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Article

Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy

by
Alice Fabbretto
1,2,*,
Mariano Bresciani
1,
Andrea Pellegrino
1,3,
Kersti Kangro
2,
Anna Joelle Greife
1,4,
Lodovica Panizza
1,
François Steinmetz
5,
Joel Kuusk
2,
Claudia Giardino
1 and
Krista Alikas
2
1
Institute of Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy
2
Department of Remote Sensing, Tartu Observatory, University of Tartu, 61602 Tõravere, Estonia
3
Department of Engineering, University of Sapienza, 00185 Rome, Italy
4
Department of Environmental Science and Policy, University of Milan, 20133 Milan, Italy
5
HYGEOS, 59000 Lille, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(15), 8357; https://doi.org/10.3390/app15158357
Submission received: 1 July 2025 / Revised: 22 July 2025 / Accepted: 26 July 2025 / Published: 27 July 2025
(This article belongs to the Special Issue Applications of Remote Sensing for Natural Hazard and Environment Monitoring)
Browse Figures
Versions Notes

Abstract

This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Võrtsjärv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance (Rrs), optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle < 16°. Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale.

1. Introduction

Inland water ecosystems play pivotal roles by offering a wide range of ecosystem services. They are dynamic and optically complex, with their water quality being shaped by a range of physical and biochemical processes of anthropogenic or climatic origin, which vary significantly across time and space [1,2,3,4,5,6]. Over the past few decades, satellite-based remote sensing has become a powerful tool for studying aquatic ecosystems, offering consistent and large-scale coverage across space and time [7,8]. Remote sensing data, which are especially valuable for providing both historical and near-real-time information on water changes, complement in situ measurements [9]. While in situ methods enable accurate identification of water quality parameters, they are labor-intensive and costly, limiting their applicability for large-scale monitoring [10]. The key to assessing water quality using remote sensing methods is to analyze the interactions between the concentrations of water constituents and the spectral signals detected by the sensors [11]. In aquatic remote sensing, the radiometric quantity remote sensing reflectance (Rrs)—defined as a function of specific inherent optical properties (e.g., spectral absorption and backscattering forms [12]) and water constituent concentrations—is used for water quality analysis [13]. The optical domain has been explored for the detection of optically active water constituents, including chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) [14], which can be inferred from stronger absorption in the blue part of the spectrum and its exponential decrease with increasing wavelength [15]. In recent decades, the synoptic recovery of these parameters on a fine scale and with high frequency has been made possible thanks to the latest generation of medium- and high-resolution multispectral sensors, such as the Sentinel-2 (S2), Sentinel-3 (S3), and Landsat satellites [16,17,18,19,20]. Nevertheless, to increase the accuracy of estimates of variables currently observed by multispectral sensors, imaging spectroscopy provides useful information [21,22,23,24,25,26,27,28,29,30,31]. This technique enables the detection of additional variables of interest for multiple user-oriented applications, such as phycocyanin (PC)—an auxiliary photosynthetic pigment with a significant presence in cyanobacteria [32]. Due to the specific absorption characteristics of PC, remote sensing of freshwater cyanobacterial biomass has largely focused on algorithms developed using hyperspectral reflectance [33,34,35]. Therefore, the synergistic use of hyperspectral images with established multispectral data could support future efforts to assess the responses of lakes to environmental and anthropogenic pressures [36,37]. Additionally, the fusion of hyperspectral data with high-frequency in situ measurements offers new opportunities for ecological modeling, enabling researchers to track spatial patterns, understand underlying processes, and forecast future changes in aquatic ecosystems with greater precision [38,39,40,41]. The advent of advanced hyperspectral sensors, such as PRecursore IperSpettrale della Missione Applicativa (PRISMA) and the Environmental Mapping and Analysis Program (EnMAP), is allowing many aquatic remote sensing challenges to be overcome, such as gaps in technical capabilities—particularly in processing, interpreting, and integrating data with field measurements [42,43,44,45,46,47,48,49,50,51,52,53].
The focus of this work is to present an approach for the exploitation of data collected from two currently orbiting European spaceborne hyperspectral missions—PRISMA and EnMAP—which could support the study of the quality of two shallow and eutrophic Estonian lakes (Lake Peipsi and Lake Võrtsjärv). The two study areas play a crucial role in Estonia’s internal water system, in the context of which very few studies have tested the performance of hyperspectral sensors. In total, nine applications performed on 12 images (six of Lake Peipsi and six of Lake Võrtsjärv) are presented, including Rrs validation, classification of optical water types (OWTs), PC concentration estimation, macrophyte detection, and characterization of reflectance when lakes are covered by ice and snow. For Rrs validation, S2 and S3 multispectral data and in situ measurements (from fieldwork and the HYPSTAR spectroradiometer) were used as reference data. In addition, environmental data, i.e., lake surface water temperature, wind speed, and precipitation amount, were included to enrich the analysis. This study underscores the significant potential of high-resolution hyperspectral data in advancing the assessment of lake ecological status, providing an important foundation for interpretation of the results presented in the following sections.

