Medium Resolution Imaging Spectrometer Data Research Articles

Uncertainty of atmospheric scattering functions relevant for Satellite Ocean Colour Radiometry in European Seas

ABSTRACT Uncertainty assessment of Ocean Colour Radiometry (OCR) usually relies on the comparison between satellite marine reflectance and concurrent in situ radiometric measurements. However, when dealing with the standard atmospheric correction (AC), uncertainty of marine reflectance originates fundamentally from two sources: (i) uncertainty of the Level-1 calibration at top of the atmosphere, and (ii) uncertainty of the atmospheric scattering functions computed by the AC (atmospheric path reflectance and transmittance). The aim of this alternative work is thus to assess the uncertainty of these scattering functions, thanks to inherent optical properties of aerosols extracted from the AErosol Robotic NETwork (AERONET) and ingested in a radiative transfer code. The study is conducted for the MEdium Resolution Imaging Spectrometer (MERIS), over three AERONET sites selected in European seas (Aqua Alta Oceanographic Tower, AAOT; Gustav Dalen Tower, GDT; Lampedusa) and Lanai in the Pacific Ocean as a reference. We develop an uncertainty budget based on the statistical analysis of the errors between MERIS and AERONET-derived data. At 443 nm, uncertainty of MERIS atmospheric path reflectance due to random effects is estimated around 2%, while that of total atmospheric transmittance is between 0.6% and 1%. Propagation of these uncertainties to OCR is achieved following metrological principles and varies between match-ups and sites due to the distinctive contribution of the marine signal to the total radiometry: uncertainty of MERIS marine reflectance at 443 nm is 10.8% (±1.1%) at Lanai, 18.3% (±4.6%) at Lampedusa, 29.4% (±12.2%) at AAOT and up to 68.7% (±127.4%) at GDT (i.e. with a significant intra-site variability). The methodology is a first step aimed at deriving a pixel-by-pixel uncertainty budget of OCR, instead of providing overall estimates as generally done in the OC community. Limitations due to uncertainties in the AERONET data are also discussed, with a tentative quantification.

A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data

Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kd(λ)) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305,  ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed  ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated  ZSD matched well with the in situ measured  ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

The estimation of total suspended matter from satellite imagery of tropical coastal water Berau Estuary, Indonesia

Water in coastal and estuary areas needs to be investigated as human activities is allegedly decrease their quality. This has led to optical remote sensing for mapping optical water quality using empirical and semi-analytical approaches. Therefore, this study aims to estimate the Total Suspended Matter (TSM) concentration from Medium Resolution Imaging Spectrometer (MERIS) data using a spectral unmixing approach combined with a radiative transfer model. This approach was implemented in the equatorial tropical coastal water, the Berau estuary, Indonesia, by generating a look-up table (LUT) of top-of-atmosphere radiance spectra through the coupled forward models, and the endmembers were selected from the LUT. The spectral unmixing algorithm was employed to the MERIS data for decomposing the image into haze and sediment components. The data were then transformed into images of a constant haze level corresponding to 50 km visibility, and the atmospheric correction was applied. Furthermore, the TMS concentration was retrieved using the inverse semi-analytical Kubelka-Munk model. The result gave overestimated TSM concentration values on clear waters. However, in turbid waters, a lower RMSE was obtained, and the coefficient of determination was higher than in clear waters.

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.

Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

Abstract. The historic MERIS (Medium Resolution Imaging Spectrometer) sensor on board Envisat (Environmental Satellite, operation 2002–2012) provides valuable remote sensing data for the retrievals of summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI (Ocean and Land Colour Instrument) on board Sentinel 3A and 3B (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the resulting product. The presence of 15 visible and near-infrared spectral channels of MERIS allows high-quality retrievals of sea ice albedo and melt pond fraction, but it makes cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud Screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high-quality screening throughout the whole swath. A total of 3 years of reference cloud mask from AATSR (Advanced Along-Track Scanning Radiometer) (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS (Moderate Resolution Imaging Spectroradiometer) cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behavior is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF–albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artifacts, which were clearly visible in the previous version of the dataset. The developed cloud screening routine can be applied to address cloud contamination in remote sensing data over sea ice. The resulting cloud mask for the MERIS operating time, as well as the improved MPF–albedo datasets for the Arctic region, is available at https://www.seaice.uni-bremen.de/start/ (Istomina et al., 2017).

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.

Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data

The Secchi disk depth (ZSD, m) has been used globally for many decades to represent water clarity and an index of water quality and eutrophication. In recent studies, a new theory and model were developed for ZSD, which enabled its semi-analytical remote sensing from the measurement of water color. Although excellent performance was reported for measurements in both oceanic and coastal waters, its reliability for highly turbid inland waters is still unknown. In this study, we extend this model and its evaluation to such environments. In particular, because the accuracy of the inherent optical properties (IOPs) derived from remote sensing reflectance (Rrs, sr−1) plays a key role in determining the reliability of estimated ZSD, we first evaluated a few quasi-analytical algorithms (QAA) specifically tuned for turbid inland waters and determined the one (QAATI) that performed the best in such environments. For the absorption coefficient at 443 nm (a(443), m−1) ranging from ~0.2 to 12.5 m−1, it is found that the QAATI-derived absorption coefficients agree well with field measurements (r2 > 0.85, and mean absolute percentage difference (MAPD) smaller than ~39%). Furthermore, with QAATI-derived IOPs, the MAPD was less than 25% between the estimated and field-measured ZSD (r2 > 0.67, ZSD in a range of 0.1–1.7 m). Furthermore, using matchup data between Rrs from the Medium Resolution Imaging Spectrometer (MERIS) and in-situ ZSD, a similar performance in the estimation of ZSD from remote sensing was obtained (r2 = 0.73, MAPD = 37%, ZSD in a range of 0.1–0.9 m). Based on such performances, we are confident to apply the ZSD remote sensing scheme to MERIS measurements to characterize the spatial and temporal variations of ZSD in Lake Taihu during the period of 2003–2011.

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea.

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake

To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSElog < 0.33). Same-day retrieval of PC also showed good agreement with cyanobacteria biomass (R2 > 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [aph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3.

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.

OLCI-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters—a preliminary evaluation

We present here results that demonstrate the potential of the recently launched Ocean and Land Colour Instrument (OLCI) onboard the satellite Sentinel-3A to deliver accurate estimates of chlorophyll-a (chl-a) concentration in coastal waters using reflectances in the red and near-infrared (NIR) spectral regions. Two-band and three-band NIR-red models that were previously used for data from the MEdium Resolution Imaging Spectrometer (MERIS) were applied to OLCI data from the Sea of Azov and the Taganrog Bay, Russia. Atmospherically corrected reflectance data from OLCI were compared to in situ reflectance data collected concurrently with a field spectrometer. Results show that the default atmospheric correction procedure currently applied to OLCI data performs well in preserving the spectral shape of chl-a-specific reflectance features in the red and NIR regions. Similar to what was achieved with MERIS data, the NIR-red models yield accurate estimates of chl-a concentration, with accuracies on the order of 90%, though the parameters of the NIR-red algorithms based on OLCI data are slightly different from what was obtained with MERIS data. More data, from various geographical locations, need to be analyzed to establish robust NIR-red algorithms for OLCI data.

An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data

Lake Malawi is an important water resource in Africa. However, there is no routine monitoring of water quality in the lake due to financial and institutional constraints in the surrounding countries. A combination of satellite data and a semi-analytical algorithm can provide an alternative for routine monitoring of water quality, especially in developing countries. In this study, we first compared the performance of two semi-analytical algorithms, Doron11 and Lee15, which can estimate Secchi disk depth (SD) from satellite data in Lake Malawi. Our results showed that even though the SD estimations from the two algorithms were very highly correlated, the Lee15 outperformed the Doron11 in Lake Malawi with high estimation accuracy (RMSE = 1.17 m, MAPE = 18.7%, R = 0.66, p < 0.05). We then evaluated water transparency in Lake Malawi using the SD values estimated from nine years of Medium Resolution Imaging Spectrometer (MERIS) data (2003–2011) with the Lee15 algorithm. Results showed that Lake Malawi maintained four water transparency levels throughout the study period (i.e., level 1: SD > 12 m; level 2: SD between 6–12 m; level 3: SD between 3–6 m; level 4: SD between 1.5–3 m). The level 1 and 2 water areas tended to shift or trade places depending on year or season. In contrast, level 3 and 4 water areas were relatively stable and constantly distributed along the southwestern and southern lakeshores. In general, Lake Malawi is dominated by waters with SD values larger than 6 m (>95%). This study represents the first overall and comprehensive analysis of water transparency status and spatiotemporal variation in Lake Malawi.

