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\begin{article}
\title{GRASS Image Processing Environment}
\subtitle{Application to Evapotranspiration Direct Readout}
\author{Yann Chemin, Thomas Alexandridis and Ines Cherif}

\maketitle

\section{Abstract}

Satellite imagery provides a large amount of useful information. To
extract this information takes understanding, processing and time. In
GRASS GIS, a given professional of image processing can develop
his/her own modules for processing satellite imagery. The aim of this
paper is to communicate the main lines of the development of an
automated Landsat 7ETM+ chain processing using modules of the GRASS
Image Processing Environmment (GIPE). Specifically called Direct
Readout \citep{Direct_Readout}, the target is real-time processing of
Level 1B standard imagery (Digital Number) into evapotranspiration
products (considered level 4 in MODIS).


\section{Introduction}

Satellite imagery is spatial information, it has spatial dimensions,
but also radiometric, spectral and temporal resolutions. There is a
large amount of data in this raw material already, but this needs
quantification for supporting decision.
Processing of satellite imagery is the science of bringing
measurements, units to the satellite image pixel. These measurements
are commonly found in terms of indices (vegetation indices
especially), some more physically-based information can be derived and
units are then starting to appear, reflected radiations in Watt per
square meter (W/m2) are the first to be generated from the raw data
received.
Some more complex calculations, some would say modelling, can actually
bring more tangible information for decision-makers. In this paper, we
will deal with water evapo-transpired, in terms of mm/day. This kind
of information is critical in watershed analysis, water balance, and
crop production\citep{Chemin:2004}\citep{Chemin:2005}.

Evapotranspiration monitoring is on a high priority on several
decision-making entities agendas. Among those entities are various
water resources management-related agencies, publicly or privately
run. Of course, irrigated crop and environmental managers have dire
interest in accurate, spatially discreet, standardized and temporally
available water balance inputs. Also, natural resources managers use
evapotranspiration for assessing agricultural water pressures and
desertification risk\citep{Alexandridis:2007}\citep{Silleos:2008}. For
about 30 years, the steady rise of methods/models in satellite remote
sensing has increasingly permitted easier and/or more precise
calculations of ET from satellite imagery. As of the 1990s,
thermo-dynamically-based energy balance models have been given some
encouraging results, some reviews can be found in \citep{Kustas:1996},
\citep{Bastiaanssen:1998}, \citep{Menenti:2000} and
\citep{Courault:2005}.

This paper is presenting the application of a \textit{suite} of
various GRASS GIS modules for the image processing requirements of
evapotranspiration mapping. The \textit{suite} was dubbed GRASS Image
Processing Environment (GIPE), as it was created with the aim of
fulfilling any step of the processing requirements independently of
each other, if it is so desired by any future user for any other
future processing needs. This explains the number and diversity of the
modules developed within this GIPE.

\section{Objectives}

The main objectives are:

- Overview the GIPE

- Introduce the Landsat 7 ET script generator

- Produce some output of ET

\section{Overview of the GIPE}

The need of creating a set of modules in GRASS GIS to perform the
regular satellite image processing tasks is required for any
production or research work. The Grass Image Processing Environment
tries to provide such tools at the atomistic and factory levels.
Respectively, there should be a module for each step of any of the
satellite imagery processing, but for some repetitive tasks when
scripting can become too heavy, one module should provide the direct
high-level output expected without losing too much on
parameterization.

The functions developed were essentially in a few categories,
pre-processing and products calculations. Respectively, preparation of
raw satellite imagery into usable geodata (mostly radiometric
correction) and preparation of given products like NDVI, Albedo, and
higher-level ones like ET or soil moisture. In
Figure.~\ref{fig:chemin_et_al_2008_table_0}, a list of various
developed modules is shortly described.

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.5]{project_spotlight/chemin/chemin_et_al_2008_table_0.png}
  %caption of the figure
  \caption{GIPE modules listing: Various}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_table_0}
\end{figure}

Mostly Figure.~\ref{fig:chemin_et_al_2008_table_0} refers to known
processing of satellite
imagery. the module \textit{i.vi} is also (but not in this table)
available in parallel (\textit{i.vi.mpi}) and in grid
(\textit{i.vi.grid}) developed for educational purpose in
\citep{Akhter:2006}. Additionally, it can be noticed in
Figure.~\ref{fig:chemin_et_al_2008_table_0}, there are many ET map
generation modules. Three main groups of ET maps are available here.
The first one is called the \textit{Reference ET}, calibrated after a
20 cm reference height grass without water stress. The second one is
the \textit{Potential ET}, using all energy available in
evapotranspiration, considering unlimited availability of water. At
this point it is important to note that the GRASS HydroFOSS Add-on
\citep{Cannata:2007} is also having an ET module following the
\textit{Potential ET} methodology from Penman and Monteith
(\textit{h.evapo.PM}) that GIPE also installs by default. Finally,
\textit{Actual ET} is a more complex assessment of the environmental
water stress to provide information on water that \textit{actually}
vaporized from water bodies and soil or transpired from vegetation.