2. Materials and Methods

2.1. Study Area

This study focuses on two of the largest and ecologically most significant freshwater bodies in Estonia: Lake Peipsi and Lake Võrtsjärv. These lakes are key components of Estonia’s inland water system and serve critical roles in regional biodiversity, hydrology, and socio-economic activities such as fishing and recreation [54]. Lake Peipsi is located on the eastern border of Estonia (shared with Russia) and is the fourth biggest lake in Europe, covering an area of approximately 3555 km2 [55]. The lake is part of a transboundary catchment and is hydrologically connected to the Narva River, which flows into the Gulf of Finland. It is divided into three parts: the mesotrophic Lake Peipsi sensu strictu (s.s.) in the north, the hypertrophic Lake Pihkva in the south, and their connection and river-like eutrophic Lämmijärv [56]. Different water types can be observed in the lake: clearest and deepest water in the northern part, turbid and brownish water near the river Emajõgi inflow, and phytoplankton-rich water in Lämmijärv and Lake Pihkva [57]; in the summertime, cyanobacterial blooms may be present throughout the entire lake [58]. Riverine transport constitutes the primary pathway for nutrient input into the lake. Over 80% of phosphorus and nitrogen compounds are introduced into Lake Peipsi via the Velikaya River, which carries treated wastewater from the Russian city of Pskov, and the Emajõgi River, which receives treated wastewater from the Estonian city of Tartu [59]. Lake Peipsi has a shallow basin with an average depth of 7.1 m, which makes it sensitive to nutrient loading and climatic fluctuations [60]. Lake Peipsi is ecologically diverse and serves as a critical habitat for numerous fish and bird species. However, the lake has also been subject to eutrophication and water quality degradation due to agricultural runoff and historical pollution [61]. The average values recorded in the lake from the national monitoring programme [62] in the period 2020–2024 for the main bio-optical components are: Chl-a 27 ± 21 mgm−3, TSM 10 ± 8 gm−3, and CDOM (443 nm) 3 ± 1.4 m−1.
Lake Võrtsjärv, located in south-central Estonia, is the largest lake entirely within the country’s borders, with a surface area of about 270 km2. It is significantly shallower than Lake Peipsi, with an average depth of 2.8 m. Lake Võrtsjärv is a very turbid, well-mixed, eutrophic, and non-stratified lake with high levels of phytoplankton, concentration of TSM, and absorption coefficient of CDOM. The lake is part of the Emajõgi River system, which connects it to Lake Peipsi. Lake Võrtsjärv is also a vital freshwater resource, it provides habitats for several protected species, and plays a key role in regional hydrological dynamics [63]. Due to its shallow nature, it is highly vulnerable to climatic variability, particularly changes in precipitation and evaporation rates. The littoral zone of Lake Võrtsjärv is covered by a wide belt of aquatic macrophytes, dominated by Phragmites australis, common reed, and other helophytes; notably, the southwestern region—thanks to its relative protection from prevailing winds—exhibits the highest density of aquatic vegetation [64]. In addition, it is characterized by ice cover that lasts on average more than 100 days, approximately from November to April [65]. The average values recorded in the lake from the national monitoring programme [66] in the period 2020–2024 for the main bio-optical components are: Chl-a 31 ± 14 mgm−3, TSM 18 ± 12 gm−3, and CDOM (443 nm) 2.8 ± 2 m−1.
Together, these two lakes provide a representative context for examining limnological processes, environmental changes, and human impacts in Northern European freshwater systems.
Ancillary variables such as lake surface water temperature, wind speed, and precipitation, retrieved from the ERA5 reanalysis dataset via the Copernicus Climate Data Store [67], supported the description of the study area. The collection of all these data supports a robust characterization of optical conditions across varying temporal and spatial scales. The study areas are presented in Figure 1: blue dots represent the validation points, while the blue boxes highlight the areas targeted for the applications and correspond to the identification numbers (IDs) listed in Table 1.