Spatial and temporal changes of primary production in a deep peri-alpine lake

ABSTRACTLake productivity is fundamental to biogeochemical budgets as well as estimating ecological state and predicting future development. Combining modelling with Earth Observation data facilitates a new perspective for studying lake primary production. In this study, primary production was modelled in the large Lake Geneva using the MEdium Resolution Imaging Spectrometer (MERIS) image archive for 2002–2012. We used a semi-empirical model that estimates primary production as a function of photosynthetically absorbed radiation and quantum yield of carbon fixation. The necessary input parameters of the model—concentration of chlorophyll a, downwelling irradiance, and the diffuse attenuation coefficient—were obtained from MERIS products. The primary production maps allow us to study decennial temporal (with daily frequency) and spatial changes in this lake that a single sample point cannot provide. Modelled estimates agreed with in situ results (R2 = 0.68) and showed a decreasing trend (∼27%) in production in Lake Geneva for the selected decade. Yet, in situ monitoring measurements missed the general increase of productivity near the incoming Rhône River. We show that the temporal and spatial resolution provided by satellite observations allows estimates of primary production at the basin-scale. The phytoplankton annual primary production was estimated as ∼302 (SD 20) g C m−2 yr−1 for Lake Geneva for 2003 to 2011. This study demonstrates that maps of primary production can be obtained even with reduced resolution (1200  m) MERIS data and relatively simple methods, and thereby calls for deeper integration of remote sensing products into conventional in situ observation approaches.

Concentrations of Multiple Phytoplankton Pigments in the Global Oceans Obtained from Satellite Ocean Color Measurements with MERIS

The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].

Optical Classifications of Poyang Lake Water and Long-Term Dynamics Based on MERIS Observations

Using observations from the Medium Resolution Imaging Spectrometer (MERIS) and an unsupervised (Iterative Self-Organizing Data Analysis Technique) clustering method, the class centers for four different optical classes (i.e., moderate turbid or Class I; turbid or Class II; extremely turbid or Class III; and clear waters or Class IV) of Poyang Lake water were determined. A squared-Mahalanobis distance-based classification scheme was then used to classify 320 MERIS data between 2003 and 2012 (∼2.7 images/month on average). Significant dynamics of the probability of occurrence (POO) for different classes were observed through long-term analysis. The extremely turbid and clear water classes dominated Poyang Lake, accounting for ∼70% of the total surface area within the entire time series. Class III and IV waters showed out-of-phase fluctuation patterns, while a large POO was observed in dry seasons for the extremely turbid water class (Class III), and the clear water class (Class IV) represented most of the lake in the wet seasons. The northern narrow water channel of Poyang Lake was generally characterized by Class II and III waters. In contrast, the high POO of Class IV was demonstrated in the southern main lake and some eastern and southern isolated sublakes. The hydrological analysis shows that the south-to-north water level gradients were positively correlated with the combined POOs of Classes II and III ( R 2 = 0.4) and that the gradients were negatively correlated with the POO of Class IV ( R 2 = 0.36). Significant relationships existed between the POOs of different classes and local precipitation. This study provides critical information for future environmental conservation planning.

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (Cchla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based Cchla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured Cchla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured Cchla data in each OWT to calibrate the optimal Cchla estimation model for each OWT. We then developed a soft-classification-based Cchla estimation method, which blends the Cchla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based Cchla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R2), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of Cchla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.

MERIS observations of phytoplankton phenology in the Baltic Sea

The historical data from the MEdium Resolution Imaging Spectrometer (MERIS) is an invaluable archive for studying global waters from inland lakes to open oceans. Although the MERIS sensor ceased to operate in April 2012, the data capacities are now re-established through the recently launched Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development of a consistent time series for investigating phytoplankton phenology features is crucial if the potential of MERIS and OLCI data is to be fully exploited for inland water monitoring. This study presents a time series of phytoplankton abundance and bloom spatial extent for the highly eutrophic inland water of the Baltic Sea using the 10-year MERIS archive (2002–2011) and a chlorophyll-a based Summed Positive Peaks (SPP) algorithm. A gradient approach in conjunction with the histogram analysis was used to determine a global threshold from the entire collection of SPP images for identifying phytoplankton blooms. This allows spatio-temporal dynamics of daily bloom coverage, timing, phytoplankton abundance and spatial extent to be investigated for each Baltic basin. Furthermore, a number of meteorological and hydrological variables, including spring excess phosphate, summer sea surface temperature and photosynthetically active radiation, were explored using boosted regression trees and generalised additive models to understand the ecological response of phytoplankton assemblages to environmental perturbations and potential predictor variables of summer blooms. The results indicate that the surface layer excess phosphate available in February and March had paramount importance over all other variables considered in governing summer bloom abundance in the major Baltic basins. This finding allows new insights into the development of early warning systems for summer phytoplankton blooms in the Baltic Sea and elsewhere.

