Medium Resolution Imaging Spectrometer Research Articles

Remote Sensing-Driven Pacific Oyster (Crassostrea gigas) Growth Modeling to Inform Offshore Aquaculture Site Selection

Aquaculture increasingly contributes to global seafood production, requiring new farm sites for continued growth. In France, oyster cultivation has conventionally taken place in the intertidal zone, where there is little or no further room for expansion. Despite interest in moving production further offshore, more information is needed regarding the biological potential for offshore oyster growth, including its spatial and temporal variability. This study shows the use of remotely-sensed chlorophyll-a and total suspended matter concentrations retrieved from the Medium Resolution Imaging Spectrometer (MERIS), and sea surface temperature from the Advanced Very High Resolution Radiometer (AVHRR), all validated using in situ matchup measurements, as input to run a Dynamic Energy Budget (DEB) Pacific oyster growth model for a study site along the French Atlantic coast (Bourgneuf Bay, France). Resulting oyster growth maps were calibrated and validated using in situ measurements of total oyster weight made throughout two growing seasons, from the intertidal zone, where cultivation currently takes place, and from experimental offshore sites, for both spat (R-2 = 0.91; RMSE = 1.60 g) and adults (R-2 = 0.95; RMSE = 4.34 g). Oyster growth time series are further digested into industry-relevant indicators, such as time to achieve market weight and quality index, elaborated in consultation with local producers and industry professionals, and which are also mapped. Offshore growth is found to be feasible and to be as much as two times faster than in the intertidal zone (p < 0.001). However, the potential for growth is also revealed to be highly variable across the investigated area. Mapping reveals a clear spatial gradient in production potential in the offshore environment, with the northeastern segment of the bay far better suited than the southwestern. Results also highlight the added value of spatiotemporal data, such as satellite image time series, to drive modeling in support of marine spatial planning. The current work demonstrates the feasibility and benefit of such a coupled remote sensing-modeling approach within a shellfish farming context, responding to real and current interests of oyster producers.

Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas

Recently, there have been significant efforts in the integration of in-situ and satellite observations for effective monitoring of coastal areas (e.g., the Copernicus program of the European Space Agency). In this study, a 15-year diurnal variation of Water Constituent Concentrations (WCCs) was retrieved from multi-sensor satellite images and in-situ hyperspectral measurements using Radiative Transfer (RT) modeling in the Dutch Wadden Sea. The existing RT model 2SeaColor was inverted against time series of in-situ hyperspectral measurements of water leaving reflectances (Rrs [sr−1]) for the simultaneous retrieval of WCCs (i.e., Chlorophyll-a (Chla), Suspended Particulate Matter (SPM), Dissolved Organic Matter (CDOM)) on a daily basis between 2003 and 2018 at the NIOZ jetty station (the NJS) located in the Dutch part of the Wadden Sea. At the same time, the existing coupled atmosphere-hydro-optical RT model MOD2SEA was used for the simultaneous retrieval of WCCs from time series of multi-sensor satellite images of the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, Multispectral Instrument (MSI) onboard Sentinel-2 and Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 between 2003 and 2018 over the Dutch Wadden Sea. At the NJS, a direct comparison (Taylor diagram and statistical analysis) showed strong agreement between in-situ and satellite-derived WCC values (Chla: R2 ≥ 0.70, RMSE ≤7.5 [mg m−3]; SPM: R2 ≥ 0.72, RMSE ≤5.5 [g m−3]; CDOM absorption at 440 nm: R2 ≥ 0.67, RMSE ≤1.7 [m−1]). Next, the plausibility of the spatial variation of retrieved WCCs over the study area was evaluated by generating maps of Chla [mg m−3], SPM [g m−3], and CDOM absorption at 440 nm [m−1] from MERIS and OLCI images using the MOD2SEA model. The integration of the spatio-temporal WCC data obtained from in-situ measurements and satellite images in this study finds applications for the detection of anomaly events and serves as a warning for management actions in the complex coastal waters of the Wadden Sea.

Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing.

Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency Envisat MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008–2011, 2017, and 2018. A total of 2321 lakes across the contiguous United States were included in the analysis. We examined four different thresholds to define when a waterbody is classified as experiencing a bloom. Across these four thresholds, we explored variability in bloom percentage with changes in seasonality and lake size. As a validation of algorithm performance, we analyzed the agreement between satellite observations and previously established ecological patterns, although data availability in the wintertime limited these comparisons on a year-round basis. Changes in cyanobacterial bloom percentage at the national scale followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a maximum in fall, and decreased through the winter. Wintertime data, particularly in northern regions, were consistently limited due to snow and ice cover. With the exception of the Southeast and South, regional patterns mimicked patterns found at the national scale. The Southeast and South exhibited an unexpected pattern as cyanobacterial bloom percentage reached a maximum in the winter rather than the summer. Lake Jesup in Florida was used as a case study to validate this observed pattern against field observations of chlorophyll a. Results from this research establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States. In addition, methods presented in this study can be tailored to fit the specific requirements of an individual system or region.

