\chapter{Overview of Air- and Space-borne Sensors
\label{chap2}}{Claudia Giardino, Astrid Bracher, Malik Chami}

Imaging spectrometry of aquatic ecosystems has been in development since the late 1980s when the first airborne hyperspectral sensors were deployed mostly over lakes. As the spaceborne and airborne hyperspectral sensors become increasingly available and appropriate for aquatic ecosystem detection, monitoring, and assessment, new studies are possible. Satellite instruments, with very high spectral resolution (1 nm and better), originally designed for atmospheric applications, have shown to provide additional opportunities for applications of retrieving information on optical properties of the open ocean. However, science-based applications will need to be further developed to an operational level. 

In such a frame we aim to provide a summary of the current status and limitations of airborne and satellite hyperspectral remote sensing for water applications. For the purposes of this report, we consider the term \textit{imaging spectroscopy} as acquisition of images in hundreds of contiguous, registered, spectral bands, such that for each pixel a radiance spectrum can be derived.

\section{Status}
\subsection{Airborne imaging spectroscopy}
Since the early 1980s, airborne observations have been used to demonstrate the capabilities that hyperspectral remote sensing brings to understanding environment, atmosphere components, and terrestrial and aquatic ecosystems, as summarized in Table \ref{Table-2.1}. In this context, high-resolution airborne hyperspectral sensors (Table \ref{Table-2.1}) have been providing enhanced mapping tools (e.g., \cite{Hunter-Gilvear-Tyler-others-2010,Dekker-Phinn-Anstee-others-2011}). The Airborne Prism EXperiment (APEX) has also been used to define and validate algorithms for mapping primary producers in eutrophic lake waters (\cite{Bolpagni-Bresciani-Laini-others-2014}), or to assess atmospheric correction procedures over small eutrophic lakes using the Flexible Experimental Imaging Spectrometer (FENIX) sensor (\cite{Markelin-Simis-Hunter-Spyrakos-Tyler-Clewley-Groom-2016}). 

\begin{table}[htb!]
\centering
\medskip \footnotesize
\caption{Main features of some typical airborne hyperspectral imaging systems, including spectral range, spectral sampling interval (SSI), number of channels, field of view (FOV) and instantaneous field of view (IFOV) (adapted from \cite{Taramelli-Tornato-Magliozzi-Mariani-Valentini-Zavagli-others-Rast-2020}). See Acronyms for full sensor names.}
\label{Table-2.1}
\begin{tabular}{|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.20\linewidth}|}
\hline
\textbf{Mission/ Sensor (Country)} & \textbf{Spectral range (nm)} & \textbf{SSI (nm)} & \textbf{Number of bands} & \textbf{FOV (deg)} & \textbf{IFOV (mrad)} & \textbf{Features} \\
\hline
AISA EAGLE

(Finland) & 400-970 & 1.25 & 488 & 37.7 & 1 & Coastal monitoring, classification of vegetation \\
\hline
APEX

(Europe) & 380–970

940–2500 & 0.5–8.0

10.0–6.0 & 114

199 & 28 & 0.48 & Pigments and CHL fluorescence in agriculture \\
\hline
AVIRIS-NG (USA) & 380-2500 & 9.7-12 & 224 & 30 & 7.5 & Ecology, agricultural and forest status, oceanography, geology, atmosphere, ice, snow, clouds \\
\hline
CASI

(Canada) & 400–2500  & 2.4/7.5 & 96/200 & 40 & 0.49/ 0.698& Oil/gas/mineral resource exploration and other fields such as agriculture, forestry, and coastal issues \\
\hline
AISA FENIX (Finland) & 380-2500 & 3.37 & 620 & 32.3 & 17.453 & Similar to AVIRIS \\
\hline
HyMap

(Australia) & 400–2500 & 10–20 & 128 & 61.3 & 2×2.5 & Mineral exploration and environmental, agricultural and forest monitoring \\
\hline
Hyplant-DUAL

(Germany-Finland) & 380–970

970–2500

670–780 & 4.0

13.3

0.25 & 350

272

1024 & 32.3 & 0.0832 & Agricultural and forest status, vegetation fluorescence \\
\hline
MIVIS (Italy) & 433–833