Figure.~\ref{fig:chemin_et_al_2008_table_1} provides with specific
energy balance modules created for the purpose of calculating actual
evapotranspiration using mainly equations from \citep{Pawan:2004} and
\citep{Bastiaanssen:1995}. Those are in fact two versions of the
Surface Energy Balance Algorithm for Land (SEBAL) as named by
\citep{Bastiaanssen:1995}.

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.5]{project_spotlight/chemin/chemin_et_al_2008_table_1.png}
  %caption of the figure
  \caption{GIPE modules listing: Energy Balance}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_table_1}
\end{figure}



\section{Processing ET}

Processing a satellite image for evapotranspiration is overviewed in
Fig.~\ref{fig:chemin_et_al_2008_figure_1}

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.55]{project_spotlight/chemin/chemin_et_al_2008_figure_1.png}
  %caption of the figure
  \caption{ET Processing Overview}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_figure_1}
\end{figure}

\subsection{Script generator: Landsat 7ETM+}

The Landsat 7ETM+ script generator is a small program included in GIPE
that creates a full processing of various ET maps by auto-configuring
all the steps from importing/calibrating landsat bands up to the final
products. Below is a small sample of the script after generation. It
loads the .met metadata file, reads parameters from it, and then fills
up the GIPE modules pertaining to the pre-processing and higher level
processing of the data.

Ungzip all Landsat bands, import/create new location, set region to
temperature map.
\textit{DN to Radiance to Reflectance Top of Atmosphere:}

\begin{smallverbatim}i.dn2full.l7 metfile=pXXrXX.met
output=pr\end{smallverbatim}\newline

[skipping...Albedo, Pre-ET Potential, Download/Import SRTM,
Slope/Aspect...]\newline \textit{Potential ET:}

\begin{smallverbatim}i.evapo.potrad -r albedo=pr.albedo tempk=pr.61 \
 lat=pr.latitude doy=pr.doy tsw=pr.tsw etpot=pr.etpot \
rnetd=pr.rnetd\end{smallverbatim}\newline
\textit{NDVI, SAVI:}

\begin{smallverbatim}i.vi viname=ndvi red=pr.3 nir=pr.4
vi=pr.ndvi\end{smallverbatim}\newline

[skipping...Pre-ET stuff, Net radiation,...]\newline
\textit{Soil heat flux:}

\begin{smallverbatim}i.eb.g0 albedo=pr.albedo ndvi=pr.ndvi tempk=pr.61 \
 rnet=pr.rnet time=pr.sath g0=pr.g0\end{smallverbatim}\newline

[skipping...Displacement height, Atmospheric air density,..]\newline
\textit{Sensible heat flux \citep{Pawan:2004}:}

{\footnotesize\tt i.eb.h\_iter rohair=pr.rohair cp=1004.16 dtair=pr.delta \
 tempk=pr.61 disp=pr.disp z0m=pr.z0m z0h=pr.z0h \
 u2m=u2 h0=pr.h0}\newline
\textit{Soil Moisture:}

\begin{smallverbatim}i.eb.evapfr -m rnet=pr.rnet g0=pr.g0 h0=pr.h0 \
 evapfr=pr.evapfr theta=pr.theta\end{smallverbatim}\newline
\textit{Actual ET:}

\begin{smallverbatim}i.eb.eta rnetday=pr.rnetd evapfr=pr.evapfr \
 tempk=pr.61 eta=pr.eta\end{smallverbatim}

\section{Experimental Results}

The study area is in the area of Strimonas river basin in Greece
\citep{Alexandridis:2008}. Strimonas river basin is a transboundary
river basin situated in north Greece (Serres). A large reservoir (Lake
Kerkini) had been constructed in the 1930's to store and regulate
water in the Greek part, and has now become a wetland of International
Importance by the Ramsar Convention, and is included in the Natura
2000 network. An extensive irrigation system taps water from the
reservoir and river diversion dams, and distributes water using open
canals. Local groundwater pumping stations provide supplementary water
to account for the inefficient operation of the irrigation system.
Irrigated area exceeds 100,000 ha, dominated by rice, maize and
cotton.\newline

