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Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturate...
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... with MODISA showing the lowest noise and MERIS-FR (full resolution) showing the highest noise [note that MERIS-RR (reduced resolution) data have much higher SNRs than MERIS-FR data and therefore would not lead to the image noise shown here; see Section 4]. Using a simple method (see Section 5), the RMS noises from adjacent pixels for various Chl values were quantified and listed in Table 1. For low-Chl waters (0.01 to 0.2 mg m −3 ) the relative pixelization noise decreased with increasing concen- tration. ...
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... ratio be- tween the mean RMS error and the predefined Chl value was used to represent the relative pixelization noise (in percentage). Results are listed in Table 1. For each predefined Chl, relative noise increased from MODISA to SeaWiFS to MERIS-FR. ...
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... demonstrated in the Chl data product for the noise-induced errors, most errors originated from the NIR atmospheric correction bands rather than from the blue-green bands that were used in the bio-optical inversion [4]. This explains why MODISA Chl showed much lower pixelization noise than Sea- WiFS Chl (Table 1), as MODISA SNRs in the NIR bands (748 and 869 nm) are much higher than Sea- WiFS SNRs in the corresponding NIR bands (765 and 865 nm). However, the same principle cannot ex- plain why MERIS-FR Chl showed much higher pix- elization noise than SeaWiFS Chl or why MERIS-RR Chl showed much higher pixelization noise than MODISA Chl. ...
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... is clear that MODISA SNRs are sufficient to re- solve very small changes (<10%) even at extremely low Chl concentrations (∼0.03 mg m −1 ; Table 1). When the new band-subtraction algorithm was used, the sensitivity was even higher to resolve changes of <5% for Chl between 0.01 and 0.4 mg m −3 ( [33]; Fig. 10). ...
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... Radiation resolution can usually be characterized by the signal-to-noise ratio (SNR) or noise equivalent radiance (NEΔL). Since the water signal is low, comprising only one-tenth of the total top of atmosphere radiation received by the sensor, the radiometric noise of the sensor can significantly impact estimations of inland lake water quality parameters (with errors up to 80 %) (Gordon and Castaño, 1987;Hu et al., 2012b). A higher SNR improves the quality of the data and improves the ability of the sensor to capture subtle variations in water components (Wang et al., 2009). ...
... In contrast, a great amount of noise leads to severe degradation of data quality, sharply reducing the useful signal in remote sensing images (Qi et al., 2017). Many studies have used the local variance approach to assess the SNR of various sensors, and found that (Cao et al., 2018;Hu et al., 2012a;Hu et al., 2012b;Li et al., 2015;Qi et al., 2017), the radiometric performance of sensors with relatively high spatial resolution is affected by low quantization levels and SNRs compared to ocean satellite sensors (Li et al., 2015). Hence, can the sensor's radiation resolution of HJ-2 CCD adequately capture the weak spectral information required for C Chla monitoring? ...
... (1) Pure image windows were extracted using the Canny edge detector operator (Fig. 3) to ensure that the selected pixels refer to oligotrophic waters where C Chla are below 0.1 mg/L. The image was segmented using a 3 × 3 window excluding edge pixels and abnormally high value pixels, to exclude effects from sun glint, clouds, and other factors (Cao et al., 2018;Hu et al., 2012b;Li et al., 2015). (2) Histogram statistics. ...
HJ-2 (Huanjing-2) was launched in September 2020. It has a charge-coupled device (CCD) that offers a spatial resolution of 16 m and a revisiting time of two days, making it an excellent option for environmental and disaster monitoring. However, more evaluation is needed for the ability of CCD to monitor water quality. As chlorophyll a concentration (CChla) is the main indicator for water quality evaluation, in this study, the performance of the CCD sensor to monitor CChla has been verified, with a particular focus on typical eutrophic lakes. The essential findings can be summarized as follows: (1) the CCD sensor has a signal-to-noise ratio (SNR) greater than 400 in all bands except the green band, indicating that it meets the requirements for water quality monitoring; (2) the semi-empirical estimation model of CChla derived from HJ-2 CCD has a mean absolute deviation (MAD) of 8.18 μg/L, root mean square deviation (RMSD) of 10.66 μg/L and mean absolute percentage deviation (MAPD) of 20.93 %; (3) the higher temporal and spatial resolution of the CCD data enables effective monitoring of water quality parameters characterized by high spatiotemporal variations; (4) HJ-2 CCD exhibits strong consistency with Sentinel-2 MSI and Sentinel-3 OLCI across various bands, with most bands showing correlation coefficients exceeding 0.8 and a MAPD below 30 %. This study reveals the significant potential and practical value of HJ-2 CCD data for monitoring and assessing water quality, providing important information for the design and development of next-generation satellites.
