HawkEye was an ocean color instrument onboard SeaHawk, the first dedicated Ocean Color CubeSat, launched in 2018 to provide targeted high spatial resolution (∼130 m) images of open ocean, coastal, inland, and estuarine waters with eight spectral bands in the visible NIR region. This study presents the first comprehensive assessment of HawkEye standard Level-2 remote sensing reflectance (R_rs) products. A total of 51 cloud-free HawkEye scenes over 15 globally distributed inland and coastal water sites with contrasting optical conditions were found to be suitable for match-up analysis, spanning two years. Compared to R_rs from ocean color component of the Aerosol Robotic Network (AERONET- OC) and in situ hyperspectral measurements from coastal Georgia waters (USA), HawkEye R_rs accuracy varied between 31 and 61 % for Median Symmetric Accuracy (MdSA) depending on the wavelength. The R_rs at 510 nm was the most accurate (31 % MdSA), and the accuracy decreased towards the blue bands, with 412 nm being the least accurate (61.7 % MdSA). HawkEye R_rs is more accurate in low Chlorophyll-a waters compared to high Chlorophyll-a waters. HawkEye products accuracy improved with a site-specific atmospheric correction (e.g. the Management Unit of the North Sea Mathematical Models (MUMM) correction improved results in two turbid coastal waters tested). HawkEye R_rs product accuracy was comparable with ocean color sensors, OLCI, AQUA and VIIRS. These results thus indicate that HawkEye imagery is suitable for aquatic remote sensing applications. Further, the results serve as a reference and offer potential areas of improvement for future ocean color CubeSat missions.

The use of high-resolution data in aquatic applications increased significantly in the last decade with the launch of decametre-scale optical sensors. More recently, commercial very-high resolution (VHR) sensors, offering finer spatial and temporal resolutions, have shown the potential of complementing data from high-resolution missions. Planet SuperDove (SD), with a band-setting similar to the Copernicus Sentinel-2 MultiSpectral Instrument (S2-MSI), a 3-m spatial resolution and quasi-daily revisiting time, show the potential for widening water monitoring applications to smaller water basins, and finer-scale phenomena. However, the uncertainties in SD products need to be quantified, to assess their fitness-for-purpose for these applications. This work aims to provide uncertainty estimates for SD-derived aquatic remote sensing reflectance (R_RS) in different water types, benefitting from the radiometric measurements of the AERONET-OC network. R_RS was derived from both Surface Reflectance (SR) products, distributed by Planet, or from data processed with ACOLITE. The comparability between SD and S2-MSI products was also assessed comparing R_RS and Rayleigh-corrected reflectance (R_RC) from S2-MSI and SD. The results indicate generally low performance across all bands for both SD R_RS products, except in the most turbid waters, and highlight the lack of a publicly available robust atmospheric correction processor for SD data for most optical water types. The comparison to S2-MSI shows promising results only when comparing R_RC values, but differences still suggest issues associated with calibration and radiometry of the SD sensors. The results also highlight the need for a harmonization strategy to ensure consistent integration of these datasets within multi-source monitoring systems.

This study reports the outcomes of the third Atmospheric Correction Intercomparison Exercise (ACIX-III Aqua), which evaluated the performance of atmospheric correction (AC) methods for hyperspectral PRISMA satellite data over inland and coastal waters. The exercise included five AC processors (ACOLITE, hGRS, iCOR, MIP, and POLYMER), the standard PRISMA Level 2C product, and an adjacency correction tool (T-Mart) tested with ACOLITE. A total of 239 cloud-free PRISMA scenes from 2019 to 2024 were compared with in situ data of remote sensing reflectance, gathered from both hyperspectral and multispectral radiometers across eight distinct optical water types (OWTs). The accuracy of each AC method varied with spectral band, but all showed largest and lowest discrepancies with in situ data at 443 nm and 560 nm, respectively. All AC methods showed the best agreement with in situ data in greenish waters (OWT 4b) and highest uncertainties were yielded in humic-rich waters (OWT 7). Consistently with the previous ACIX-Aqua study focused on multispectral data, no single AC method outperformed the others across all OWTs. The study confirmed the ongoing challenges of AC over optically complex waters, yet the exercise allowed the community to advance in developing AC methods for hyperspectral satellite images and supporting the development of future operational hyperspectral missions, such as PRISMA Second Generation (PRISMA 2G) and CHIME.

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region.

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, aCDOM (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.

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.

The Aerosol Robotic Network - Ocean Color (AERONET-OC) instrument located at the Pålgrunden site in Lake Vänern provides values of remote sensing spectral reflectance R_RS(λ) since 2008. These in situ R_RS(λ) indicated a temporal increase from 2015 at center-wavelengths in the green and red spectral regions. To investigate the environmental and climate processes responsible for this increase, water color trends in Lake Vänern were analyzed considering in situ limnological measurements, meteo-climatic quantities and additionally satellite-derived data products from the Moderate Resolution Imaging Spectroradiometer on board the Aqua platform (MODIS-A). Satellite ocean color R_RS(λ) data assessed against in situ R_RS(λ) from the Pålgrunden site showed satisfactory agreement at a number of spectral bands. Relying on these validation results, comprehensive statistical analysis were performed using MODIS-A R_RS(λ). These indicated periodical changes between 2002 and 2021 with clear minima occurring between 2010 and 2013. The complementary analyses of temporal changes characterizing limological and meteo-climatic quantities, and also relationships between these quantities and R_RS(λ), indicated the existence of complex and concurrent bio-geochemical processes influencing water color in Lake Vänern. In particular, significant correlations were observed between R_RS(λ) and turbidity, and also between R_RS(λ) and total biovolume. Additionally, an early warming of Lake Vänern surface waters was identified since spring 2014. This occurrence could potentially affect the vertical mixing and water exchange between turbid coastal and pelagic waters with implications for phytoplankton phenology.

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022).

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automatic in situ observations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

Satellites have generated extensive data of remote sensing reflectance spectra (R_rs(λ)) covering diverse water classes or types across global waters. Spectral classification of satellite R_rs(λ) data allows for the distinguishing and grouping of waters with characteristic bio-optical/biogeochemical features that may influence the productivity of a given water body. This study reports new satellite water class products (Level-2 and Level-3) from the Visible Infrared Imaging Radiometer Suite (VIIRS). We developed and implemented a hyperspectral scheme that accounts for the R_rs(λ) spectral shapes and globally resolves oceanic, coastal, and inland waters into 23 water classes. We characterized the light absorption and scattering coefficients, chlorophyll-a concentration, diffuse attenuation coefficient, and suspended particulate matter for individual water classes. It is shown that the water classes are separable by their distinct bio-optical and biogeochemical properties. Furthermore, validation result suggests that the VIIRS water class products are accurate globally. Finally, we examined the spatial and temporal variability of the water classes in case studies for a demonstration of applications. The water class data in open oceans reveal that the subtropical ocean gyres have experienced dramatic expansion over the last decade. In addition, the water class data appear to be a valuable (and qualitative) indicator for water quality in coastal and inland waters with compelling evidence. We stress that this new satellite product is an excellent addition to the aquatic science database, despite the need for continuous improvement toward perfection.