1150–1550

2000–2500

8200–12700 & 20

50

8

400–500 & 20

8

64

10 & 70 & 2 & Geology and environmental study \\
\hline
\end{tabular}
\end{table}

\subsection{Spaceborne imaging spectroscopy}

 Several reviews (e.g., \cite{Giardino-Brando-Gege-Pinnel-Hochberg-Knaeps-others-Dekker-2019}) have been recently published on hyperspectral remote sensing for aquatic ecosystems. Particularly, \cite{Dierssen-Ackleson-Joyce-Hestir-Castagna-Lavender-McManus-2021} provided an analysis on science, resources, and an outlook of the state-of-the-art in the field. A figure from that review, originally presented in \cite{HESTIR2015181}, nicely illustrated the spectral, temporal and spatial characteristics of the majority of the present and future spaceborne hyperspectral sensors commonly used for freshwater ecosystem measurements. We extended the review of \cite{Dierssen-Ackleson-Joyce-Hestir-Castagna-Lavender-McManus-2021} by adding a few recently launched and planned spaceborne imaging spectroscopy missions (Fig. \ref{Fig2.1}). 

\begin{figure}[htb!]
\centering
\includegraphics*[width=0.9\textwidth]{Figs/Fig2.1}
\caption{The spectral (x-axis, in nm), temporal (y-axis, in days), and spatial (size of the bubble, in km) characteristics of satellite sensors commonly used for freshwater ecosystem measurements. Note: sensors that provide different spatial resolutions are plotted separately, and sensors with overlapping resolution characteristics are slightly skewed for graphing purposes. Multispectral missions are in grey, and hyperspectral missions are coloured. All hyperspectral missions here considered have at least 60 bands within the visible-NIR spectral range. Atmospheric sensors are indicated in light yellow at ~1000 spectral bands. Pixel size is indicated with dashed lines. SCIAMACHY is listed with 6-day coverage and 30 x 60 km spatial resolution, GOME-2 with 3-day and 40 x 40 km, and TROPOMI with daily coverage and 5.5 x 3.5 km. The FLuorescence EXplorer, FLEX, is listed for its potential use in water application (\cite{Gupana-Odermatt-Cesana-Giardino-Nedbal-Damm-2021}). These sensors are listed with the other hyperspectral sensors in Table \ref{Table-2.2}.}

\label{Fig2.1}
\end{figure} 
 
Details of most of the current and future hyperspectral missions are reported in Table \ref{Table-2.2}. Table \ref{Table-2.2} additionally includes the following atmospheric satellite sensors which have been offering high spectral resolution (\textasciitilde0.25–1 nm) enabling innovative ocean applications on water constituents (\cite{Bracher-Vountas-Dinter-Burrows-Rottgers-Peeken-2009}) and the underwater light field (e.g., \cite{Oelker-Losa-Richter-Bracher-2022}): SCIAMACHY (operation 2002-2012); Global Ozone Monitoring Experiment 2 (GOME-2, measuring since 2007); the Ozone Monitoring Instrument (OMI) sensor (measuring since 2004); S5P-TROPOMI (with data since 2018); and the ultraviolet, visible, near-infrared light spectrometer (UVN, launched 2025 on Sentinel-4 and Sentinel-5). For comparison we added an overview of multispectral ocean colour satellites (e.g., OLCI), which also provide high spectral resolution (\textasciitilde10 nm) at determined wavebands, and those with broad bands but a pixel size suitable for fine scale mapping of aquatic ecosystems (e.g., MultiSpectral Instrument (MSI)). The Fluorescence Imaging Spectrometer (FLORIS) on the FLuorescence EXplorer (FLEX) was also included. It does not cover the entire VIS-NIR, but, being in tandem with Sentinel-3, can complement OLCI observations.