2.2. In Situ Data

In situ reflectance values and water samples were collected using different methodological approaches tailored to the characteristics and monitoring infrastructure of each site. Regarding reflectance data, in Lake Peipsi, data were collected through an ad-hoc field campaign conducted synchronous to the satellite overpass (18 July 2021), thanks to the ability to calculate the orbits of PRISMA in advance using a dedicated tool provided by the Italian Space Agency (ASI) [68]. During the field campaign, above-water reflectance measurements were acquired using the TriOS RAMSES instrument [58], which was mounted on a boat-based platform. The instrument was operated following standardized protocols to minimize sunglint and optimize observation geometry. Measurements were acquired under stable atmospheric conditions, typically between 10:00 and 14:00 local time to minimize variation due to changing solar zenith angle.
In Lake Võrtsjärv, data were acquired using a fixed autonomous monitoring station (HYPSTAR), synchronous to the satellite overpasses (20 June 2024, 21 July 2024); the instrument was deployed at the pier near the central part of the lake (58.211 N, 26.108 E). The HYPSTAR system is equipped with a hyperspectral radiometer featuring dual optical entrances, configured to measure both upwelling water-leaving radiance and downwelling irradiance, allowing for calculation of the water surface reflectance at high temporal resolution [69]. Measurements were recorded at 20-min intervals under automatic quality control routines to flag suboptimal conditions (e.g., cloud cover or sunglint).
Water samples were systematically collected during the fieldwork carried out in both lakes within the national monitoring programme, and subsequent laboratory analyses were conducted to determine the concentrations of Chl-a, TSM, and CDOM, as well as microscopic analysis for the characterization of phytoplankton species [70].

2.3. Satellite Data

In this study, hyperspectral spaceborne images were obtained from two advanced spaceborne missions: PRISMA and EnMAP. These sensors provide contiguous spectral measurements in the visible to short-wave infrared range, offering detailed information on the optical properties of inland and coastal waters that are crucial for assessing water quality parameters such as Chl-a, TSM, PC, and CDOM. PRISMA—developed and operated by ASI—was launched in 2019 and delivers hyperspectral imagery with 231 spectral bands spanning the 400–2500 nm spectral range at a spatial resolution of 30 m [71,72]. It is particularly valuable for environmental monitoring due to its ability to capture small variations in water color and biogeochemical processes [43]. EnMAP—launched in 2022 and operated by the German Aerospace Center (DLR)—provides 224 spectral bands across the 420–2450 nm range at a spatial resolution of 30 m. Designed for environmental and resource monitoring, EnMAP supports the detection of complex aquatic processes, including eutrophication and sediment transport, by capturing high-resolution spectral signatures [73]. The satellite also features a high signal-to-noise ratio and rigorous radiometric calibration, making it suitable for time-series analysis and comparative studies [74]. The combination of PRISMA and EnMAP data could enhance the ability to monitor inland waters at regional scales, enabling spatially comprehensive and temporally consistent assessments [75].
As mentioned above, S2 and S3 multispectral data were used as a reference to validate the hyperspectral data, highlighting the advantages that can be gained by integrating the latter to obtain a more accurate characterization of water quality. Rrs data gathered from the S2 Multispectral Instrument were used, considering 8 spectral bands (443–842 nm) spatially resampled at 10 m. Rrs data provided by S3 Ocean and Land Colour Instrument were also used, considering 12 spectral bands (442–865 nm) with spatial resolution of 300 m. S2 and S3 multispectral images were acquired on the same day as the PRISMA and EnMAP overpasses, or within one day thereof. The dataset of the available hyperspectral spaceborne images used in this study is summarized in the following table (Table 1), which includes the application IDs and aims, the acquiring sensors, and the acquisition dates.
Table 1. Spaceborne data of Lake Peipsi and Lake Võrtsjärv: application ID, application goal, sensor, acquisition date(s).
Table 1. Spaceborne data of Lake Peipsi and Lake Võrtsjärv: application ID, application goal, sensor, acquisition date(s).
Lake Peipsi
Appl. IDAppl. goalSensorDate(s)
1Rrs validationPRISMA19 September 2020, 8 June 2021, 18 July 2021, 21 May 2022
2OWT classificationPRISMA19 September 2020
3Velikaja River influencePRISMA24 June 2020, 9 May 2021
4Emajõgi River influenceEnMAP16 May 2024
5PC conc. map + S2PRISMA16 August 2022
Lake Võrtsjärv
Appl. IDAppl. goalSensorDate(s)
6Rrs validationEnMAP20 June 2024, 21 July 2024
7PC conc. map + S3PRISMA4 April 2020
8Aquatic vegetationEnMAP21 July 2024
9Ice coveragePRISMA18 March 2022
Regarding Sentinel data, two S2 images (22 July 2024 for Application n.6 and 17 August 2022 for Application n.5, see Table 1) and four S3 images (19 September 2020, 8 June 2021 and 21 May 2022 for Application n.1 and 4 April 2020 for Application n.7) were exploited in this study.

