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\chapter{Introduction
\label{chap1}}{Jeremy Werdell, Astrid Bracher, Ana Dogliotti
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\section{Background}
Understanding how the aquatic system (open ocean, coastal and inland waters) responds to global-scale climate change and regional-scale localized environmental forcing is essential for studying global environmental systems. The aquatic system plays a major role in the Earth's carbon cycle, which links land, the ocean, and the atmosphere. It also supports marine and inland water ecosystems that are sensitive to natural hazards and environmental stressors. Coastal and inland aquatic ecosystems, including wetlands, for example, are of fundamental interest to society and the economy given their tight link to urbanization and economic value creation. These ecosystems, which are continuously impacted by natural processes and human activities, also play a significant role in the carbon cycle and comprise critical habitats for biodiversity. Systematic, high-quality and global observations, such as those provided by satellite remote sensing techniques, are key to understanding the intertwined and complex aquatic system. Remote sensing of aquatic ecosystems provides an essential tool for assembling such datasets. Satellites equipped with optical sensors to sense the ocean surface gather information over broad spatial areas and long time periods, something that traditional field methods onboard ships or aircrafts cannot equivalently achieve. As such, the geophysical records derived from these satellite sensor measurements, called \textit{ocean colour}, are critical for monitoring water quality and carbon storage; understanding shifts in phytoplankton communities; assessing primary productivity; and tracking long-term environmental trends in key regions of the aquatic system, including sinks for atmospheric carbon, drinking water reservoirs, and fishing grounds. In the context of climate change and human impacts, satellite-based aquatic monitoring therefore remains an essential tool for both basic research and applied science and policy.

The continuous global data record from polar orbiting optical satellites in low Earth orbit now spans nearly thirty years and has focused on sensing the global oceans. Heritage ocean colour satellite instruments measure the spectral radiance emanating from the top of the atmosphere at several visible, near-infrared, and sometimes, short-wave infrared bands. Atmospheric correction algorithms are typically applied to remove the contribution of the atmosphere from the top-of-atmosphere (TOA, all abbreviations are listed in the section Acronyms) radiances which can exceed 90\% of the total signal, and produce estimates of spectral water-leaving radiance, which is the light exiting the water that describes the \textit{colour} of the water body. Bio-optical algorithms are then applied to the water-leaving signal to produce estimates of geophysical and optical quantities, such as the near-surface concentration of the phytoplankton pigment chlorophyll-a (CHL), and spectral inherent optical properties, among many others. In turn, these quantities can be used to infer higher order geophysical factors (for example, metrics of phytoplankton community composition) that contain critical ecological information of interest as described above.

Most heritage satellite ocean colour instruments collect(ed) data within a limited number of broad wavelength bands in the visible (approximate band resolution of 10–20 nm) and near-infrared (NIR) to shortwave infrared (SWIR; approximate band resolution of 20–40 nm) regions of the spectrum. The visible bands are used to determine key oceanic properties, while the NIR and SWIR bands facilitate atmospheric correction. Generally, the number of visible spectral bands increased progressively from the proof-of-concept National Aeronautics and Space Administration (NASA) Coastal Zone Color Sensor (CZCS; 1978 to 1986), to the most recent missions, e.g., NASA Moderate Resolution Imaging Spectroradiometer (MODIS; 1999, 2002) and the European Space Agency (ESA) Sentinel-3 (S3) Ocean and Land Colour Imager (OLCI; 2016, 2018) sensors with 7.5 to 10 nm band resolution and 7 and 10 bands, respectively, for aquatic remote sensing applications. S3-OLCI, with two missions flying in tandem, has also improved global full resolution datasets at 300 m (daily global coverage) which significantly advanced the monitoring of coastal and inland waters. Over the same period, however, the range and sophistication of scientific applications using ocean colour data expanded dramatically. Following the launch of the NASA Sea-viewing Wide Field-of-view Sensor (SeaWiFS; 1997), the community began to recognize a growing gap between scientific demands and the capabilities of existing instruments. In response, the aquatic radiometry community initiated activities to define the observational requirements for future satellite missions that could meet emerging scientific and environmental monitoring needs. These evolving goals ultimately led to the development of missions such as the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory, Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Sentinel-3 Next Generation Optical (S3NGO) Advanced OLCI (AOLCI), all of which are designed to provide advanced spectral and radiometric capabilities for studying the Earth’s ocean and inland waters.