... Recent studies have found that the multispectral measurements from the geostationary GOES ABI sensor can monitor the aquatic ecosystems with similar spatial variability to the corresponding MODIS observations, demonstrating the potential of geostationary observations to enhance polar-orbiting observations (Houskeeper et al., 2022). However, limited by their lower SNRs than that of MODIS (1000) in the NIR wavelengths (Hu et al., 2012;Schmit et al., 2017), the GOES ABI data show relatively low detectability of Sargassum features comparing to MODIS images (Minghelli et al., 2021). The polar-orbiting VIIRS measurements have been used to enhance the detectability of geostationary GOES data for monitoring surface chlorophyll concentration changes (Zheng et al., 2023). ...
... Likewise, the relatively low signal-to-noise ratios (SNRs) of the Dove sensors may limit their ability to detect weak surface scums (Table II). Although Dove sensors have much higher SNRs than those of the Landsat-7/ETM+ that has been shown useful in detecting cyanobacterial blooms [34], [37], [38], they are much lower than those of medium resolution sensors such as MODIS/Aqua [62] (Table II) and other popular ocean color sensors. This, however, is the limitation of the sensor itself as opposed to the DL model. ...
Freshwater cyanobacterial blooms pose a major threat to local ecosystems, economies, and public health. Monitoring these occurrences is essential for water resource managers worldwide. Satellite remote sensing techniques can detect and quantify blooms in large inland and estuarine water bodies but monitoring blooms in small water bodies (<10 km2), including narrow river/canal systems, remains challenging. This is due to the coarse spatial resolution (>300 m) or low re-visit frequency (>10 days) of most operational satellite sensors. The ephemeral nature of cyanobacterial blooms and form dense surface mats (or ‘scums’) that aggregate nearshore further highlight the need for sensors with higher spatial and temporal resolutions. In this study, a deep learning model based on Convolutional Neural Network U-net was developed to detect cyanobacterial blooms (i.e., scums) in the highly modified and managed Caloosahatchee River (i.e., C-43 canal) and Caloosahatchee River Estuary (CRE) (Florida, USA) using Dove imagery (3-m resolution) obtained near-daily from the PlanetScope satellite constellation. The approach consisted of three steps: 1) training and validating the U-net model with “ground truth” images; 2) classifying bloom pixels; and 3) quantifying bloom area using linear unmixing. Validation results indicate an overall F1 score of 89.6% when assessing bloom area. Application of the model revealed the westward expansion of a cyanobacteria bloom from C-43 to CRE in summer 2018, indicating the physical transport of the bloom originating upstream in Lake Okeechobee to the estuary. This approach was tested on other inland water bodies, indicating potential for monitoring cyanobacterial blooms on a global scale.
... Despite coarser spatial resolution than some polar orbiting platforms currently used for water quality applications (i.e., 300 m Sentinel-3 and ≤ 60 m Sentinel-2), geostationary satellites provide improved temporal resolution and the potential for dynamic signal-to-noise ratios (SNR) across spectral bands by altering the integration (i.e., dwell) time (Hu et al., 2012). The time in which GLIMR may acquire an image with similar spatial coverage of a polar orbiting platform depends on the swath width of that satellite. ...
The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr − 1 , while oil films require glint >10 − 5 sr − 1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites , such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.
... Fuxian Lake, a clean water body in the Yunnan-Guizhou Plateau region (Fig. 1b), was selected for the SNR estimation (Cao et al., 2022b). The main steps were: (1) Using the Canny operator to extract the pure window of the image (Fig. 1d); (2) Screening the image pixels in the range within a 3*3 window, with a threshold set at 1.002 (Hu et al., 2012a); (3) Calculating the standard deviation within this region as the 'noise estimate' (LSD) and determining the mean value as the 'signal estimate' (DN). The SNR was calculated as follows: ...
... According to previous studies (Hu et al., 2012a;Lee et al., 2014b), a high SNR in a satellite sensor can be achieved by having a low spatial resolution (e.g., 1000 m for MODIS) or a wide spectral band (e.g., 50-100 nm for Landsat). This allows enough photons to ensure image quality. ...
