Search Results (13)

Ultraviolet (UV) radiation effects on Earth’s ecosystems on a global scale can be assessed on a basis of satellite estimates of hyperspectral irradiance on the surface and in ocean waters and the spectral biological weighting functions. The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. The assessment of in-water irradiance requires additional information on inherent optical properties (IOPs) of ocean water. Our Ozone Monitoring Instrument (OMI) surface hyperspectral irradiance algorithm is updated by implementing a new absorbing aerosol correction based on OMI daily retrievals of UV aerosol absorption optical depth (AAOD). To provide insight into the temporal and spatial variability of absorbing aerosols, we consider a monthly global AAOD climatology derived from the OMI UV aerosol algorithm. Hyperspectral underwater irradiance is computed using Hydrolight radiative transfer calculations along with a Case I water model of IOPs extended into UV. Both planar and scalar irradiances are computed on the Earth’s surface and propagated underwater. The output surface products include the UV index. The output underwater products include the hyperspectral diffuse attenuation coefficients of the planar and scalar irradiances. Effects of the seasonal variability of AAOD on the UV index and the deoxyribonucleic acid (DNA) damage dose rates are considered. The reduction in the UV index and DNA damage dose rate due to the presence of absorbing aerosols can be as large as 30–40%. Full article

This study identifies the characteristics of water regions with negative normalized water-leaving radiance ($n{L}_w\left(\lambda \right)$) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of $n{L}_w\left(\lambda \right)$ and the single-scattering albedo ($\mathsf{\omega }\left(\lambda \right)$) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI $n{L}_w\left(\lambda \right)$, indicate the occurrence of negative values in SGLI $n{L}_w\left(\lambda \right)$ in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by $\mathsf{\omega }\left(\lambda \right)$ at 443 nm alone. Additionally, a comparison of in situ $n{L}_w\left(\lambda \right)$ measurements in Tokyo Bay and the Ariake Sea with $n{L}_w\left(\lambda \right)$ values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative $n{L}_w\left(\lambda \right)$ values are more likely to occur. These regions also tend to have lower $n{L}_w\left(\lambda \right)$ values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative $n{L}_w\left(\lambda \right)$ values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative $n{L}_w\left(\lambda \right)$ values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances. Full article

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) in the respective catchment of each bay. The optical properties were measured in situ over the last decade by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8/OLI data. The absorption coefficient of colored dissolve organic matter (CDOM) at 440 nm, a_CDOM (440), was significantly correlated to Wetlands which may act as sources of CDOM, while Developed areas (Agricultural and Urban classes) were negatively correlated. The Agriculture class was also negatively related to suspended particulate organic matter (SPOM), whilst Coniferous Forests and Mixed Forests as well as Meadows were positively correlated. SPOM seems thus to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land use and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale. Full article

Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge. Full article

Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km² spatial resolution. Coupled ocean–atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L⁻¹ within the validation range of 0.14 to 24 mg L⁻¹, with a detection limit of 0.25 mg L⁻¹. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR. Full article

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%). Full article

Spectral remote sensing reflectance (R_rs(λ), sr⁻¹) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where R_rs(λ) accuracy has not been well documented, the quality of R_rs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual R_rs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of R_rs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly R_rs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the R_rs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment. Full article

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel. Full article

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (R_rs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best R_rs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters. Full article

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering b_b coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and b_b. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between r_rs and b_b/a or b_b/(a + b_b) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and b_b from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission. Full article

The Eastern Lagoon of New Caledonia (ELNC) is a semi-enclosed system surrounded by an extensive coral reef barrier. The system has been suffering impacts from climate variability and anthropogenic activities, including mining exploitation. Satellite monitoring is thus an essential tool to detect such changes. The present study aimed to assess the bio-optical variability of the ELNC and examine the applicability of ocean colour algorithms, using in situ bio-optical and radiometric data, collected during the March 2014 CALIOPE 2 cruise. The chlorophyll a concentration (Chla) varied from 0.13–0.72 mg·m⁻³, and the coastal stations were spectrally dominated by non-algal particles (NAP) and coloured dissolved organic matter (CDOM) (>80% of the total non-water absorption at 443 nm), due to the contribution of allochthonous sources. The phytoplankton specific absorption was generally lower (mean, 0.049 m²·mg Chla⁻¹) than typical values observed for the corresponding Chla range, as well as the spectral slopes of the absorption of CDOM plus NAP (a_dg) (mean, 0.016 nm⁻¹) and of the particle backscattering coefficient (b_bp) (mean, 0.07 nm⁻¹). The remote sensing reflectance obtained using two in-water approaches and modelled from Inherent Optical Properties (IOPs) showed less than 20% relative percent differences (RPD). Chla estimates were highly biased for the empirical (OC4 and OC3) and semi-analytical (GSM, QAA, GIOP, LMI) algorithms, especially at the coastal stations. Excluding these stations, the GSM01 yielded the best retrievals with 35–40% RPD. a_dg(443) was well retrieved by all algorithms with ~18% RPD, and b_bp(443) with ~40% RPD. Turbidity algorithms also performed reasonably well (30% RPD), showing the capacity and usefulness of the derived products to monitor the water quality of the ELNC, provided accurate atmospheric correction of the satellite data. Regionally tuned algorithms may potentially improve the Chla retrievals, but better parameterization schemes that consider the spatiotemporal variability of the specific IOPs are still needed. Full article

Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers under different levels of gold mining activities in the Tapajós River Basin. Furthermore, it investigates possible impacts on the phytoplankton community. Two field campaigns were conducted in the Tapajós River Basin, during high water level and during low water level seasons, to measure Inherent and Apparent Optical Properties (IOPs, AOPs), including scattering (b) and absorption (a) coefficients and biogeochemical data (sediment content, pigments, and phytoplankton quantification). The biogeochemical data was separated into five classes according to the concentration of total suspended solids (TSS) ranging from 1.8 mg·L⁻¹ to 113.6 mg·L⁻¹. The in-water light environment varied among those classes due to a wide range of concentrations of inorganic TSS originated from different levels of mining activities. For tributaries with low or no influence of mining tailings (TSS up to 6.8 mg·L⁻¹), waters are relatively more absorbent with b:a ratio of 0.8 at 440 nm and b₆₆₀ magnitude of 2.1 m⁻¹. With increased TSS loadings from mining operations (TSS over 100 mg·L⁻¹), the scattering process prevails over absorption (b:a ratio of 10.0 at 440 nm), and b₆₆₀ increases to 20.8 m⁻¹. Non-impacted tributaries presented a critical depth for phytoplankton productivity of up to 6.0 m with available light evenly distributed throughout the spectra. Whereas for greatly impacted waters, attenuation of light was faster, reducing the critical depth to about 1.7 m, with most of the available light comprising of red wavelengths. Overall, a dominance of diatoms was observed for the upstream rivers, whereas cyanobacteria prevailed in the low section of the Tapajós River. The results suggest that the spatial and temporal distribution of phytoplankton in the Tapajós River Basin is not only a function of light availability, but rather depends on the interplay of factors, including flood pulse, water velocity, nutrient availability, and seasonal variation of incoming irradiance. Ongoing research indicates that the effects of mining tailings on the aquatic environment, described here, are occurring in several rivers within the Amazon River Basin. Full article

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes. Full article