Additionally, several missions not targeted originally for ocean colour applications have been exploited for aquatic ecosystem monitoring. Atmospheric optical sensors, such as the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, operation 2002-2012) and the TROPOspheric Monitoring Instrument (TROPOMI, Sentinel-5 Precursor, S5P, since 2018) have been used to derive innovative products for the global oceans. These sensors have high spectral resolution and high sensitivity over dark waters, but with large ground-pixel sizes (see Chapter \ref{chap2}). Conversely, imaging spectroscopy satellite sensors, often designed primarily for terrestrial applications, provide high spatial resolution data (<100 m), which offers the opportunity, despite their low temporal coverage, to explore applications for coastal and inland waters, e.g., Deutsches Zentrum für Luft- und Raumfahr (DLR, German Aerospace Center) Earth Sensing Imaging Spectrometer (DESIS, since 2018), Agenzia Spaziale Italiana (ASI, Italian Space Agency) PRecursore IperSpettrale della Missione Applicativa (PRISMA; since 2019) or the Copernicus Expansion Mission, CHIME (launch expected 2028). For details on all hyperspectral sensors see Chapter \ref{chap2}.

The primary advancement realized by the latter missions remains the move from multispectral to hyperspectral radiometry, which we define here as the continuous measurement of light in incremental steps (<10 nm) across the ultraviolet-to-near-infrared spectrum. Heritage multispectral instruments were designed to measure the bulk optical and geophysical properties that modulated spectral water-leaving radiances (e.g., CHL and diffuse downwelling attenuation coefficients). Traditional algorithms often estimated these properties by relating blue-to-green radiance ratios to specific water properties. Spectral matching approaches applied to multispectral radiometry retrieved more detailed optical parameters, such as spectral phytoplankton absorption, metrics of coloured dissolved organic matter (CDOM), and particle backscattering. These more advanced techniques require higher spectral resolution than band ratio algorithms, but are still limited by the number of wavelengths available from heritage instrumentation. As a result, most modern and emerging geophysical retrievals benefit from hyperspectral radiometry with resolutions finer than 5-nm. This increased visible spectral sampling improves detection of pigment absorption and particulate backscattering, leading to more accurate estimates of phytoplankton diversity, net primary production (NPP), and carbon stocks. Additionally precise measurements across the CHL fluorescence region provide key information on phytoplankton nutrient stress and pigment composition in complex waters.

\section{Scope of the report}
This scientific roadmap (version 1) serves to document the state-of-the-art in hyperspectral remote sensing of the aquatic environment. It aims to:
\begin{itemize}
    \item define the benefits of hyperspectral remote sensing techniques relative to retrievals from heritage multispectral sensors, with a focus on improved aquatic geophysical retrieval capabilities;
    \item identify new and emerging basic research and applied science questions to be addressed through future improved capabilities from aquatic hyperspectral retrievals; and,
    \item highlight gaps and challenges that remain to be addressed.
\end{itemize}

The primary audiences are:
\begin{itemize}
    \item agencies and institutions that can facilitate addressing gaps and challenges and improving retrieval capabilities (e.g., via funding or other support); and,
    \item the scientific research and applied communities that can progress towards remediating gaps and challenges and developing new and advanced retrieval approaches.
\end{itemize}

In other words, this document serves as a baseline so that the aquatic radiometry community can prepare more efficiently to take action on pursuing and remediating the identified gaps. 

The guiding questions to be addressed within this report are:
\begin{itemize}
    \item What are the pressing science questions globally and regionally, for which hyperspectral remote sensing may allow progress for aquatic applications where multispectral remote sensing has reached its limit?
    \item What are the constraints on hyperspectral remote sensing in addressing these questions? (This includes limitations in terms of spatial and temporal resolution, signal-to-noise, calibration/validation (Cal/Val) of hyperspectral data and so on).
\end{itemize}
More specifically, these chapters aim to answer the following science questions (SQ):
\begin{enumerate}
    \item[SQ1.] Which current products are derived from hyperspectral data and what are the associated challenges?
    \item[SQ2.] What additional science and applications can hyperspectral enable, not being addressed by multispectral remote sensing?
    \item[SQ3.] What is the current status and limitations of aircraft and satellite hyperspectral remote sensing?
    \item[SQ4.] What is the current status of in situ data and radiative transfer modelling for Cal/Val and algorithm development? What are the current gaps?
    \item[SQ5.] What advances in atmospheric corrections have we made using hyperspectral data? How can we further improve the corrections?
    \item[SQ6.] How will radiometric and product uncertainty quantification be addressed [for harmonization of international missions]?
    \item[SQ7.] What new methodologies can improve hyperspectral Level-1 and Level-2 products and enhance their application?
\end{enumerate}
