aerosol Retrieval using Dual-View Angle Reflectance Channels (AARDVARC) algorithm developed at Swansea University. The AOT was retrieved from the AATSR using the ATSR-DV algorithm, for the pixel size of 1 km × 1 km (at nadir). Then these values were meshed to be consistent with the sampling of the products from the other satellite instruments. The ATSR-DV results compare favourably with the products from orbiting optical instruments dedicated to aerosol retrieval, such as MODIS and MISR, which leads to the conclusion that AATSR is well suited for aerosol retrieval over land when the dual view is used with the ATSR-DV algorithm.     

Detection of algal blooms in European waters based on satellite chlorophyll data from MERIS and MODIS

A technique for algal-bloom detection in European waters is described, based on standard chlorophyll a concentration (Chl) data from two ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS). Comparison of the two data sources shows good agreement in case 1 waters, whereas the difference is significant in coastal waters including turbid areas. A relationship between the water-leaving reflectance at 667 nm and Chl for case 1 waters was used to eliminate pixels where Chl retrieval is contaminated by backscatter from inorganic suspended matter. Daily Chl data are compared to a predefined threshold map to determine whether an algal bloom has occurred. In this study, a threshold map was defined as the 90th percentile of previous years' data to take account of regional differences in typical Chl levels, with separate maps for each sensor to take account of sensor-specific bias. The algal-bloom detection processing chain is described, and example results are presented.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Global remote sensing of Trichodesmium

The maximum chlorophyll index (MCI) from the Medium Resolution Imaging Spectrometer (MERIS) satellite imager gives a robust indicator of the presence of a variety of floating slicks, near-surface vegetation, and intense surface plankton blooms. The index responds to the presence of surface slicks of Trichodesmium due to the ‘red edge’ increase in radiance with increasing wavelength near 700 nm. Global composite images of this index can be used to map the distribution of surface slicks of Trichodesmium, showing seasonal and long-term variations. The MCI also responds to the ‘red edge’ in the spectral signature of Sargassum. The two species share some areas of common occurrence and it is important to distinguish between them. We have developed spectral techniques of distinguishing between these two, and for discriminating a variety of other confusing targets that occur in different areas. We feel that MERIS MCI can be a useful tool in monitoring global Trichodesmium spatial distribution, and its short- and long-term variation.     

Estimating total suspended matter concentration in tropical coastal waters of the Berau estuary,Indonesia

This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R _rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R _rs measured in situ and R _rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.     

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.