Ocean And Land Colour Instrument Research Articles

Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators.

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017-2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production.

Analysis of Chlorophyll-a Variability in the Eastern Indonesian Waters Using Sentinel-3 OLCI from 2020-2021

The Eastern Indonesian waters are significant in influencing the global climate system and oceanic connectivity. However, the Indonesian Through Flow (ITF) facilitates the movement of waters from the Pacific Ocean to the Indian Ocean. This flow vertically mixes water masses in the Eastern regions, leading to the concentration of phytoplankton. In addition, the distribution of phytoplankton, indicative of chlorophyll-a concentration, is influenced by upwelling and downwelling phenomena. Chlorophyll-a, responsible for capturing carbon and producing oxygen in marine ecosystems, is important in regulating climate change. Moreover, oceanographic conditions play a significant role in the dispersion of chlorophyll-a concentration. Therefore, this study adopted ocean colour remote sensing technology to assess chlorophyll-a distribution. Monthly ocean colour data was collected by the multi-temporal Sentinel-3 Ocean and Land Colour Instrument (OLCI). The analysis output included chlorophyll-a concentration associated with currents and the El Nino Southern Oscillation (ENSO). Data processing using the Case-2 Regional Coast Colour (C2RCC) processor resulted in an average chlorophyll-a concentration in the Eastern Indonesian waters ranging from 0.16 to 0.52. The results showed higher chlorophyll-a levels during the southeast monsoon (July to September) and lower levels during the northwest monsoon (January to March).

Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.

Deconstructing co-occurring marine heatwave and phytoplankton bloom events in the Arkona Sea in 2018

Between May and August 2018, two separate marine heatwaves (MHWs) occurred in the Arkona Sea in the western Baltic Sea. These heatwaves bookended an extended period of phytoplankton growth in the region. Data from the Ocean and Land Colour Instrument (OLCI) on board the European Sentinel-3 satellite revealed an eddy-like structure containing high chlorophyll a (Chl-a) concentrations (ca. 25 mg.m-3) persisting for several days at the end of May in the Arkona Sea. Combining ocean colour observations, a coupled bio-optical ocean model and a particle tracking model, we examined the three dimensional relationship between these co-occurring MHW and phytoplankton bloom events. We find that the onset of the MHW in May provided the optimal conditions for phytoplankton growth, i.e. sufficient light and nutrients. Wind-driven surface eddy circulation, geostrophic eddy stirring and transient submesoscale dynamics along the edges of the eddy provided a transport path for nutrient fluxes and carbon export, and helped to sustain the phytoplankton bloom. The bloom may have indirectly had an enhancing effect on the MHW, through the impact of water constituent-induced heating rates on air-sea energy fluxes. The subsurface signature of the MHW plays a critical role in de-coupling surface and subsurface dynamics and terminating the phytoplankton bloom. Subsurface temperature anomalies of up to 8°C between 15 and 20 m depth are found to persist up to 15 days after the surface signature of the MHW has disappeared. The study reveals how surface and subsurface dynamics of MHWs and phytoplankton blooms are connected under different environmental conditions. It extends our knowledge on surface layer processes obtained from satellite data.

A novel water optical types framework for Chinese inland waters with the application of multitype satellite sensor

ABSTRACT Optical Water Type (OWT) analysis is crucial for comprehending water composition and quality, key factors in assessing water quality over extensive areas. However, China’s inland waters lack a standardized system for such analysis. To quantitatively analyze the classification results, our study compared three K-means clustering methods, for analyzing 1310 spectral data from various Chinese lakes and reservoirs, thereby addressing this gap. The innovative split-merge K-means method identified 13 distinct OWTs that more closely adhere to the principles of minimizing intra-class distance and maximizing inter-class distance. These were categorized into four groups: clear water, turbid water, eutrophic water, and special type water. Additionally, we developed a method based on Spectral Angle Distance (SAD) to evaluate the classification capabilities of 12 satellite sensors. The results show that Sentinel-3 OLCI (Ocean and Land Color Instrument), MERIS (Medium Resolution Imaging Spectrometer), and Sentinel-2 MSI (Multispectral Instrument) have the best water classification capabilities, making them well-suited for large-scale monitoring of OWT changes. Conversely, other sensors, such as the Sustainable Development Scientific Satellite-1 (SDGSAT-1), Landsat-8, GaoFen-6, GaoFen-1, GaoFen-2, Landsat-5, Landsat-7, Moderate Resolution Imaging Spectroradiometer (MODIS), and HuanJing-1, necessitate the consolidation of water types for effective categorization, indicative of their more limited classification capabilities.