2.4. Data Processing

A structured processing workflow was designed to ensure the comparability of reflectance data across the entire dataset. The first step involved the atmospheric correction of all images, which converted top of atmosphere radiance to surface reflectance. For PRISMA images, L2C standard products supplied by ASI (land-based automatic atmospheric correction with MODTRAN [76]) were chosen for Lake Võrtsjärv, considering that they are suitable for waters similar to Lake Trasimeno [77], where they have previously been validated [47]. In contrast, for images of Lake Peipsi, atmospheric correction with POLYMER [78] was performed as the standard L2C products showed poor quality. Regarding EnMAP data, the standard L2A products provided by DLR (atmospherically corrected with MIP [79]) were found to be adequate when compared with reference measurements. Table 2 provides a summary of the atmospherically corrected products employed in this study.
Subsequently, spectral bands affected by noise were identified and removed, as suggested in [46]. Removing these bands ensured that only high-quality spectral information was retained for the analysis. The preserved spectral range (450–810 nm) was standardized for both sensors, resulting in 40 bands for PRISMA and 60 for EnMAP images.
Applications n.1 and n.6, referring to Rrs validation in Lake Peipsi and Lake Võrtsjärv, were performed by extracting spectral signatures from selected regions of interest (ROIs) in the hyperspectral images, defined as 5 × 5 pixel windows, in order to reduce the impact of local heterogeneity and to provide representative average spectra. For spatial consistency, ROIs measuring 15 × 15 pixels were selected in S2 images, while a single pixel was extracted in S3 images. In situ measurements affected by sunglint disturbance were corrected with the method proposed in [80]. The locations of the ROIs for the Rrs validation with both in situ measurements and Sentinel data are displayed as blue dots in Figure 1. Several statistical metrics were calculated to validate the Rrs data: these include the Root Mean Square Deviation (RMSD), the Mean Absolute Percentage Difference (MAPD), and the Spectral Angle (SA), with the latter used to determine the similarity between two spectra [44].
Application n.2, which relates to the classification of lake water pixels into OWTs, was performed with the method proposed in [81] by comparing each pixel’s reflectance spectrum to the library of references, applying a spectral distance metric (i.e., SA) to assign each pixel to the closest matching OWT class. This step was essential for characterizing the variety of optical properties across different regions of the image.
Applications n.3 and n.4 concern the analysis of the influence—in terms of spectral signatures—of the inflow of two rivers into Lake Peipsi [82,83]. Spectral signatures were extracted from selected 5 × 5 pixel ROIs, and environmental data (i.e., precipitation amount and wind speed) were downloaded from ERA5 to support the discussion.
Applications n.5 and n.7, which refer to the estimation of PC concentration in Lake Peipsi and Lake Võrtsjärv, were performed using the machine learning algorithm Mixture Density Network (MDN). The model was previously calibrated in [34] using in situ radiometric measurements resampled for both Hyperspectral Imager for the Coastal Ocean (HICO) and PRISMA sensors, covering water bodies around the world. Therefore, the MDN was applied in its default mode to PRISMA images, considering a specific subset of bands from 504 to 723 nm. The cloudy pixels were masked using the threshold proposed in [84]. Environmental data (i.e., lake surface water temperature) were downloaded from ERA5 to support the discussion.
Application n.8 concerns the detection of submerged, floating, and emergent macrophytes in Lake Võrtsjärv. Spectral signatures were extracted from selected 3 × 3 pixel ROIs and compared with the literature.
Application n.9, related to the estimation of the surface reflectance of an image acquired over Lake Võrtsjärv covered by ice and snow, was performed through the integration of surface reflectance across the visible and near-infrared spectrum [85].