\begin{table}
\centering
\medskip \footnotesize
\caption{Main features of some current and planned spaceborne hyperspectral imaging systems including current atmospheric sensors SCIAMACHY, OMI, TROPOMI, GOME-2, and UVN. BRDF is the Bidirectional Reflectance Distribution Function (adapted from \cite{Taramelli-Tornato-Magliozzi-Mariani-Valentini-Zavagli-others-Rast-2020}). For comparison, multispectral ocean and land sensors have been included.}
\label{Table-2.2}

\begin{tabular}
{|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>
{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|}
\hline
\textbf{Mission/ Sensor (Country)} & \textbf{Launch date} & \textbf{Spectral range (nm)} & \textbf{Spectral resolution (nm)} & \textbf{Number of bands} & \textbf{Spatial resolution (m)} & \textbf{Swath (km)} & \textbf{Features} & \textbf{Data portal} \\
\hline
EO1-Hyperion (USA) & 2000 & 450-2500 & 10 & 220 & 30 & 7.5 & Technology validation/demonstration mission & \href{https://data.nasa.gov/dataset/eo-1-hyperion}{data.nasa.gov} \\
\hline
CHRIS-PROBA (Europe) & 2001 & 400-1050 & 1.25 @400 nm; 11 @1050 nm & 62 Mode 1; 18 Mode 2-5 & 36 Mode 1; 18 Mode 2-5 & 14 & Technological demonstrator to collect BRDF data & \href{https://earth.esa.int/eogateway/catalog/proba-chris-level-1a}{earth.esa.int} \\
\hline
SCIAMACHY (DE-NL-BE) & 2002 & 240-2380 & 0.4 in UV-VIS & \textasciitilde1150 in UV-VIS & 30000 by 60000 & 960 & Atmospheric sensor also sensing open ocean & \href{https://earth.esa.int/eogateway/instruments/sciamachy}{earth.esa.int} \\
\hline
OMI (NL-USA) & 2004 & 270-500 & 0.5 in VIS & \textasciitilde460 & 13000 by 24000 & 2600 & Atmospheric sensor also sensing open ocean & \href{https://www.earthdata.nasa.gov/data/instruments/omi}{earthdata.nasa.gov} \\
\hline
GOME-2 (Europe) & 2006 & 240-790 & 0.26-0.51 & \textasciitilde1150 in UV-VIS & 40000 by 40000 & 1920 & Atmospheric sensor also sensing open ocean & \href{https://data.eumetsat.int/product/EO:EUM:DAT:METOP:GOMEL1}{data.eumetsat.int} \\
\hline
ISS-HICO (USA) & 2009 & 380-960 & 5.7 & 102 & 90 & 42-192 & Observing coastal ocean & \href{https://search.earthdata.nasa.gov/search?fi=HICO}{earthdata.nasa.gov} \\
\hline
S5P-TROPOMI (Europe) & 2017 & 270-495 675-775 2305-2385 & 0.55 0.55 0.25 & 410 182 320 & 3500 by 5500 & 2600 & Atmospheric sensor also sensing open ocean (and coastal?) & \href{https://mpc-vdaf.tropomi.eu/}{mpc-vdaf.tropomi.eu} \\
\hline
ISS-DESIS (Germany) & 2018 & 402-1000 & 10, 2.5 & 60, 235 & 30 & 30 & Applications relying to VIS-NIR and to use the BRDF & \href{https://www.dlr.de/en/eoc/research-transfer/projects-missions/desis/data-access}{dlr.de} \\
\hline
PRISMA (Italy) & 2019 & 400-2500, 400-700 & 10, 300 & 239, 1 & 30, 5 & 30 & Precursor of Application Mission for different applications & \href{http://prisma.asi.it/}{prisma.asi.it} \\
\hline
ISS-HIUSI (Japan) & 2019 & 440-2500 & 10-12.5 & 185 & 20 x 30 & 20 & Oil/gas/mineral resource exploration and other fields such as agriculture, forestry, and coastal issues & \href{https://www.eoportal.org/satellite-missions/iss-hisui\#iss-utilization-hisui-hyperspectral-imager-suite}{eoportal.org} \\
\hline
\end{tabular}
\end{table}

