# Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy
## 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]. 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 accurateidentification 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 PRecurscore 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 Vortsjarv). 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.