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.9]{project_spotlight/chemin/chemin_et_al_2008_figure_2.jpg}
  %caption of the figure
  \caption{ETa map of Strimonas Basin after SEBAL95}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_figure_2}
\end{figure}


\subsection{Actual ET}
A first SEBAL processing by \citep{Bastiaanssen:1995} yielded to an
evapotranspiration map (v1995, using i.eb.h\_SEBAL95 to estimate
sensible heat flux) in Fig.\ref{fig:chemin_et_al_2008_figure_2},
similarly, another map was created following SEBAL version as seen in
\citep{Pawan:2004} (using i.eb.h\_iter instead). The determination of
the sensible heat flux by iteration being the specificity of this
model, the main module used has been cited above. Another module
(i.eb.h0) is an atomic equation that does not perform anything else
than h0 calculation. Using shell scripting and i.eb.h0 and other
i.eb.*, one can reconstruct complex models like i.eb.h\_SEBAL95 or
i.eb.h\_iter or any other at their will.

A different location, southwest Southwestern of Strimonas study area,
is also used to demonstrate the comparison capacity of having several
models targeting the same processing into a single processing GRASS
environment. A disparity map (in
Fig.\ref{fig:chemin_et_al_2008_figure_3}) was made as [ETa\_v1995 -
ETa\_v2004] in mm/day of difference across the two versions. One can
see that if the map is positive, then the ETa\_v1995 version is
overestimating and if negative, ETa\_v1995 is underestimating.

Additionally, a histogram (in
Fig.\ref{fig:chemin_et_al_2008_figure_4}) of the disparity map shows
the amount and value of the disparity in terms of pixel count and ETa
difference (mm/day). The equation is disparity\_eta=[eta\_SEBAL95 -
eta\_Pawan04], which incidentally has a lot to do with the way both
estimate h0 parameter through (i.eb.h\_SEBAL95 or i.eb.h\_iter).

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.3]{project_spotlight/chemin/chemin_et_al_2008_figure_3.png}
  %caption of the figure
  \caption{ETa disparity map}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_figure_3}
\end{figure}

%\setkeys{Gin}{width=1\textwidth}
\begin{figure}[htbp]
  \centering
  %name of your graphic, without the path AND in PNG (screenshots etc)/PDF (drawings) format:
  \includegraphics[scale=0.3]{project_spotlight/chemin/chemin_et_al_2008_figure_4.png}
  %caption of the figure
  \caption{ET disparity histogram (v1995-v2004)}
  %label of the figure, which has to correspond to \ref{}:
  \label{fig:chemin_et_al_2008_figure_4}
\end{figure}

One can witness in Fig.\ref{fig:chemin_et_al_2008_figure_4} that both
methods being from the same origin, yet the newer implementation by
\citep{Pawan:2004} (esp. i.eb.h\_iter) differs from the original
\citep{Bastiaanssen:1995} (see i.eb.h\_SEBAL95). The high peak on the
negative side near 0.0mm/d difference is the water pixels estimations,
which are quite concurrent. However the smooth distribution on the
positive side indicates that SEBAL from Bastiaanssen in 1995 has
higher land-based estimations than the version of Pawan in 2004. This
is certainly due to the re-assessment of the [Tsoil\-Tair] and
aerodynamic roughness of momentum heat (rah) during the iterations of
i.eb.h\_SEBAL95, which do not occur in i.eb.h\_iter
\citep{Pawan:2004}.

\section{Conclusion}

The GRASS Image Processing Environment (GIPE) is a set of Add-Ons for
GRASS GIS to process various satellite imagery into higher-level
products. Landsat example above is but one of regular satellite
imagery that can be (pre)processed in GIPE. Currently, specific
satellite pre-processing found in GIPE includes the following sensors:
ASTER, MODIS, AVHRR, Landsat 5/7. Higher-level processing is sensor
independent, as long as spectral or temporal requirements are met.
Finally, there are additional modules not listed here, mostly because
of unfinished state to the day of this reporting.

\section{Acknowledgements}

This work was funded by the extension of the European Space Agency
GSE-Land project
(\href{http://www.gmes-gseland.info/}{http://www.gmes-gseland.info/})
in Greece. Infoterra GmbH is gratefully acknowledged for their
support.

\address{Yann Chemin - \email{ychemin AT csu.edu.au}\\
International Centre of Water for Food Security\\ 
\url{http://www.csu.edu.au/research/icwater}\\
\\Thomas Alexandridis - \email{thalex AT auth.gr} \\
Ines Cherif - \email{icherif AT yahoo.com}\\Agricultural University of Thessaloniki\\ \url{http://users.auth.gr/thalex}}

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\end{thebibliography}
\end{footnotesize}

\end{article}
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