\begin{table}
\centering
\medskip \footnotesize
\begin{tabular}
{|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>
{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|}
\hline
Gaofen-5-AHSI (China) & 2021 & 400-2500 & 5-10 & 330 & 30 & 60 & Ecological environment monitoring and natural resource exploration & \href{https://space.skyrocket.de/doc_sdat/gf-5.htm}{space.skyrocket.de} \\
\hline
EnMAP (Germany) & 2022 & 420-2450 & 10 & 246 & 30 & 30 & Scientific path finder mission for later operational services, for environmental monitoring, process understanding & \href{https://planning.enmap.org/}{planning.enmap.org} \\
\hline
ISS-EMIT (USA) & 2022 & 380-2500 & ≤7.5 & 285 & 60 & 77 & Mineral Dust Source Measurements & \href{https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/}{earth.jpl.nasa.gov} \\
\hline
PACE-OCI (USA) & 2024 & 315-895; 940, 1038, 1249, 1378, 1618, 2130, 2258 & 1.25-2.5 UV-NIR & >200 & 1200 at nadir & 2663 & Global ocean biology, chemistry and ecology & \href{https://search.earthdata.nasa.gov/search?portal=obdaac&fi=OCI}{earthdata.nasa.gov} \\
\hline
S4-UVN (Europe) & 2025 & 305-500 (750-775) & 0.5 (0.12) & 391 (209) & 8000 by 8000 & - & Open ocean and coast 16.6$^\circ$W-8.8$^\circ$E, 30$^\circ$N to 65$^\circ$N Geostationary, every hour & \href{https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-4/Sentinel-4_spectrometer}{esa.int} \href{https://www.eumetsat.int/sentinel-4}{eumetsat.int} \\
\hline
S5-UVN (Europe) & 2025 & 300-500 685-710 743-773 1590-1675 2305-2385 & 0.5 0.25 0.25 & 400 101 121 & 7500 by 7500 & 2670 & Atmospheric sensor also sensing open ocean (and coastal?) & \href{https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5/Sentinel-5_s_imaging_spectrometer}{esa.int} \href{https://www.eumetsat.int/sentinel-5}{eumetsat.int} \\
\hline
FLEX-FLORIS (Europe) & 2026   & 500-780 & 0.3-3 &   & 300 & 150 & Quantitative global mapping of actual photosynthetic activity of terrestrial ecosystems, as a function of variable vegetation health status and environmental stress conditions & \href{https://earth.esa.int/eogateway/missions/flex}{earth.esa.int} \\
\hline
\end{tabular}
\end{table}

\begin{table}
\centering
\medskip \footnotesize
\begin{tabular}{|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>{\raggedright\arraybackslash}p{0.1\linewidth}|>
{\raggedright\arraybackslash}p{0.05\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|>{\raggedright\arraybackslash}p{0.15\linewidth}|}
\hline
CHIME (Europe) & 2028 & 400-2500 & ≤10 & >200 & 20-30 & 130 & World ecosystems, natural disasters & \href{https://sentinels.copernicus.eu/web/success-stories/-/chime-europe\%C2\%B4s-revolutionary-eye-in-the-sky-is-set-to-transform-environmental-monitoring}{sentinels.copernicus.eu} \\
\hline
GLIMR (USA) & 2029 & 340-1040 & 1-15 & 250 & 300 &   & Ocean biology, chemistry and ecology and coastal hazards from U.S. to Brazil & \href{https://science.nasa.gov/mission/glimr/}{science.nasa.gov} \\
\hline
SBG (USA) & 2030 & 400-2500, TIR & 8-12 & >210 & 30 & 185 & World ecosystems, natural disasters & \href{https://sbg.jpl.nasa.gov/}{sbg.jpl.nasa.gov} \\
\hline
S3NGO-AOLCI (Europe) & 2034 & 340-1040, TIR & 1.25-5 & 408 & 150 & 1400 & global ocean, coastal and  most inland waters biology, chemistry and underwater radiation & \\
\hline
Multispectral Ocean colour satellites (SeaWiFS, MODIS, MERIS, OLCI) & since 1980s & 400-900 (not continuous) & 10 & \textasciitilde20 & >300 & >1000 & Oceans, coastal and inland waters & See space agencies data portal \\
\hline
Land Multispectral missions (e.g. Landsat, S2, S2NG, Landsat-Next) & since 1984 & 400-2500 (not continuous) & 20-40 & \textasciitilde10 & 10-30 & >180 & Land & See space agencies data portals   \\
\hline
\end{tabular}
\end{table}

