\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[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{MONITORING CROP PHENOLOGY AT FIELD SCALE COMBINING HIGH AND MEDIUM
SPATIAL RESOLUTION DATA}



\author[1]{Henry Rivas}%
\author[2]{Nicolas Delbart}%
\author[3]{Catherine Ottlé}%
\author[3]{Fabienne Maignan}%
\author[4]{Emmanuelle Vaudour}%
\affil[1]{Université de Paris, UMR8586 PRODIG}%
\affil[2]{Université Paris Diderot, UMR8586 PRODIG}%
\affil[3]{Laboratoire des Sciences du Climat et l'Environnement}%
\affil[4]{ECOSYS, AgroParisTech}%


\vspace{-1em}



  
  \date{May 22, 2024}


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





\selectlanguage{english}
\begin{abstract}
Crop monitoring requires both high spatial resolution data (HSR) for
observing within sub-parcel scale, and high temporal resolution (HTR) to
monitor vegetation changes during along the crop cycle. However, these
simultaneous requirements are difficult to fulfill by the same
satellite. In temperate areas such as the Versailles Plain, near Paris,
France, HRS data have at best a dozen images exploitable per year, even
with Sentinel-2 because of cloud cover, while those with medium spatial
resolution (MRS) provide daily images, but at the generally mixed pixel
scale. In France, the Land Parcel Identification System (LPIS) is an
information system of the crop types declared by farmers, providing
reference information about the annual crops cultivated within each
agricultural parcel. In this work, the objective was to monitor the
phenology of annual crops recorded in the LPIS of 2016, using satellite
image time series from HRS Sentinel-2 (10m) and MRS Proba-V (100m)
acquired from early to end of 2016 over the Versailles Plain, a small
agricultural region (221 km2) cultivated with annual crops. From the two
types of time series, the temporal variations of vegetation indices
(NDVI / EVI2) of crops were extracted in order to analyze the crop
seasonal variations of winter wheat, winter oilseed rape and maize over
2857 parcels with average size of 6.88 ha. The linear method of spatial
disaggregation was applied on the MRS data, using fractions of each crop
type in the mixed pixels calculated from the 2016-LPIS. The temporal
responses from HRS data were compared with those of the MRS sub-pixels.
Comparisons between both time series revealed significant correlations
for the three studied crops (winter wheat = 0.94, winter oilseed rape =
0.74 and maize = 0.79). By improving the temporal frequency of the
monitoring, from13 images for HRS to 25 images for MRS, the
disaggregated MRS time series enabled to distinguish the phenological
stages of the three studied crops better than the HRS time series. In
conclusion, our method of spatial disaggregation can be used to improve
the exploitation of satellite data at MRS in seasonal crop monitoring,
especially during the transition periods when the spectral indices of
crops are likely to change quickly.%
\end{abstract}%



\sloppy


\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/egu2019-11103/egu2019-11103}

\end{center}
\end{figure}

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