Impacts of Hydrology and Extreme Events on Dissolved Organic Carbon Dynamics in a Heavily Urbanized Estuary and Its Major Tributaries: A View From Space

AbstractDissolved organic matter and its colored component, Colored Dissolved Organic Matter (CDOM), play a major role in global carbon budgets, and their fluxes provide an essential link between terrestrial and aquatic biogeochemical cycles. Satellite observations can uniquely capture the hydro‐biogeochemical connectivity of terrestrial and aquatic landscapes, across scales. Yet, accurate satellite retrievals of CDOM and dissolved organic carbon (DOC) dynamics remain challenging in urbanized estuaries and coasts. Here, we present an advanced unified algorithm for space‐based retrieval of coastal CDOM and DOC dynamics and its application in Long Island Sound—one of the world's most heavily urbanized estuaries that is becoming increasingly vulnerable to climate change stressors. A rich bio‐optical data set, encompassing a wide range of environmental conditions, was integrated into the algorithm training to retrieve DOC concentrations and CDOM spectral shape (i.e., spectral slope S275–295)—a proxy for DOC quality. The new algorithms were applied to full‐resolution satellite imagery from the Sentinel‐3 Ocean and Land Color Instrument (OLCI) after thoroughly evaluating the performance of six ocean color atmospheric correction approaches (ACOLITE, BAC, C2RCC, MUMM, l2gen, and Polymer). Evaluation of the algorithms yielded mean absolute percent differences of 28%, 12%, and 10% for aCDOM(300), S275–295, and DOC, respectively. Application of the algorithms to multi‐year satellite OLCI imagery captured, for the first time, the coupled impact of seasonal transitions, wind regimes, freshwater inputs, anthropogenic disturbances, and hydrological extremes (both intense precipitation and droughts) on DOC fluxes and CDOM quality at the ecosystem scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in complex urban−estuary systems.

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BB-AERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONET-OC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf.

Validation of photosynthetically active radiation by OLCI on Sentinel-3 against ground-based measurements in the central Mediterranean and possible aerosol effects

ABSTRACT Instantaneous determinations of photosynthetically active radiation (PAR) over the sea from the Ocean and Land Color Instrument (OLCI) on Sentinel-3 are compared with in-situ measurements at the island of Lampedusa in the central Mediterranean Sea. Radiative transfer calculations show that the PAR measured at the island site is representative for open ocean conditions. Satellite data show a good agreement (5.2% positive bias, R2 = 0.97) with in-situ data, in line with similar analyses for other satellite sensors. Larger satellite-in situ differences are found during summer, and the possible role of aerosols in degrading PAR estimate has been investigated by comparing AOD values measured at Lampedusa and derived by OLCI. The relative difference between OLCI and in-situ PAR appears to be negatively correlated with the relative differences between OLCI and in-situ AOD, suggesting that a more accurate determination of AOD, in particular, for cases with AOD > 0.2, mostly related to Saharan dust, may lead to improved satellite PAR estimates.

Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates.

Retrieving land surface reflectance anisotropy with Sentinel-3 observations and prior BRDF model constraints