Applicability of Earth Observation chlorophyll-a data in assessment of water status via MERIS — With implications for the use of OLCI sensors

Earth Observation (EO) offers spatially and temporally unique data for generating information required under various environmental regulations for assessing the status of surface waters. These requirements, which are laid down in, for example, European Union directives and the Clean Water Act in the United States, share two core elements with respect to status assessments: 1) the status assessment is done using discrete classes, typically for water bodies, sub-areas or critical sites representative for certain area of interest, and 2) phytoplankton chlorophyll a (chl-a) is one of the main variables considered. We analysed the benefits of using chl-a concentrations derived from EO data for the status assessments specified in the EU Water Framework Directive (WFD). Our study focused especially on EO observations' ability to capture extreme and transient events (such as instances of cyanobacteria blooms) more frequently than the monitoring-station data conventionally employs. The accuracy of EO-based chl-a assessment was studied for, in all, 129 Finnish water bodies in the area of the Baltic Sea, in Northern Europe. Natural conditions in this coastal area – particularly its multitude of small bays, numerous estuaries, and mosaic of islands – impose exceptionally strict requirements for an EO instrument's spatial resolution. The analysis revealed that an instrument with a 300 m resolution, such as the MEdium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI), can be used to estimate the water quality in 62% of these water bodies. Processing of MERIS data into chl-a concentrations by means of a FUB inversion model demonstrated good accuracy relative to monitoring stations' measurements for the open-water season in 2003–2011. This extensive dataset showed a 23% difference in modal values between EO- and station-sampling-based chl-a concentrations. The bias in EO chl-a estimates was found to increase with low Secchi disk depth, elevated turbidity, and the presence of intensive phytoplankton blooms. The monitoring-station and EO data showed similar distributions of chl-a observations for a given day and location, a finding that supports the comprehensive use of EO-derived chl-a concentrations in assessment. For determination of a water body's status, the EO data required but also allowed for statistical analysis that differs from what has typically been utilised with sparse measurements from monitoring-station data. The geometric mean or the mode of the EO observations was found to represent the main bulk of the chl-a concentrations well statistically. In contrast, the arithmetic mean of EO observations yields chl-a concentrations that are roughly 1.1–1.6 μg/l higher and hence can lead to over-estimation in the associated status assessment. This paper also presents a new approach applicable for evaluating the validity of EO-based algorithms for any coastal water area requiring assessment. With this quality-grade (QG) method, the EO chl-a estimation accuracy is rated in terms of three grades, with water bodies taken as the evaluation units. For this, the method utilises statistical differences between EO and station-sampling chl-a concentrations and applies background information on optical properties obtained from measurements at routine-monitoring stations. The QG method showed the EO-based chl-a accuracy to suffice for assessing the status of 65% of the coastal water bodies examined. At concentrations representing the threshold for the target of “good status” under the WFD, the EO approach produced 0.6 μg/l higher chl-a values than the stations' sampling did. The MERIS results point to clear benefits of using OLCI-based status assessment throughout the Sentinel-3 era.

A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters From Simulated MERIS and OLCI Data

Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691 $\text {mg} \cdot \text {m}^{-3}$ , mean absolute percentage error = 172.863%, and coefficient of determination ( $R^{2}) = 0.730$ . FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.

Atmospheric Correction of Multispectral Satellite Images Based on the Solar Radiation Transfer Approximation Model

A model of the solar radiation transfer in the Earth’s atmosphere is developed for explicit calculations of the transfer characteristics of the atmosphere (brightness coefficients, transmission, and spherical albedo) in the visible and near-IR atmospheric windows depending on the surface air pressure (or terrain elevation), aerosol optical parameters, and angles indicating the position of the Sun and the satellite system with respect to the Earth’s surface. The model is based on spectrally dependent approximations obtained using the atmospheric radiative transfer code DISORT. The effect of altitudinal stratification of the atmospheric optical parameters on the accuracy of underlying surface spectral albedo retrieval from the brightness coefficients at the top of the atmosphere is investigated. An algorithm is developed for radiometric correction of multispectral satellite images based on the model suggested. Approbation of the algorithm is performed using MERIS (Medium Resolution Imaging Spectrometer) data. It is shown that accounting for optical properties of the atmosphere and adjacency effects in processing satellite images substantially improves the visibility of the underlying surface and renders their spectral reflectance parameters.