\documentclass[11pt,twoside]{article}\makeatletter \IfFileExists{xcolor.sty}% {\RequirePackage{xcolor}}% {\RequirePackage{color}} \usepackage{colortbl} \usepackage{wrapfig} \usepackage{ifxetex} \ifxetex \usepackage{fontspec} \usepackage{xunicode} \catcode`⃥=\active \def⃥{\textbackslash} \catcode`❴=\active \def❴{\{} \catcode`❵=\active \def❵{\}} \def\textJapanese{\fontspec{Noto Sans CJK JP}} \def\textChinese{\fontspec{Noto Sans CJK SC}} \def\textKorean{\fontspec{Noto Sans CJK KR}} \setmonofont{DejaVu Sans Mono} \else \IfFileExists{utf8x.def}% {\usepackage[utf8x]{inputenc} \PrerenderUnicode{–} }% {\usepackage[utf8]{inputenc}} \usepackage[english]{babel} \usepackage[T1]{fontenc} \usepackage{float} \usepackage[]{ucs} \uc@dclc{8421}{default}{\textbackslash } \uc@dclc{10100}{default}{\{} \uc@dclc{10101}{default}{\}} \uc@dclc{8491}{default}{\AA{}} \uc@dclc{8239}{default}{\,} \uc@dclc{20154}{default}{ } \uc@dclc{10148}{default}{>} \def\textschwa{\rotatebox{-90}{e}} \def\textJapanese{} \def\textChinese{} \IfFileExists{tipa.sty}{\usepackage{tipa}}{} \fi \def\exampleFont{\ttfamily\small} \DeclareTextSymbol{\textpi}{OML}{25} \usepackage{relsize} \RequirePackage{array} \def\@testpach{\@chclass \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else \ifnum \@lastchclass=7 5 \else \ifnum \@lastchclass=8 \tw@ \else \ifnum \@lastchclass=9 \thr@@ \else \z@ \ifnum \@lastchclass = 10 \else \edef\@nextchar{\expandafter\string\@nextchar}% \@chnum \if \@nextchar c\z@ \else \if \@nextchar l\@ne \else \if \@nextchar r\tw@ \else \z@ \@chclass \if\@nextchar |\@ne \else \if \@nextchar !6 \else \if \@nextchar @7 \else \if \@nextchar (8 \else \if \@nextchar )9 \else 10 \@chnum \if \@nextchar m\thr@@\else \if \@nextchar p4 \else \if \@nextchar b5 \else \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi} \gdef\arraybackslash{\let\\=\@arraycr} \def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}} \def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}} \def\abbr{} \def\corr{} \def\expan{} \def\gap{} \def\orig{} \def\reg{} \def\ref{} \def\sic{} \def\persName{}\def\name{} \def\placeName{} \def\orgName{} \def\textcal#1{{\fontspec{Lucida Calligraphy}#1}} \def\textgothic#1{{\fontspec{Lucida Blackletter}#1}} \def\textlarge#1{{\large #1}} \def\textoverbar#1{\ensuremath{\overline{#1}}} \def\textquoted#1{‘#1’} \def\textsmall#1{{\small #1}} \def\textsubscript#1{\@textsubscript{\selectfont#1}} \def\textxi{\ensuremath{\xi}} \def\titlem{\itshape} \newenvironment{biblfree}{}{\ifvmode\par\fi } \newenvironment{bibl}{}{} \newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par } \newenvironment{citbibl}{}{\ifvmode\par\fi } \newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi } \newenvironment{docDate}{}{\ifvmode\par\fi } \newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi } \newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par } \newenvironment{msHead}{\vskip 6pt}{\par} \newenvironment{msItem}{\vskip 6pt}{\par} \newenvironment{rubric}{}{} \newenvironment{titlePart}{}{\par } \newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}} \newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}} \newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}} \newcolumntype{P}[1]{){\arraybackslash}p{#1}} \newcolumntype{B}[1]{){\arraybackslash}b{#1}} \newcolumntype{M}[1]{){\arraybackslash}m{#1}} \definecolor{label}{gray}{0.75} \def\unusedattribute#1{\sout{\textcolor{label}{#1}}} \DeclareRobustCommand*{\xref}{\hyper@normalise\xref@} \def\xref@#1#2{\hyper@linkurl{#2}{#1}} \begingroup \catcode`\_=\active \gdef_#1{\ensuremath{\sb{\mathrm{#1}}}} \endgroup \mathcode`\_=\string"8000 \catcode`\_=12\relax \usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry} \usepackage{framed} \definecolor{shadecolor}{gray}{0.95} \usepackage{longtable} \usepackage[normalem]{ulem} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage{graphicx} \usepackage{marginnote} \renewcommand{\@cite}[1]{#1} \renewcommand*{\marginfont}{\itshape\footnotesize} \def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif} \pagestyle{fancy} \usepackage[pdftitle={Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal}, pdfauthor={}]{hyperref} \hyperbaseurl{} \paperwidth210mm \paperheight297mm \def\@pnumwidth{1.55em} \def\@tocrmarg {2.55em} \def\@dotsep{4.5} \setcounter{tocdepth}{3} \clubpenalty=8000 \emergencystretch 3em \hbadness=4000 \hyphenpenalty=400 \pretolerance=750 \tolerance=2000 \vbadness=4000 \widowpenalty=10000 \renewcommand\section{\@startsection {section}{1}{\z@}% {-1.75ex \@plus -0.5ex \@minus -.2ex}% {0.5ex \@plus .2ex}% {\reset@font\Large\bfseries}} \renewcommand\subsection{\@startsection{subsection}{2}{\z@}% {-1.75ex\@plus -0.5ex \@minus- .2ex}% {0.5ex \@plus .2ex}% {\reset@font\Large}} \renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% {-1.5ex\@plus -0.35ex \@minus -.2ex}% {0.5ex \@plus .2ex}% {\reset@font\large}} \renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% {-1ex \@plus-0.35ex \@minus -0.2ex}% {0.5ex \@plus .2ex}% {\reset@font\normalsize}} \renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}% {1.5ex \@plus1ex \@minus .2ex}% {-1em}% {\reset@font\normalsize\bfseries}} \def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt} \@tempdima 1.5em \begingroup \parindent \z@ \rightskip \@pnumwidth \parfillskip -\@pnumwidth \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par \endgroup} \def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}} \def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}} \def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}} \def\l@subparagraph{\@dottedtocline{5}{10em}{5em}} \@ifundefined{c@section}{\newcounter{section}}{} \@ifundefined{c@chapter}{\newcounter{chapter}}{} \newif\if@mainmatter \@mainmattertrue \def\chaptername{Chapter} \def\frontmatter{% \pagenumbering{roman} \def\thechapter{\@roman\c@chapter} \def\theHchapter{\roman{chapter}} \def\thesection{\@roman\c@section} \def\theHsection{\roman{section}} \def\@chapapp{}% } \def\mainmatter{% \cleardoublepage \def\thechapter{\@arabic\c@chapter} \setcounter{chapter}{0} \setcounter{section}{0} \pagenumbering{arabic} \setcounter{secnumdepth}{6} \def\@chapapp{\chaptername}% \def\theHchapter{\arabic{chapter}} \def\thesection{\@arabic\c@section} \def\theHsection{\arabic{section}} } \def\backmatter{% \cleardoublepage \setcounter{chapter}{0} \setcounter{section}{0} \setcounter{secnumdepth}{2} \def\@chapapp{\appendixname}% \def\thechapter{\@Alph\c@chapter} \def\theHchapter{\Alph{chapter}} \appendix } \newenvironment{bibitemlist}[1]{% \list{\@biblabel{\@arabic\c@enumiv}}% {\settowidth\labelwidth{\@biblabel{#1}}% \leftmargin\labelwidth \advance\leftmargin\labelsep \@openbib@code \usecounter{enumiv}% \let\p@enumiv\@empty \renewcommand\theenumiv{\@arabic\c@enumiv}% }% \sloppy \clubpenalty4000 \@clubpenalty \clubpenalty \widowpenalty4000% \sfcode`\.\@m}% {\def\@noitemerr {\@latex@warning{Empty `bibitemlist' environment}}% \endlist} \def\tableofcontents{\section*{\contentsname}\@starttoc{toc}} \parskip0pt \parindent1em \def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}} \newenvironment{reflist}{% \begin{raggedright}\begin{list}{} {% \setlength{\topsep}{0pt}% \setlength{\rightmargin}{0.25in}% \setlength{\itemsep}{0pt}% \setlength{\itemindent}{0pt}% \setlength{\parskip}{0pt}% \setlength{\parsep}{2pt}% \def\makelabel##1{\itshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{sansreflist}{% \begin{raggedright}\begin{list}{} {% \setlength{\topsep}{0pt}% \setlength{\rightmargin}{0.25in}% \setlength{\itemindent}{0pt}% \setlength{\parskip}{0pt}% \setlength{\itemsep}{0pt}% \setlength{\parsep}{2pt}% \def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2014-01-15 (revised: 15 January 2014)} \def\TheID{\makeatother } \def\TheDate{2014-01-15} \title{Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ \def\@listi{\leftmargin\leftmargini \topsep 2\p@ plus1\p@ minus1\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep 1pt} } \makeatother \fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm} \fancyhf{} \setlength{\headheight}{14pt} \fancyhead[LE]{\bfseries\leftmark} \fancyhead[RO]{\bfseries\rightmark} \fancyfoot[RO]{} \fancyfoot[CO]{\thepage} \fancyfoot[LO]{\TheID} \fancyfoot[LE]{} \fancyfoot[CE]{\thepage} \fancyfoot[RE]{\TheID} \hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true} \fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}} \date{} \usepackage{authblk} \providecommand{\keywords}[1] { \footnotesize \textbf{\textit{Index terms---}} #1 } \usepackage{graphicx,xcolor} \definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Dr. Farooq Ahmad} \author[2]{Dr. Farooq Ahmad} \affil[1]{ Department of Geography, University of the Punjab, Lahore} \renewcommand\Authands{ and } \date{\small \em Received: 10 December 2013 Accepted: 5 January 2014 Published: 15 January 2014} \maketitle \begin{abstract} Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface. Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance. As remote sensed data is generally available at large scale, rather than at field-plot level, use of this information would help to improve crop management at broad-scale. Utilizing the Landsat TM/ETM+ ISODATA clustering algorithm and MODIS (Terra) the normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) datasets allowed the capturing of relevant rice cropping differences. In this study, we tried to analyze the MODIS (Terra) EVI/NDVI (February, 2000 to February, 2013) datasets for rice fractional yield estimation in Narowal, Punjab province of Pakistan. For large scale applications, time integrated series of EVI/NDVI, 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season. The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS/Sinusoidal to the national coordinate systems. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices. These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessment. \end{abstract} \keywords{EVI, Landsat TM/ETM+, land-use, multitemporal, multi-spectral, NDVI, Pakistan.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par emote sensing dataset offers unique possibilities for spatial and temporal characterization of the changes. The fundamental requirement is the availability of different dates of satellite imagery which permits continuous monitoring of change and environmental developments over time \hyperref[b115]{(Lu et al., 2004;} Nasr and Helmy, 2009; Ahmad, 2012b; . RS sensor is a key device that captures data about an object or scene remotely. Since objects have their unique spectral features, they can be identified from RS imagery according to their unique spectral characteristics \hyperref[b225]{(Xie, 2008}; Ahmad and Shafique, 2013; . A good case in vegetation mapping by using RS technology is the spectral radiances in the red and near-infrared (NIR) regions, in addition to others . The radiances in these regions could be incorporated into the spectral vegetation indices (VI) that are directly related to the intercepted fraction of photosynthetically active radiation \hyperref[b17]{(Asrar et al., 1984;}\hyperref[b62]{Galio et al., 1985;}\hyperref[b225]{Xie, 2008}; Ahmad and Shafique, 2013; . The spectral signatures of photosynthetically and nonphotosynthetically active vegetation showed obvious difference and could be utilized to estimate forage quantity and quality of grass prairie \hyperref[b29]{(Beeri et al., 2007;}\hyperref[b225]{Xie, 2008;}.