3. Results and Discussion

The PRISMA and EnMAP reflectance data, which have already been validated in previous studies (e.g., [44,48]), also showed consistent results with both in situ and S2 and S3 data in this study. Examples of radiometric validation of satellite data are shown below (Figure 2 and Figure 3). In Figure 2, Rrs data gathered from fieldwork in Lake Peipsi were compared with the PRISMA L1 product corrected using the POLYMER model, showing a level of agreement with SA equal to 15.6°. PRISMA data, similar to the in situ measurements, captured greater variability around the 700 nm peak—related to the presence of Chl-a (average in situ value: 36.6 mgm−3)—while presenting a slight overestimation in the blue region of the spectrum. In the case of Lake Võrtsjärv, there was a higher level of agreement between the in situ data provided by the HYPSTAR instrument and EnMAP L2A reflectance data, with SA value equal to 6.4°. It is interesting to note that the sensor adequately captured the absorption feature in the blue region of the spectrum, due to the presence of CDOM (average in situ value: 2.6 m−1) in the lake. In the second example (Figure 3), the comparisons between PRISMA L1 products corrected using POLYMER and multispectral S3 and between EnMAP L2A and multispectral S2 data are reported: the results showed a level of agreement with MAPD of less than 25% and an average SA of 9.8°. In general, as can be seen from the reference measurements and confirmed by the satellite products, these lakes are characterized by high optical variability, mainly due to factors such as seasonality, precipitation, and wind speed.
In the following paragraph, different applications are detailed for each image in the dataset, including spectral signature characterization and the generation of advanced products. Starting from the true color composition, each satellite image was stretched to enhance the features relevant to the specific application. Figure 4 presents Application n. 2 (see Figure 1 and Table 1), applied to the PRISMA image acquired on 19 September 2020 over Lake Lämmijärv. A map was generated based on OWT, and the spectral signatures corresponding to four main distinct classes are shown: OWT 4a, 4b, 5a (the latter being associated with the majority of water pixels in the scene), and 6 [81]. This outcome is consistent with the results reported in [57], which stated that 65% of Lake Peipsi belongs to moderately turbid water and only 5% to brown water, which occurs in the narrowest part of the Lake Lämmijärv. The first three classes presented characteristics typical of green waters and differed from each other in terms of phytoplankton load and short-wave absorption due to CDOM. OWT 4b may be indicative of Coccolithophore blooms, which can significantly influence vertical mixing processes and have a substantial impact on the vertical transport of nutrients [86]. OWT 5a exhibited a spectral peak around 700 nm, suggesting a high concentration of Chl-a—this is typically associated with eutrophication, a phenomenon that requires close monitoring due to its detrimental effects on the lake ecosystem. OWT 6, on the other hand, presented the typical spectral signature of brown waters with a high debris load. Within the area identified by OWT 6, optically shallow waters are present [63], with resuspension caused by wind playing a key role in the determination of optical characteristics.
Figure 5 shows a comparison between two PRISMA images acquired on 24 June 2020 and 9 May 2021, showing the area where the Velikaya River enters Lake Pihkva (Application n.3). The ROIs extracted from the images are located at the same position. Ancillary datasets included ERA5-derived precipitation and wind data. Notably, cumulative rainfall during the week prior to the first acquisition was 8.8 mm, increasing significantly to 62.1 mm in the week preceding the second acquisition. As for wind conditions, on the first date the average speed was 2.3 ms−1 and the maximum speed recorded was 3.9 ms−1, while on the second date the average speed was 6.1 ms−1 with a maximum of 8.8 ms−1. The combined influence of elevated precipitation and strong wind conditions contributed to an increased sediment load transported by the river into the lake [87].
Figure 6 shows Application n.4, in which the effect of the inflow from the Emajõgi River—which connects Lake Võrtsjärv to Lake Peipsi—was evaluated, as captured in the EnMAP image acquired on 16 May 2024. To this end, a 5 km transect was established for the extraction of spectral signatures, extending from within the river into the inner lake, with sampling points spaced at 1 km intervals. As the measurement points progress from the river toward the lake, where the influence of the inflow gradually diminishes, corresponding changes in the spectral signatures were observed. Initially, the spectra exhibited markedly higher concentrations of CDOM and TSM, which decreased in magnitude along the transect and reached their lowest levels at the final ROI (distance from the shore ≈ 4 km), where the river’s influence was minimally detectable. Notably, the spectral signature observed within the ROI located inside the river is similar to the characteristic spectral signature of Lake Võrtsjärv, as illustrated in the example provided in Figure 2. This similarity is attributable to the hydrological connection, wherein water entering Lake Peipsi originates from Lake Võrtsjärv via the river [88].
This result suggests that the combined spatial resolution and spectral properties of satellite sensors such as EnMAP can support analyses regarding the influence of river discharge into lakes. Therefore, understanding of the hydrological processes in lakes can progress, reaching the maturity of what has been achieved for many years by monitoring river plumes entering sea waters [89,90,91].
Figure 7 shows Application n.5, in which the concentration of PC was estimated (using the MDN model) for a PRISMA image acquired on 16 August 2022 over Lake Peipsi. The average water temperature for the previous month has increased by 3 °C over the last three years, which may be associated with the elevated concentrations of PC [92], the presence of which was identifiable from the PRISMA spectral signatures (absorption peak around 620 nm and reflectance peak around 650 nm), while remaining undetectable when using the S2 sensor, given its lower spectral resolution. Indeed, historical data demonstrate a consistent link between rising water temperatures and the increased frequency of cyanobacterial blooms, particularly during the summer months. Moreover, models predict that global warming will lead to earlier and longer blooms, along with shifts in the seasonal dynamics of rivers and lakes [92,93,94,95]. Laboratory analysis confirmed that, at that time, Aphanizomenon flos-aquae Ralfs was dominating in the lake.
Figure 8 shows Application n.7, in which the concentration of PC was estimated (using the MDN model) for a PRISMA image acquired on 4 April 2020 over Lake Võrtsjärv. In the westernmost region of the lake, elevated concentrations of PC were observed. These concentrations are also discernible in the spectral signatures obtained from PRISMA imagery; however, due to the lower spatial resolution, this feature was not detectable in S3 data. Laboratory analysis confirmed that, at that time, Limnothrix planctonica Meffert and L. redekei Meffert were dominant in the lake.
It is worth noting that the comparable spectral configurations of the PRISMA and EnMAP missions can facilitate the adaptation of the MDN model—originally calibrated and validated for PRISMA—to EnMAP data, thereby laying the foundation for its application to other current and future hyperspectral satellite platforms [47].
In Figure 9 regarding Application n.8, the capability of the EnMAP image to distinguish between emergent plants, common reeds, floating plants, submerged plants, and water pixels is demonstrated. This analysis was conducted near the station “Õhne” (58.202 N, 26.018 E) identified in [96]. Based on [97], it is possible to assume that the aquatic vegetation species identified in the image could be Phragmites australis (emergent), Nuphar lutea (floating), and Myriophyllum spicatum (the dominating species among the submerged plants, for at least 20 years [98]).
As mentioned in Section 2.1, Lake Võrtsjärv is covered by ice for a period of around 131 days from November to April [64]. Furthermore, from January to March, snow coverage reduces incident light by more than 40%, and the attenuation of light by ice varies from 4% in December to around 26% in March [99]; in addition, air temperatures range around 0.6 °C [63]. Application n.9 (Figure 10) shows the map of the integral of reflectance from 450 to 1000 nm (R450–1000) generated from a PRISMA image acquired on 18 March 2022. The spectral signatures extracted from the image likely correspond to lake cover in terms of snow and ice, which potentially represent other physical properties such as snow impurities and thickness [100,101].