\begin{figure}
\centering
\includegraphics*[width=0.9\textwidth]{Figs/Fig2.2}
\caption{True-colour composite of the spaceborne hyperspectral images from PACE, EMIT, PRISMA and EnMAP, along with Sentinel-3 OLCI, all acquired on 21-05-2025 in Lake Malawi; bottom left: the \textit{R\textsubscript{rs}} spectra from Level-2 products covering a common portion of waters in the Northern part of the lake; bottom right: an \textit{R\textsubscript{rs}} spectrum from EnMAP over the coastal lagoon.} \label{Fig2.2}
\end{figure} 

Figure \ref{Fig2.2} shows an example of images collected from the last generation of spaceborne imaging spectroscopy missions. All images were collected on the same day (21 May 2025) over Lake Malawi, which is the ninth largest freshwater lake on Earth, located in the East African Rift. Pseudo true-colour composites were plotted alongside remote sensing reflectance (\textit{R\textsubscript{rs}}) data derived from Level-2 products over the area of water imaged by all sensors, in the northern part of the lake. The spectra derived from the five sensors were similar to those of deep blue lakes. EnMAP also provided fine-scale details of a coastal lagoon, which showed spectral signatures more typical of turbid, productive waters.

In addition to the missions described above, we note that the number of commercial hyperspectral satellite missions is continuously increasing in recent years. At the \textit{3rd Workshop on International Cooperation In Spaceborne Imaging Spectroscopy}, held at ESA in November 2024, it was noted that approximately 78\% of satellite imaging spectroscopy missions are for commercial initiatives, which reflects the rapid increase of the sector, particularly in the USA and China. These commercial missions are typically oriented to minisatellites with a ground sampling distance on the order of <10 m, and, usually, lower radiometric performance but increased ability to enable local to regional applications.

\section{Limitations}

\subsection{Airborne}

In addition to providing flexible fine scale and improved radiometry mapping and short-term changes (\cite{Zhang-Hu-Cannizzaro-Kowalewski-Janz-2018}), airborne imaging spectroscopy offers unique data for testing satellite-based systems (e.g., PRISMA, EnMAP, Surface Biology and Geology, SBG) despite limitations of spatial and temporal coverage. For example, \cite{Giardino-Candiani-Zilioli-2005} and \cite{Cogliati-Sarti-Chiarantini-Cosi-Lorusso-Lopinto-others-Colombo-2021} used airborne MIVIS and HyPlant images respectively, for simulating MERIS data on lakes and assessing PRISMA mission requirements during its commissioning phase.

Limitations of airborne imaging spectrometry include the larger cost of associated field work. Airborne campaigns can be expensive to organize as a result of the rental expenses of the plane. Another limitation is the technical challenge in processing airborne data, especially when accurate atmospheric correction is required for high-altitude flights, and when georeferencing becomes problematic without known ground control points over aquatic scenes (\cite{Zhang-Hu-English-Carlson-MullerKarger-ToroFarmer-Herwitz-2015}).

\subsection{Spaceborne }

Despite the recognized success of spaceborne hyperspectral satellite observations, they may still present some inaccuracies in monitoring environments that are highly variable in space and time, such as inland and near coastal waters (\cite{Ozesmi-Bauer-2002}). \cite{Olmanson-Brezonik-Bauer-2015} observed that spatial resolution was one of the primary limiting factors in the application of satellite remote sensing to freshwater ecosystems. The signal-to-noise ratio (SNR) of the sensor is critical in making accurate measurements of water quality parameters (\cite{Gege-Dekker-2020}). Since water is inherently darker than the land surface, ocean colour sensors require much higher SNRs than those for measuring the land surface (\cite{Hu-Feng-Lee-Davis-Mannino-McClain-Franz-2012}). Current hyperspectral, high spatial (100m) sensors provide SNR values typically lower than 400 which is convenient for land surfaces but does not meet the minimal requirements of measuring the relatively dark ocean (\cite{Qi-Lee-Hu-Wang-2017}). An SNR value higher than 500 (ideally 800, \cite{MullerKarger-Hestir-Ade-2018}) is required to observe dark absorbing waters that are often encountered, for example, in a significant number of productive lakes at latitudes >40$^\circ$N/S. Thus, high spatial and spectral resolution data are essential attributes to provide accurate retrieval of water quality in both optically deep and shallow waters. They are also essential in feasibility studies and proposals for an aquatic ecosystem Earth observing sensor (\cite{CEOS-2018}).