\par RS is the technology that can give an unbiased view of large areas, with spatially explicit information distribution and time repetition, and has thus been widely used to estimate crop yield and offers great potential for monitoring production, yet the uncertainties associated with large-scale crop yield \hyperref[b164]{(Quarmby et al., 1993;}\hyperref[b21]{Báez-González et al., 2002;}\hyperref[b53]{Doraiswamy et al., 2003;}\hyperref[b173]{Ruecker et al., 2007;}\hyperref[b11]{Ahmad and Shafique, 2013a)} estimates are rarely addressed .\par RS dataset of better resolution at different time interval helps in analyzing the rate of changes as well as the causal factors or drivers of changes \hyperref[b45]{(Dai and Khorram, 1999;}\hyperref[b166]{Ramachandra and Kumar, 2004;}\hyperref[b5]{Ahmad, 2012b)}. Hence, it has a significant role in planning at different spatial and temporal scales. Change detection in agricultural planning helped in enhancing the capacity of local governments to implement sound environmental management \hyperref[b162]{(Prenzel and Treitz, 2004;}\hyperref[b166]{Ramachandra and Kumar, 2004;}\hyperref[b5]{Ahmad, 2012b)}. This involves development of spatial and temporal database and analysis techniques. Efficiency of the techniques depends on several factors such as classification schemes, modelling, spatial and \hyperref[b166]{(Ramachandra and Kumar, 2004;}\hyperref[b5]{Ahmad, 2012b)}. Natural resources in the arid environment are declining in productivity and require special attention, and if the ecological condition persists, a further decline in resources may result in land degradation \hyperref[b20]{(Babu et al., 2011)}.\par Preprocessing of satellite datasets prior to vegetation extraction is essential to remove noise \hyperref[b176]{(Schowengerdt, 1983}; and increase the interpretability of image data \hyperref[b34]{(Campbell, 1987;}\hyperref[b177]{Schowengerdt, 2006;}. The ideal result of image preprocessing is that all images after image preprocessing should appear as if they were acquired from the same sensor \hyperref[b76]{(Hall et al., 1991;}\hyperref[b225]{Xie, 2008;}. Image preprocessing commonly comprises a series of operations, including but not limited to bad lines replacement, radiometric correction, geometric correction, image enhancement and masking although variations may exist for images acquired by different sensors \hyperref[b176]{(Schowengerdt, 1983;}\hyperref[b34]{Campbell, 1987;}\hyperref[b225]{Xie, 2008;}. Long-term observations of remotely sensed vegetation dynamics have held an increasingly prominent role in the study of terrestrial ecology \hyperref[b33]{(Budde et al., 2004;}\hyperref[b161]{Prasad et al., 2007;}\hyperref[b150]{Ouyang et al., 2012;}\hyperref[b4]{Ahmad, 2012a)}.\par The development of long-term data records from multi-satellites/multi-sensors is a key requirement to improve our understanding of natural and humaninduced changes on the Earth and their implications (NRC, 2007; \hyperref[b130]{Miura et al., 2008;}\hyperref[b6]{Ahmad, 2012c)}. A major limitation of such studies is the limited availability of sufficiently consistent data derived from long-term RS \hyperref[b150]{(Ouyang et al., 2012;}\hyperref[b4]{Ahmad, 2012a;}. The benefit obtained from a RS sensor, largely depends on its spectral resolution \hyperref[b95]{(Jensen, 2005;}\hyperref[b4]{Ahmad, 2012a;}, which determines the sensor's capability to resolve spectral features of land surfaces \hyperref[b60]{(Fontana, 2009;}\hyperref[b4]{Ahmad, 2012a;}. One of the key factors in assessing vegetation dynamics and its response to climate change is the ability to make frequent and consistent observations \hyperref[b203]{(Thomas and Leason, 2005;}\hyperref[b150]{Ouyang et al., 2012;}\hyperref[b4]{Ahmad, 2012a;}.