Sentinel-3 supports governmental policies and European programmes such as the Copernicus Global Land Service with daily monitoring of the land surface by measuring the Earth's surface with its multi-band wide-swath visible and near-infrared radiometers. To fully exploit Sentinel-3 data sets, performing a correction of directional effects caused by the land surface reflectance anisotropy properties is mandatory. This correction requires first an estimation of the bidirectional reflectance distribution function (BRDF) to be properly achieved. The quality and robustness of global estimates of key land surface variables derived from space-borne sensors depend clearly on an accurate assessment of the spectral BRDF.Here, we present an algorithm aiming at harnessing the Sentinel-3's wide imaging swath to retrieve the land surface BRDF, approximating it via a kernel-driven semi-empirical model which can then be used to normalise the observations to a common Sun-sensor geometry. Our algorithm, named ReBeLS (Regularised BRDF inversion for Land Surface), uses a temporally regularised BRDF inversion approach and, together with prior knowledge of the land surface BRDF obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, assimilates Sentinel-3 surface reflectance observations. We focus on the results derived from observations provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors; however, the algorithm can also be applied to reflectance observations acquired by the Sea and Land Surface Temperature Radiometer (SLSTR). The retrieved BRDF model parameters reproduce within uncertainties the directional signature as seen by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The generated Sentinel-3 normalised surface reflectance products are a significant advance compared with time series of directional surface reflectance and maximum value composite images, showing a statistically significant (p≪0.05) correlation (r>0.9) with VIIRS nadir BRDF-adjusted surface reflectance. Furthermore, the ReBeLS Sentinel-3 BRDF products offer enough flexibility to allow users to compute spectral surface albedos or to normalise surface reflectance to any Sun-sensor configuration.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L−1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Radiometric assessment of OLCI, VIIRS, and MODIS using fiducial reference measurements along the Atlantic Meridional Transect

High quality independent ground measurements that are traceable to metrology standards, with a full uncertainty budget, are required for validation over the lifetime of ocean-colour satellite missions. In this paper, we used radiometric Fiducial Reference Measurements (FRM) collected during four Atlantic Meridional Transect (AMT) field campaigns from 2016 to 2019 to assess the performance of radiometric products from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3A (S-3A) and 3B (S‐3B), the Moderate Resolution Imaging Spectroradiometer instrument aboard Aqua (MODIS-Aqua), and the Visible Infrared Imaging Radiometer Suite instrument aboard Suomi NPP and NOAA-20 (Suomi-VIIRS and NOAA-20 VIIRS). The AMT provides one of the few sampling platforms that make high-quality in situ radiometric measurements in oligotrophic, low chlorophyll-a oceanic waters for ocean colour satellite validation. In situ data were acquired and processed following established FRM protocols, calibrated to metrology standards, referenced to inter-comparison exercises and with a full uncertainty budget. From these we selected an uncertainty threshold, which we used as part of a matchup procedure that takes into account the temporal and spatial variability of both the in situ and satellite data. Three atmospheric correction models were compared for S-3A and S‐3B OLCI radiometric products; the standard OLCI IPF-OL-2, POLYMER and NASA SeaDAS l2gen. Based on the round-robin comparison, POLYMER provided the best performance in the retrieval of water-leaving radiances. The analysis showed that Suomi-VIIRS and MODIS-Aqua performed better than NOAA-20 VIIRS, and comparably with S‐3B OLCI standard products. The S-3A OLCI standard product outperformed the NASA products. The S-3A OLCI and S‐3B OLCI instruments were also compared during their tandem phase, which showed that S‐3B OLCI radiances were systematically higher than S-3A OLCI across the spectrum.

A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data

Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.

Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach

Inland and coastal waters provide key ecosystem services and are closely linked to human well-being. In this study, we propose a semi-analytical method, which can be applied to Sentinel-2 MultiSpectral Instrument (MSI) images to retrieve high spatial-resolution total suspended solids (TSS) concentration in a broad spectrum of aquatic ecosystems ranging from clear to extremely turbid waters. The presented approach has four main steps. First, the remote sensing reflectance (Rrs) at a band lacking in MSI (620 nm) is estimated through an empirical relationship from Rrs at 665 nm. Second, waters are classified into four types (clear, moderately turbid, highly turbid, and extremely turbid). Third, semi-analytical algorithms are used to estimate the particulate backscattering coefficient (bbp) at a reference band depending on the water types. Last, TSS is estimated from bbp at the reference band. Validation and comparison of the proposed method with three existing methods are performed using a simulated dataset (N = 1000), an in situ dataset collected from global inland and coastal waters (N = 1265) and satellite matchups (N = 40). Results indicate that the proposed method can improve TSS estimation and provide accurate retrievals of TSS from all three datasets, with a median absolute percentage error (MAPE) of 14.88 %, 31.50 % and 41.69 % respectively. We also present comparisons of TSS mapping between the Sentinel-3 Ocean and Land Colour Instrument (OLCI) and MSI in Lake Kasumigaura, Japan and the Tagus Estuary, Portugal. Results clearly demonstrate the advantages of using MSI for TSS monitoring in small water bodies such as rivers, river mouths and other nearshore waters. MSI can provide more detailed and realistic TSS estimates than OLCI in these water bodies. The proposed TSS estimation method was applied to MSI images to produce TSS time-series in Lake Kasumigaura, which showed good agreements with in situ and OLCI-derived TSS time-series.

Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice

Abstract. The CryoSat-2 radar altimeter and ICESat-2 laser altimeter can provide complementary measurements of the freeboard and thickness of Arctic sea ice. However, both sensors face significant challenges for accurately measuring the ice freeboard when the sea ice is melting in summer months. Here, we used crossover points between CryoSat-2 and ICESat-2 to compare elevation retrievals over summer sea ice between 2018–2021. We focused on the electromagnetic (EM) bias documented in CryoSat-2 measurements, associated with surface melt ponds over summer sea ice which cause the radar altimeter to underestimate elevation. The laser altimeter of ICESat-2 is not susceptible to this bias but has other biases associated with melt ponds. So, we compared the elevation difference and reflectance statistics between the two satellites. We found that CryoSat-2 underestimated elevation compared to ICESat-2 by a median difference of 2.4 cm and by a median absolute deviation of 5.3 cm, while the differences between individual ICESat-2 beams and CryoSat-2 ranged between 1–3.5 cm. Spatial and temporal patterns of the bias were compared to surface roughness information derived from the ICESat-2 elevation data, the ICESat-2 photon rate (surface reflectivity), the CryoSat-2 backscatter, and the melt pond fraction derived from Sentinel-3 Ocean and Land Color Instrument (OLCI) data. We found good agreement between theoretical predictions of the CryoSat-2 EM melt pond bias and our new observations; however, at typical roughness <0.1 m the experimentally measured bias was larger (5–10 cm) compared to biases resulting from the theoretical simulations (0–5 cm). This intercomparison will be valuable for interpreting and improving the summer sea ice freeboard retrievals from both altimeters.

A new approach to improve precipitable water vapour estimations of Sentinel-3A satellite data using LST, elevation and NDVI over Iran

ABSTRACT The total precipitable water vapour (TPW) extraction algorithm using the Sentinel-3A Ocean and Land Colour Instrument (OLCI) has the potential to be improved on a regional scale. The aim of this study was to improve the TPW extraction algorithm of OLCI for the first time using environmental variables, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and elevation from mean sea level, on a regional scale over Iran. A previously developed TPW recovery algorithm was applied to the OLCI data during cloud-free days throughout a one-year period (1 January–29 December 2020). The artificial neural network (ANN) methodology was utilized in eight models. The evaluation results revealed the effectiveness of the models varied based on the topography and climate of each station. The assessment findings demonstrated that model 2, which integrated LST, elevation, and NDVI data in the ANN framework, outperformed other models across the study area.

Application of Satellite-Derived Summer Bloom Indicators for Estonian Coastal Waters of the Baltic Sea

The aim of this study was to test and develop the indicators for the remote sensing assessment of cyanobacterial blooms as an input to the estimation of eutrophication and the environmental status (ES) under the Marine Strategy Framework Directive (MSFD) in the optically varying Estonian coastal regions (the Baltic Sea). Here, the assessment of cyanobacteria blooms considered the chlorophyll-a (chl-a), turbidity, and biomass of N2-fixing cyanobacteria. The Sentinel-3 A/B Ocean and Land Colour Instrument (OLCI) data and Case-2 Regional CoastColour (C2RCC) processor were used for chl-a and turbidity detection. The ES was assessed using four methods: the Phytoplankton Intensity Index (PII), the Cyanobacterial Surface Accumulations Index (CSA), and two variants of the Cyanobacterial Bloom Indicator (CyaBI) either with in situ-measured cyanobacterial biomass or with satellite-estimated cyanobacterial biomass. The threshold values for each coastal area ES assessment are presented. During 2022, the NW Gulf of Riga reached good ES, but most of the 16 coastal areas failed to achieve good ES according to one or multiple indices. Overall, the CyaBI gives the most comprehensive assessment of cyanobacteria blooms, with the CyaBI (in situ) being the best suited for naturally turbid areas. The CyaBI (satellite) could be more useful than in situ in large open areas, where the coverage of in situ sampling is insufficient.