The impact of sea bottom effects on the retrieval of water constituent concentrations from MERIS and OLCI images in shallow tidal waters supported by radiative transfer modeling

Many coastal waters include large areas of Optically Shallow Waters (OSWs) where the sea-bottom affects above-water observations of remote sensing reflectance (Rrs [sr−1]). If not treated, the effect of bottom reflectance will interfere with the correct retrieval of Water Constituent Concentrations (WCCs) from hyperspectral and multispectral remote sensing observations. To study this phenomenon in more detail, the existing semi-infinite 2SeaColour Radiative Transfer (RT) model was modified into a finite water layer model, bounded by a diffusely reflecting surface at the sea-bottom. From simulations with the new model, called Water - Sea Bottom (WSB) model, it was observed that a ratio of spectral bands in the Near-Infrared, bands 750 nm and 900 nm, is nearly insensitive to the WCCs and increases with the shallowness of the water, and therefore can be used as a robust index to detect OSWs. The newly established Near-Infrared Bottom Effect Index (NIBEI) was applied to a series of satellite observations over the Wadden Sea during high and low tidal phases. Images from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI) were processed to retrieve WCCs of the study area. The results indicate that the sea-bottom effect in OSWs affects the accuracy of atmospheric correction and retrievals. On the other hand, applying the NIBEI to flag OSWs improves the reliability and consistency of WCCs maps. The application of proposed NIBEI on satellite images requires only Top Of Atmosphere (TOA) radiances at 750 nm and 900 nm and does not depend on atmospheric correction and ancillary local input data (e.g., bathymetry map, bottom type, empirical coefficients, in-situ measurements). As a result, the proposed NIBEI can readily be applied to detect OSWs on various ocean colour remote sensors in various shallow coastal regions.

Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content.

Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m2, and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m2; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI’s sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm2 is lower than in other chlorophyll content ranges; DD’s sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing.

Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatiotemporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio (USA) respectively during 2003–2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Sentinel-3 Ocean Land Color Imager (OLCI) data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels

The retrieval accuracies of three typical near-infrared (NIR) precipitable water vapor (PWV) products are thoroughly discussed in this article. The NIR PWV data are obtained from three satellite sensors: the Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra, the medium-resolution imaging spectrometer (MERIS)/Envisat, and the medium-resolution spectral imager (MERSI)/FY-3A. Collocated Global Positioning System (GPS) PWV data from GPS network are employed as the reference data set because of its high precision in water vapor measurement. Relative difference and root-mean-square (rms) difference are computed for “Clear,” “Cloudy,” and “All Weather” categories for each NIR water vapor product. The results reveal that PWV derived from NIR sensors tend to underestimate the water vapor values with the existence of cloud, as NIR signals cannot penetrate the cloud. Under “Clear” condition, the overall rms for remote sensors are close to the expected goal accuracies, namely, with root-mean-square-error (RMSE) of 5.480 mm for MODIS/Terra, 3.708 mm for MERIS/Envisat, 8.644 mm for MERSI/FY-3A. MERIS/Envisat has the highest PWV retrieval accuracy, while the MODIS/Terra PWV product has the best correlation with GPS PWV ( $R^{2}$ is 0.951). The MODIS/Terra tends to overestimate PWV value, while MERSI/FY-3A tends to underestimate the PWV value. Moreover, a comprehensive comparison of seasonal variation and wet/dry variation for each NIR PWV product is also performed in this study. The results indicate that the RMSE increases significantly under wet conditions (PWV larger than 20 mm) than under dry conditions (PWV smaller than 20 mm) for all remote sensing PWV products.

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

On a relationship between the river runoff and the river plume area in the northeastern Black Sea

ABSTRACT The coastal zone of the northeastern part of the Black Sea is characterized by 80 small rivers with a runoff which may reach 800 m3 s−1. Maximum values of the runoff are related to heavy rain that occasionally occurs over the coastal zone. River runoff forms a river plume in the coastal zone of the sea, which, depending on the local wind speed and direction and current velocity and direction, amount of the runoff and density difference with sea water, may propagate in different directions from the river mouth, and may be involved in the mesoscale and sub-mesoscale circulation. The aim of this study is to investigate the relationship between the runoff of the main rivers along the Russian coast of the Black Sea and the area of the appropriate river plumes. In this paper, we compared the daily runoff from the Mzymta and Sochi rivers and the area of river plumes detected by high concentration of suspended matter acquired daily from Medium Resolution Imaging Spectrometer (MERIS) of satellite Envisat (Environmental Satellite) on different dates in 2006–2011. We took 1 g m−3 as a formal threshold to calculate an outer limit and area of the river plume because it better corresponds with the form of the plume on true colour satellite images. As a result, we obtained a linear regression between the river runoff and the river plume area with a correlation coefficient 0.99 for the Mzymta River and a parabolic regression for the Sochi River. A linear regression with a correlation coefficient 0.98 can be applied to the Sochi River as well. The slope of the linear regressions differs three times between these two rivers, which may be explained by a significantly different distance of hydrological gauge stations from the river mouths, as well as by physical and geochemical characteristics of the river mouths. Anyway, such relationships even though they can be specific for each river, will considerably help estimate areas of river plumes and identify coastal regions subject to environmental risks after heavy rain.

Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (SPOC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ13CPOC) values. The SPOC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the SPOC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.

Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

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 &gt; 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 &gt; 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.

Effects of increasing aerosol optical depth on the gross primary productivity in China during 2000–2014

Abstract The aerosol optical depth (AOD) has been shown to influence the gross primary productivity (GPP) in terrestrial ecosystems by increasing the diffuse fraction, which could expend radiation scattering and improve the light-use efficiency of shade leaves. Previous studies have suggested that variations in aerosols are the main cause of changes in solar radiation over China. In this study, we analyzed the spatial and temporal trends of the AOD and GPP, which were extracted from Medium Resolution Imaging Spectrometer (MODIS) products and radiation (total solar radiation and diffuse radiation) and diffuse fraction data obtained from meteorological stations in China from 2000 to 2014. The correlations among these variables were investigated according to three vegetation types. The results showed that the AOD increased by approximately 11.4% between 2001 and 2014. The diffuse radiation and GPP increased by 6.7% and 8.9% in the same period, respectively. The spatial patterns of the AOD were found to be similar to that of the diffuse radiation over China. The results showed a significantly positive correlation between the AOD and GPP for forests (p

The distribution of pelagic Sargassum observed with OLCI

ABSTRACT We present monthly maps and derived statistics to describe the large-area distribution of Sargassum in the equatorial Atlantic, Caribbean, and Gulf of Mexico, using image data from the OLCI (Ocean and Land Colour Instrument) on the Sentinel 3a satellite. OLCI has provided image data since October 2016 which can be compared with images produced by MERIS (Medium Resolution Imaging Spectrometer) in the period 2002 to 2012. Results show a significant and continuing increase of the area covered by pelagic Sargassum, starting from a first expansion into the Caribbean and equatorial Atlantic in 2011. We use results from a global detection scheme based on the MCI (Maximum Chlorophyll Index) derived from MERIS and OLCI and implemented by ESA’s Grid Processing on Demand (GPOD) project. The scheme uses the OLCI image data set at the reduced resolution of 1200m to give monthly global composite images at 5km spatial resolution, designed to record location, extent and intensity of intense plankton blooms and floating vegetation. In the present study area, images are dominated by a growing and spreading distribution of pelagic Sargassum, whose origin and annual cycle remain unexplained. The composite images give no indication of similar populations in other parts of the global ocean. Results derived from MERIS and OLCI are consistent with those derived from MODIS and VIIRS.

Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002–2012) were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R2: 0.58–0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.

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.

Dissolved organic matter at the fluvial–marine transition in the Laptev Sea using in situ data and ocean colour remote sensing

Abstract. River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.

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 &lt; 0.33). Same-day retrieval of PC also showed good agreement with cyanobacteria biomass (R2 &gt; 0.66, p &lt; 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.

Satellite-Based Time Series of Chlorophyll in Chilko Lake, British Columbia, Canada

In Canada, many northern lakes are remote and difficult to access, with limited limnological data. Satellite sensors provide widespread coverage and growing time series of data unavailable via conventional sampling, but global validation is still limited. We evaluated chlorophyll estimates from the MERIS (Medium Resolution Imaging Spectrometer) sensor on board the European Space Agency (ESA) ENVISAT satellite for the ultra-oligotrophic Chilko Lake in the coastal mountains of central British Columbia. This lake supports a valuable sockeye salmon (Oncorhynchus nerka) population. We obtained good temporal coverage, through 1,425 scenes between June 18, 2002 and April 6, 2012. Although pre-processing was required to produce a high-quality dataset, one standard ESA algorithm generated chlorophyll estimates similar to field data. Regional and interannual phenological patterns were clear, and differences that may be important determinants of salmon production were well described. Although MERIS ceased operation in April 2012, it was replaced by the OLCI (Ocean and Land Color Instrument) on the SENTINEL 3a and 3b satellites launched in February 2016 and April 2018, respectively. We conclude that, with appropriate quality control and in situ validation, satellite-generated chlorophyll time series in sockeye salmon rearing lakes have significant potential as a fisheries planning and analysis tool.

Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment

Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific and operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisitions, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g·m−2 (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in situ measurements was observed ( r = 0.77, p < 0.01), providing increased confidence to users of the product over vineyard dominated Mediterranean environments.