It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.
... Then, OLI and OLI2 SNRs were computed from spatially uniform clear waters by averaging the locally computed mean (μ) to standard deviation (σ) ratio (i.e., μ / σ ) from the five SNO scene pairs. The local mean and standard deviation were calculated by applying a running 3 × 3 element window as larger window sizes (e.g., 5 × 5, 7 × 7, etc.) increase inter-pixel heterogeneity, amplifying random noise in the analysis (Hu et al., 2012). Note that the SNRs were calculated from the same ROIs in the underfly scene pairs to allow for a fair comparison. ...
... SNRs are 7 to 30% higher than OLI in the ρ t product, likely due to the 14-bit quantization rate of OLI2 versus 12-bit OLI observations. underscored that this SNR analysis was conducted with selected scene pairs over oligotrophic lakes and blue coastal waters with spatially uniform waters (Section 2.3) to prevent the underestimation of SNR owing to the natural variability common in eutrophic and/or turbid waters (Hu et al., 2012;Nasiha et al., 2022). s. ...
... ellites/landsat-next/), which is scheduled for launch in 2030, and will allow for enhanced aquatic science and applications (Wulder et al., 2022). Note that the OLI and OLI2 SNRs are still well below the threshold requirements (e.g., SNR of 1000) for the blue bands of OC instruments (Hu et al., 2012). Further, future focal plane designs may consider sensor designs other than pushbroom architectures to minimize the need for numerous detectors for a wide-swath coverage. ...
With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11-17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρ t) and atmospherically corrected reflectance (aquatic reflectance; ρ AR w) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρ AR w products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρ w product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρ AR w , ρ ac w , and ρ pol w , respectively. Overall, we found the OLI2-OLI ρ t products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ~0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ~2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρ w (ρ AR w) were estimated to be ~2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7-30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρ w from 0.3 to 5.3% (e.g., Δρ AR w (443 nm, ΔVZA, ΔRAA) = ρ AR,OLI w (443, +6.3,10) − ρ AR,OLI2 w (443, +3.9,− 13) < 0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρ ac w are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.
... Satellite observations of FLH were divided by satellite-derived chlorophyll a concentrations to retrieve F /Chl passive sat . We also excluded pixels with chlorophyll a concentrations <0.1 or >0.4 mg m −3 , which bounds the estimated lower FLH detection limit 18,62,63 as well as more uncertain chlorophyll a concentration retrievals 64 and upper levels where the relationship between chlorophyll a and phytoplankton light absorption become increasingly nonlinear 65,66 . Within this chlorophyll a range, the relationship between chlorophyll a concentrations and phytoplankton light absorption is indistinguishable from linear 66 ; therefore, an attempt to convert chlorophyll a to absorption is not necessary in this case (and could possibly introduce error due to the impact of high chlorophyll a values on chlorophyll a versus absorption equations developed using datasets from the global ocean 66 ). ...
... For the time series analyses of equatorial Pacific F /Chl passive sat (that is, Fig. 4c,d,f), we used the Niño3 box where F /Chl passive sat was validated, and where chlorophyll a consistently falls within the applied chlorophyll a thresholds (in >90% images <5% pixels fall outside this range; including these pixels does not change trends). In Fig. 3d, F /Chl passive sat pixels were averaged over 100-km-radii circles around each ship track sampling point, to reduce noise in signals derived from individual pixels 63 ...
Projected responses of ocean net primary productivity to climate change are highly uncertain¹. Models suggest that the climate sensitivity of phytoplankton nutrient limitation in the low-latitude Pacific Ocean plays a crucial role1–3, but this is poorly constrained by observations⁴. Here we show that changes in physical forcing drove coherent fluctuations in the strength of equatorial Pacific iron limitation through multiple El Niño/Southern Oscillation (ENSO) cycles, but that this was overestimated twofold by a state-of-the-art climate model. Our assessment was enabled by first using a combination of field nutrient-addition experiments, proteomics and above-water hyperspectral radiometry to show that phytoplankton physiological responses to iron limitation led to approximately threefold changes in chlorophyll-normalized phytoplankton fluorescence. We then exploited the >18-year satellite fluorescence record to quantify climate-induced nutrient limitation variability. Such synoptic constraints provide a powerful approach for benchmarking the realism of model projections of net primary productivity to climate changes.