4. Conclusions

Imaging spectroscopy is emerging as a functional technique for assessing the status of inland waters. Although this technique can provide useful information for studying the optical characteristics of aquatic ecosystems, it requires considerable research and development in terms of the used methodology and its application, with several challenges such as atmospheric correction still to be addressed (see, e.g., [44]). In this study, a preliminary analysis of PRISMA and EnMAP hyperspectral data was conducted on two Estonian lakes for which the water quality is greatly influenced by several inflows and meteorological variables, such as precipitation, wind speed, and lake water temperature. These lakes also exhibit complex optical properties and, to date, have been studied using mainly multispectral data [58,70], which might be suboptimal in terms of distinguishing spectral features at specific narrow bands over a variety of surface properties and OWTs. A satellite dataset consisting of 12 images (nine gathered from PRISMA and three from EnMAP) was collected, enabling a total of nine different applications to be developed. A subset of the images was used to evaluate the quality of Rrs data, given the availability of coincident in situ and multispectral measurements as a reference. For the PRISMA images of Lake Peipsi, atmospheric correction was performed with POLYMER while, for Lake Võrtsjärv and in the case of the EnMAP images, standard Rrs products were used. The spectral comparison demonstrated the ability of hyperspectral sensors to capture the spectral signatures of the reference measurements in terms of their magnitude and shape (SA below 16°), with larger differences at the shortest wavelengths as observed in previous studies [44,46]. In particular, the spectral signatures provided a qualitative estimate of the main optical characteristics of the lakes, as influenced by river inputs or environmental factors, such as increasing temperature, in agreement with the existing literature [92]. The high spectral and spatial resolutions enabled the characterization of OWTs, the detection of PC and macrophytes, and the riverine influences on water Rrs. In Applications n.5 and n.7, the PC concentrations retrieved from PRISMA were consistent with our knowledge. The spectral and spatial resolutions of the PRISMA and EnMAP imagery were also exploited for the detection of aquatic vegetation association groups.
This study demonstrated that PRISMA and EnMAP hyperspectral data, complemented by both in situ measurements and Sentinel imagery, can support the comprehensive evaluation of optically complex water ecosystems such as the two largest Estonian lakes. The limited availability of in situ measurements and the frequent presence of cloud cover prevented the development of more quantitative applications and the investigation of critical events (e.g., surface runoff). These limitations underscore the importance of a high-frequency revisit time for the development of aquatic ecosystem-related applications, as well as higher number of in situ measurements for validation.
The findings of this study offer valuable information to guide the development of future hyperspectral missions, providing useful insights for the selection of observation windows to capture key seasonal dynamics in lake ecosystems. This information, together with the expansion of automated hyperspectral sensor networks (e.g., HYPERNETS) in inland water bodies, has the potential to significantly improve the ability of future missions—such as ESA’s Copernicus Hyperspectral Imaging Mission for the Environment (CHIME [102])—to monitor inland water quality on a large scale.