Another limitation of many spaceborne hyperspectral sensors is their narrow swath (<200 km) that is not necessarily appropriate for observing aquatic ecosystems at a global scale. These require a revisit time that is sufficient to characterize highly dynamic fine-scale processes such as river discharges, flooding events, and phytoplankton blooms (<2 days). Only the PACE hyperspectral mission is currently able to provide a global coverage of the Earth every two days, and this coverage is at the expense of reduced spatial resolution (1 km) that is usually not sufficient for inland and coastal water applications.

Since no single sensor can meet all requirements of high spatial, temporal, and spectral resolution with sufficient SNRs for aquatic applications, final recommendations for future directions in imaging spectrometry should also exploit using synergistically different sensor types (hyper- and multispectral, high spatial and high temporal resolution, VIS-NIR and other spectral domains), as e.g., described in \cite{Losa-Soppa-Dinter-others-2017} or \cite{Guanter-Brell-Chan-Giardino-GomezDans-Mielke-others-Yokoya-2019}. Satellite instruments, with a very high spectral resolution (1 nm and better, originally designed for atmospheric applications), so far do not provide operational water-leaving radiance products. This has limited their exploitation for aquatic remote sensing applications. Algorithms designed for these sensors so far focus on retrieving information from the top of the atmosphere to define strong spectral features in the ocean (either caused by inelastic scattering effects or phytoplankton pigments) while simultaneously accounting for the strong absorption by trace gases and broad band absorption and scattering effects from the ocean and atmosphere. For SCIAMACHY and GOME-2 the major constraint is their large footprint (around 0.5$^\circ$ latitude / longitude), which limits assessing their retrieval accuracy with in situ point measurements, and also limits application of their products to the open ocean. These limitations have been improved by OMI (18 by 24 km footprint) and more so by TROPOMI for which the global coverage is reached nearly within one day on a much smaller footprint (\textasciitilde5.5 km). On the other hand, OMI and TROPOMI only deliver data from UV to blue (500 nm), while SCIAMACHY and GOME-2 cover the whole UV-VIS-NIR range. Since different atmospheric sensors have different configurations, producing consistent time series for specific ocean colour products is challenging (\cite{Oelker-Richter-Dinter-Rozanov-Burrows-Bracher-2019}). In 2025, ESA and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) launched two successor missions to TROPOMI, UVN on Sentinel-4 and Sentinel-5, with a pixel size of 3.5 km x 7 km.


\section{Perspectives}

In order to improve future sensor capabilities for hyperspectral remote sensing applications, we envisaged the following perspectives: 

\begin{enumerate}
    \item Combining data from multiple satellite sensors (hyperspectral and multispectral) and Earth observation (EO) missions (land, ocean, and atmospheric satellite platforms) can overcome limitations of individual sensors and platforms through synergistic integration.
    \item As the requirements for observing ecological and hydrological processes in inland and coastal waters are not fully met, priority should, for example, be given to developing imaging spectroscopy satellite sensors with a spatial resolution of around 20–30 m and enhanced technical specifications, such as higher SNRs.
    \item It is crucial to transition current hyperspectral satellite research missions into operational platforms to ensure the routine and reliable monitoring of water quality.
    \item Airborne hyperspectral remote sensing has provided valuable support in the field for the development and operation of satellite missions and applications, including fine-scale mapping and hazard identification. This support should be maintained and augmented as the role of drones increases.
    \item Public and commercial operators are expected to provide high-quality airborne/drone hyperspectral data with appropriate geocoding and accurate atmospheric correction methods for water.
\end{enumerate}

