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\begin{document}

\title{Exploring mission design for imaging spectroscopy retrievals for land
and aquatic ecosystems}



\author[1]{Ann Raiho}%
\author[2]{Kerry Cawse-Nicholson}%
\author[3]{Adam Chlus}%
\author[4]{Jeff Dozier}%
\author[2]{Michelle M. Gierach}%
\author[3]{Kimberley Miner}%
\author[5]{Shawn Paul Serbin}%
\author[3]{David Schimel}%
\author[6]{Fabian Schneider}%
\author[1]{Alexey N Shiklomanov}%
\author[7]{S. McKenzie Skiles}%
\author[2]{David Ray Thompson}%
\author[3]{Philip Townsend}%
\author[2]{Shannon-Kian Zareh}%
\author[8]{Benjamin Poulter}%
\affil[1]{NASA Goddard Space Flight Center}%
\affil[2]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[3]{Jet Propulsion Laboratory}%
\affil[4]{University of California, Santa Barbara}%
\affil[5]{Brookhaven National Laboratory (DOE)}%
\affil[6]{California Institue of Technology}%
\affil[7]{University of Utah}%
\affil[8]{NASA}%


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  \date{May 19, 2024}


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\selectlanguage{english}
\begin{abstract}
The retrival algorithms used for optical remote sensing satellite data
to estimate Earth's geophysical properties have specific requirements
for spatial resolution, temporal revisit, spectral range and resolution,
and instrument signal to noise ratio (SNR) performance to meet science
objectives. Studies to estimate surface properties from hyperspectral
data use a range of algorithms sensitive to various sources of
spectroscopic uncertainty, which are in turn influenced by mission
architecture choices. Retrieval algorithms vary across scientific fields
and may be more or less sensitive to mission architecture choices that
affect spectral, spatial, or temporal resolutions and spectrometer SNR.
We used representative remote sensing algorithms across terrestrial and
aquatic study domains to inform aspects of mission design that are most
important for impacting accuracy in each scientific area. We simulated
the propagation of uncertainties in the retrieval process including the
effects of different instrument configuration choices. We found that
retrieval accuracy and information content degrade consistently at
\textgreater{}10 nm spectral resolution, \textgreater{}30 m spatial
resolution, and \textgreater{}8 day revisit. In these studies, the noise
reduction associated with lower spatial resolution improved accuracy vis
\selectlanguage{ngerman}à vis high spatial resolution measurements. The interplay between
spatial resolution, temporal revisit and SNR can be quantitatively
assessed for imaging spectroscopy missions and used to identify key
components of algorithm performance and mission observing criteria.%
\end{abstract}\selectlanguage{ngerman}%



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