Author Contributions

Conceptualization, A.F., M.B., A.P., C.G. and K.A.; methodology, A.F., M.B., A.P. and C.G.; software, A.F., A.P., A.J.G. and F.S.; validation, A.F., A.P. and L.P.; formal analysis, A.F., M.B., A.P., L.P. and C.G.; investigation, A.F., K.K. and K.A.; data curation, A.F., A.P., L.P., A.J.G., F.S. and J.K.; writing—original draft preparation, A.F. and A.P.; writing—review and editing, M.B., K.K., A.J.G., L.P., J.K., C.G. and K.A.; supervision, M.B., C.G. and K.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by funding from the agreement ASI-CNR, n. 20195HH0 “Attività scientifica di CAL/VAL della missione PRISMA, PRISCAV” and Estonian Research Council grant PRG2646 “Methods, Traceability and Validation of the In-Water Ocean Color Measurements”. The collection of HYPSTAR data was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 775983 (HYPERNETS project) and by the European Space Agency under the HYPERNET-POP contract.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We are very grateful to A. Q. Scotti and N. Ghirardi for the valuable discussions that supported the analysis developed in this study. Our appreciation extends to E. Lopinto and P. Sacco from ASI, N. Pinnel from DLR, and M. Soppa from AWI for the relevant discussions on the PRISMA and EnMAP missions. We would like to express our gratitude to the anonymous reviewers for their detailed comments that significantly improved the manuscript.