... The converted xy coordinates for macroalgae were located in the purple-red gamut of the CIE diagram, which was far below the predefined boundary (Extended Data Fig. 1). Moreover, our algorithm is not affected by highly turbid waters for the following two reasons: first, extremely high turbidity tends to saturate the MODIS ocean bands 64 , which can be easily excluded; second, without a fluorescence peak, the reflectance of unsaturated turbid waters, after conversion to CIE coordinates, will be located below the predefined boundary (see example in Extended Data Fig. 1b). We also confirmed that the spectral shapes of coccolithophore blooms are different from dinoflagellates and diatoms (see example in Extended Data Fig. 1b), and thus they are excluded from our algorithm. ...
Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems1,2—yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly (P < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions.
... The signal-to-noise ratio (SNR) of a spectrometer is a very important metric when using micro-spectrometers for the spectral acquisition of water bodies. The current international water color satellites MODIS Terra/Aqua (spatial resolution: 250 m and 500 m), MERIS Envisat (spatial resolution: 300 m), VIIRS (spatial resolution: 750 m) and Sentinel-3A/B (spatial resolution: 300 m) all have SNRs greater than 1000 for measurements that occur in the ocean [31]; measurements in inland lakes mainly use Landsat OLI (spatial resolution: 30 m) HJ-1 (spatial resolution: 30 m), etc., all of which have SNRs greater than 100 [32]. The use of Landsat as a data source for lake water observations on the Qinghai-Tibet Plateau requires that the signal-to-noise ratio of the micro-spectrometer be greater than 100 for application in field measurements. ...
Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies.
... Compared to the hyperspectral sensor HICO, PRISMA offers a finer GSD (HICO's GSD is about 90 m at nadir), enabling the mapping of smaller inland aquatic areas and nearshore waters. Furthermore, the requirements for ocean colour missions are SNR > 600 for the NIR bands and > 1000 for the UV-VIS bands (Hu et al., 2012;Mouw et al., 2015) while future imaging spectrometers will have SNR > 400 in the VNIR (NASEM, 2018). This seems to suggest that techniques to improve the SNR L TOA might be required for specific applications which need higher values of SNR. ...
Hyperspectral remote sensing reflectance (Rrs) derived from PRISMA in the visible and infrared range was evaluated for two inland and coastal water sites using above-water in situ reflectance measurements from autonomous hyper- and multispectral radiometer systems. We compared the Level 2D (L2D) surface reflectance, a standard product distributed by the Italian Space Agency (ASI), as well as outputs from ACOLITE/DSF, now adapted for processing of PRISMA imagery. Near-coincident Sentinel-3 OLCI (S3/OLCI) observations were also compared as it is a frequent data source for inland and coastal water remote sensing applications, with a strong calibration and validation record. In situ measurements from two optically diverse sites in Italy, equipped with fixed autonomous hyperspectral radiometer systems, were used: the REmote Sensing for Trasimeno lake Observatory (RESTO), positioned in a shallow and turbid lake in Central Italy, and the Acqua Alta Oceanographic Tower (AAOT), located 15 km offshore from the lagoon of Venice in the Adriatic Sea, which is characterised by clear to moderately turbid waters. 20 PRISMA images were available for the match-up analysis across both sites. Good performance of L2D was found for RESTO, with the lowest relative (Mean Absolute Percentage Difference, MAPD < 25%) and absolute errors (Bias < 0.002) in the bands between 500 and 680 nm, with similar performance for ACOLITE. The lowest median and interquartile ranges of spectral angle (SA < 8°) denoted a more similar shape to the RESTO in situ data, indicating pigment absorption retrievals should be possible. ACOLITE showed better statistical performance at AAOT compared to L2D, providing R² > 0.5, Bias < 0.0015 and MAPD < 35%, in the range between 470 and 580 nm, i.e. in the spectral range with highest reflectances. The addition of a SWIR based sun-glint correction to the default atmospheric correction implemented in ACOLITE further improved performance at AAOT, with lower uncertainties and closer spectral similarity to the in situ measurements, suggesting that ACOLITE with glint correction was able to best reproduce the spectral shape of in situ data at AAOT. We found good results for PRISMA Rrs retrieval in our study sites, and hence demonstrated the use of PRISMA for aquatic ecosystem mapping. Further studies are needed to analyse performance in other water bodies, over a wider range of optical properties.