Remote estimation of phycocyanin concentration in inland waters based on optical classification

In recent years, under the dual pressure of climate change and human activities, the cyanobacteria blooms in inland waters have become a threat to global aquatic ecosystems and the environment. Phycocyanin (PC), a diagnostic pigment of cyanobacteria, plays an essential role in the detection and early warning of cyanobacterial blooms. In this context, accurate estimation of PC concentration in turbid waters by remote sensing is challenging due to optical complexity and weak optical signal. In this study, we collected a comprehensive dataset of 640 pairs of in situ measured pigment concentration and the Ocean and Land Color Instrument (OLCI) reflectance from 25 lakes and reservoirs in China during 2020–2022. We then developed a framework consisting of the water optical classification algorithm and three candidate algorithms: baseline height, band ratio, and three-band algorithm. The optical classification method used remote sensing reflectance (Rrs) baseline height in three bands: Rrs(560), Rrs(647) and Rrs(709) to classify the samples into five types, each with a specific spectral shape and water quality character. The improvement of PC estimation accuracy for optically classified waters was shown by comparison with unclassified waters with RMSE = 72.6 μg L−1, MAPE = 80.4 %, especially for the samples with low PC concentration. The results show that the band ratio algorithm has a strong universality, which is suitable for medium turbid and clean water. In addition, the three-band algorithm is only suitable for medium turbid water, and the line height algorithm is only suitable for high PC content water. Furthermore, the five distinguished types with significant differences in the value of the PC/Chla ratio well indicated the risk rank assessment of cyanobacteria. In conclusion, the proposed framework in this paper solved the problem of PC estimation accuracy problem in optically complex waters and provided a new strategy for water quality inversion in inland waters.

Seasonal and spatial variability in bio-optical properties of the Persian Gulf: Implications for ocean color remote sensing

In this study, the absorption coefficients of phytoplankton community (aPh(λ)), Colored Dissolved Organic Matter (CDOM) [aCDOM(λ)], and Non-Algal Particles (NAP) [aNap(λ)] were analyzed in the northern Persian Gulf, a Mediterranean-type semi-enclosed marginal sea, through seven cruise expeditions conducted from May 2018 to February 2022. The highest and lowest aPh(443) values were observed in the shallow area (depth ≤20 m) during the wet season (November–April) (0.043 ± 0.004 m-1) and within the deep area (depth >20 m) through the dry season (July–September) (0.024 ± 0.003 m-1), respectively. The chlorophyll-specific absorption (a*Ph(λ)) values increased from the deep to shallow areas as well as the wet to dry seasons. A significant difference was further detected in a*Ph(λ) magnitude at the blue (443 nm) and red (675 nm) wavelengths, with the mean values of 0.0124/0.0223 m2 mg−1 and 0.0069/0.0135 m2 mg−1 in the wet/dry seasons, respectively. The mean values of aCDOM(443) in the shallow area during the wet season (0.10 ± 0.001 m-1) were significantly larger than those in the deep area during the dry season (0.06 ± 0.002 m-1). Similarly, aNAP(443) decreased from the shallow (0.056 ± 0.021 m-1) to deep areas (0.019 ± 0.011 m-1), and from the wet (0.035 ± 0.020 m-1) to dry (0.029 ± 0.015 m-1) seasons. The statistical analysis revealed that the differences in aCDOM(λ) and aNAP(λ) spectral distribution were not statistically significant for the duration of the wet and dry seasons, whereas significant differences were observed in the shallow and deep samples. During both wet and dry seasons, the magnitude of the blue-green absorption spectra was dominated by CDOM, but the red bands were associated with phytoplankton dominated samples. The ratio of aCDOM(λ)/a*Ph(λ) was utilized as an index for uncertainty assessment to estimate Chl-a concentrations by the National Aeronautics and Space Administration (NASA) standard Chl-a algorithms for the Moderate Resolution Imaging Spectrometer (MODIS; OC3M) and Sentinel-3 Ocean and Land Color Instrument (OLCI) (OLCI; OC4v6). The results additionally demonstrated that the satellite-derived Chl-a was overestimated due to higher aCDOM(λ) and underestimated owing to lower a*Ph(λ) at the low (<0.5 mg m−3) and high (>3 mg m−3) concentrations of Chl-a, respectively. The study findings established that the red bands should be considered for tuning or developing new Chl-a algorithms in the Persian Gulf.