Conflicts of Interest

Author François Steinmetz was employed by the company HYGEOS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Study areas: Lake Võrtsjärv and Lake Peipsi. Blue dots represent the validation points; blue boxes represent the areas targeted for various applications with corresponding IDs.
Figure 1. Study areas: Lake Võrtsjärv and Lake Peipsi. Blue dots represent the validation points; blue boxes represent the areas targeted for various applications with corresponding IDs.
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Figure 2. From left to right: spectral comparisons between PRISMA data corrected with POLYMER and in situ measurements in Lake Peipsi (six ROI selected during the fieldwork on 18 July 2021); spectral comparison between EnMAP L2A data and in situ measurements (HYPSTAR data acquired on 20 June 2024 and 21 July 2024) in Lake Võrtsjärv. Blue and green curves represent in situ data and hyperspectral spaceborne data (mean + st.dev.), respectively. These results refer to Applications n.1 and n.6.
Figure 2. From left to right: spectral comparisons between PRISMA data corrected with POLYMER and in situ measurements in Lake Peipsi (six ROI selected during the fieldwork on 18 July 2021); spectral comparison between EnMAP L2A data and in situ measurements (HYPSTAR data acquired on 20 June 2024 and 21 July 2024) in Lake Võrtsjärv. Blue and green curves represent in situ data and hyperspectral spaceborne data (mean + st.dev.), respectively. These results refer to Applications n.1 and n.6.
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Figure 3. From left to right: spectral comparison between PRISMA data corrected with POLYMER and S3 data in Lake Peipsi (19 September 2020, 8 June 2021, 21 May 2022); spectral comparison between EnMAP L2A (21 July 2024) data and S2 (22 July 2024) data in Lake Võrtsjärv. Blue and green curves represent multispectral spaceborne data and hyperspectral spaceborne data (mean + st.dev.), respectively. These results refer to Applications n.1 and n.6.
Figure 3. From left to right: spectral comparison between PRISMA data corrected with POLYMER and S3 data in Lake Peipsi (19 September 2020, 8 June 2021, 21 May 2022); spectral comparison between EnMAP L2A (21 July 2024) data and S2 (22 July 2024) data in Lake Võrtsjärv. Blue and green curves represent multispectral spaceborne data and hyperspectral spaceborne data (mean + st.dev.), respectively. These results refer to Applications n.1 and n.6.
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Figure 4. (a) PRISMA image acquired on 19 September 2020 over Lake Lämmijärv, (b) OWT classification map, (c) spectral signatures extracted from the four ROIs displayed in (a) and following the same colors. These results refer to Application n.2.
Figure 4. (a) PRISMA image acquired on 19 September 2020 over Lake Lämmijärv, (b) OWT classification map, (c) spectral signatures extracted from the four ROIs displayed in (a) and following the same colors. These results refer to Application n.2.
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Figure 5. From left to right: PRISMA images acquired on (a) 24 June 2020 and (b) 9 May 2021 over Lake Pihkva, (c) spectral signatures extracted from the two ROIs located at the same position on different dates. These results refer to Application n.3.
Figure 5. From left to right: PRISMA images acquired on (a) 24 June 2020 and (b) 9 May 2021 over Lake Pihkva, (c) spectral signatures extracted from the two ROIs located at the same position on different dates. These results refer to Application n.3.
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Figure 6. (a) EnMAP image acquired on 16 May 2024 at the confluence where the Emajõgi River flows into Lake Peipsi, (b) spectral signatures extracted along a transect crossing the study area. Each curve (corresponding to the points marked in the image using the same color scheme) represents the Rrs values recorded at successive points along the transect. This result refers to Application n.4.
Figure 6. (a) EnMAP image acquired on 16 May 2024 at the confluence where the Emajõgi River flows into Lake Peipsi, (b) spectral signatures extracted along a transect crossing the study area. Each curve (corresponding to the points marked in the image using the same color scheme) represents the Rrs values recorded at successive points along the transect. This result refers to Application n.4.
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Figure 7. (a) PRISMA image acquired on 16 August 2022 over Lake Peipsi, (b) PC concentration map, (c) spectral comparison between PRISMA L1 corrected with POLYMER and S2 (17 August 2022), displayed in green and blue, respectively. These results refer to Application n.5.
Figure 7. (a) PRISMA image acquired on 16 August 2022 over Lake Peipsi, (b) PC concentration map, (c) spectral comparison between PRISMA L1 corrected with POLYMER and S2 (17 August 2022), displayed in green and blue, respectively. These results refer to Application n.5.
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Figure 8. (a) PRISMA image acquired on 4 April 2020 over Lake Võrtsjärv, (b) PC concentration map, (c) spectral comparison between PRISMA L2C and S3 (4 April 2020), displayed in green and blue, respectively. These results refer to Application n.7.
Figure 8. (a) PRISMA image acquired on 4 April 2020 over Lake Võrtsjärv, (b) PC concentration map, (c) spectral comparison between PRISMA L2C and S3 (4 April 2020), displayed in green and blue, respectively. These results refer to Application n.7.
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Figure 9. (a) EnMAP image acquired on 21 July 2024 over Lake Võrtsjärv, (b) spectral signatures extracted from the five ROIs displayed in (a), following the same color scheme. This result refers to Application n.8.
Figure 9. (a) EnMAP image acquired on 21 July 2024 over Lake Võrtsjärv, (b) spectral signatures extracted from the five ROIs displayed in (a), following the same color scheme. This result refers to Application n.8.
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Figure 10. (a) PRISMA image acquired on 18 March 2022 over Lake Võrtsjärv, (b) map of the integral of reflectance, (c) spectral signatures extracted from the three ROIs displayed in (a), following the same colors. These results refer to Application n.9.
Figure 10. (a) PRISMA image acquired on 18 March 2022 over Lake Võrtsjärv, (b) map of the integral of reflectance, (c) spectral signatures extracted from the three ROIs displayed in (a), following the same colors. These results refer to Application n.9.
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Table 2. Atmospherically corrected products used in this study.
Table 2. Atmospherically corrected products used in this study.
Study AreaPRISMAEnMAP
Lake PeipsiL1 + POLYMERL2A standard product
Lake VõrtsjärvL2C standard productL2A standard product
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Fabbretto, A.; Bresciani, M.; Pellegrino, A.; Kangro, K.; Greife, A.J.; Panizza, L.; Steinmetz, F.; Kuusk, J.; Giardino, C.; Alikas, K. Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy. Appl. Sci. 2025, 15, 8357. https://doi.org/10.3390/app15158357

AMA Style

Fabbretto A, Bresciani M, Pellegrino A, Kangro K, Greife AJ, Panizza L, Steinmetz F, Kuusk J, Giardino C, Alikas K. Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy. Applied Sciences. 2025; 15(15):8357. https://doi.org/10.3390/app15158357

Chicago/Turabian Style

Fabbretto, Alice, Mariano Bresciani, Andrea Pellegrino, Kersti Kangro, Anna Joelle Greife, Lodovica Panizza, François Steinmetz, Joel Kuusk, Claudia Giardino, and Krista Alikas. 2025. "Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy" Applied Sciences 15, no. 15: 8357. https://doi.org/10.3390/app15158357

APA Style

Fabbretto, A., Bresciani, M., Pellegrino, A., Kangro, K., Greife, A. J., Panizza, L., Steinmetz, F., Kuusk, J., Giardino, C., & Alikas, K. (2025). Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy. Applied Sciences, 15(15), 8357. https://doi.org/10.3390/app15158357

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