\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Multi-sensor approach for high space and time resolution land surface
temperature}



\author[1]{Ankur Rashmikant Desai}%
\author[1]{Anam Munir Khan}%
\author[1]{Ting Zheng}%
\author[2]{Sreenath Paleri}%
\author[2]{Brian J. Butterworth}%
\author[3]{Temple R. Lee}%
\author[4]{Joshua B Fisher}%
\author[4]{Glynn Hulley}%
\author[5]{Tania Kleynhans}%
\author[5]{Aaron Gerace}%
\author[1]{Philip A Townsend}%
\author[6]{Paul Christopher Stoy}%
\author[7]{Stefan Metzger}%
\affil[1]{University of Wisconsin-Madison}%
\affil[2]{Dept. of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison}%
\affil[3]{National Oceanic and Atmospheric Administration (NOAA)}%
\affil[4]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[5]{Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology}%
\affil[6]{University of Wisconsin - Madison}%
\affil[7]{NEON Program, Battelle}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Surface-atmosphere fluxes and their drivers vary across space and time.
A growing area of interest is in downscaling, localizing, and/or
resolving sub-grid scale energy, water, and carbon fluxes and drivers.
Existing downscaling methods require inputs of land surface properties
at relatively high spatial (e.g., sub-kilometer) and temporal (e.g.,
hourly) resolutions, but many observed land surface drivers are not
available at these resolutions. We evaluate an approach to overcome this
challenge for land surface temperature (LST), a World Meteorological
Organization Essential Climate Variable and a key driver for surface
heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study
Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19)
field experiment provided a scalable testbed. We downscaled LST from
satellites (GOES-16 and ECOSTRESS) with further refinement using
airborne hyperspectral imagery. Temporally and spatially downscaled LST
compared well to observations from a network of 20 micrometeorological
towers and airborne in addition to Landsat-based LST retrieval and
drone-based LST observed at one tower site. The downscaled 50-meter
hourly LST showed good relationships with tower
(r\textsuperscript{2}=0.79, precision=3.5 K) and airborne
(r\textsuperscript{2}=0.75, precision=2.4 K) observations over space and
time, with precision lower over wetlands and lakes, and some improvement
for capturing spatio-temporal variation compared to geostationary
satellite. Further downscaling to 10 m using hyperspectral imagery
resolved hotspots and cool spots on the landscape detected in drone LST,
with significant improvement in precision by 1.3 K. These results
demonstrate a simple pathway for multi-sensor retrieval of high space
and time resolution LST.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