\par Landsat ETM+ has shown great potential in agricultural mapping and monitoring due to its advantages over traditional procedures in terms of cost effectiveness and timeliness in availability of information over larger areas \hyperref[b137]{(Murthy et al., 1998;}\hyperref[b165]{Rahman et al., 2004;}\hyperref[b2]{Adia and Rabiu, 2008;}\hyperref[b7]{Ahmad, 2012d)} and ingredient the temporal dependence of multi-temporal image data to identify the changing pattern of vegetation cover and consequently enhance the interpretation capabilities. Integration of multi-sensor and multitemporal satellite data effectively improves the temporal attribute and the accuracy of results \hyperref[b2]{(Adia and Rabiu, 2008;}\hyperref[b7]{Ahmad, 2012d)}.\par The MODIS (Terra) NDVI \hyperref[b172]{(Rouse et al., 1973}) and EVI \hyperref[b109]{(Liu and Huete, 1995;}\hyperref[b99]{Justice et al., 1998;}\hyperref[b86]{Huete et al., 1999)} datasets provide unique opportunities for monitoring terrestrial vegetation conditions at regional and global scales \hyperref[b226]{(Yang et al., 1997;}\hyperref[b157]{Piao et al., 2006;}\hyperref[b4]{Ahmad, 2012a;}, and has widely been used in research areas of net primary production \hyperref[b160]{(Potter et al., 1993;}\hyperref[b154]{Paruelo et al., 1997} Multi-year time series of EVI/NDVI can reliably measure yearly-changes in the timing of the availability of high-quality vegetation. The biological significance of NDVI indices should be assessed in various habitat types before they can be widely used in ecological studies \hyperref[b77]{(Hamel et al., 2009;}\hyperref[b4]{Ahmad, 2012a)}. The premise is that the NDVI is an indicator of vegetation health, because degradation of ecosystem vegetation, or a decrease in green, would be reflected in a decrease in NDVI value \hyperref[b77]{(Hamel et al., 2009;}\hyperref[b128]{Meneses-Tovar, 2011;}\hyperref[b4]{Ahmad, 2012a)}. The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a RS tool in studying vegetation phenology cycles at a regional scale \hyperref[b72]{(Guo, 2003;}\hyperref[b4]{Ahmad, 2012a)}.\par The NDVI is established to be highly correlated to green-leaf density and can be viewed as a proxy for above-ground biomass \hyperref[b210]{(Tucker and Sellers, 1986;}\hyperref[b8]{Ahmad, 2012e)}. The NDVI is the most commonly used index of greenness derived from multispectral RS data (USGS, 2010; Ahmad, 2012e), and is used in several studies on vegetation, since it has been proven to be positively correlated with density of green matter \hyperref[b206]{(Townshend et al., 1991;}\hyperref[b87]{Huete et al., 1997;}\hyperref[b85]{Huete et al., 2002;}\hyperref[b47]{Debien et al., 2010;}\hyperref[b8]{Ahmad, 2012e)}. The NDVI provides useful information for detecting and interpreting vegetation land cover it has been widely used in RS studies \hyperref[b54]{(Dorman and Sellers, 1989;}\hyperref[b138]{Myneni and Asrar, 1994;}\hyperref[b63]{Gao, 1996} The District Narowal (Figure \hyperref[fig_0]{1}; 2) lies in the Punjab province of Pakistan from 31° 55' to 32° 30' North latitude and 74° 35' to 75° 21' East longitude. The district is bounded on the north-west by Sialkot district, on the north by Jammu State, on the east by Gurdaspur district (India) and on the south by Amritsar district (India) and Sheikhupura district {\ref (GOP, 2000)}. The general aspect of the district is a plain slopping down from the uplands at the base of the Himalayas to the level country to the south-west (Figure \hyperref[fig_2]{3}), and the general altitude is 266 meters above sea level {\ref (GOP, 2000;}\hyperref[b182]{Shah, 2007)}.\par Bounded on the south-east by the river Ravi, the district is fringed on the either side by a line of fresh alluvial soil, about which rise the low banks that form the limits of the river bed. At about a distance of 24 km from Ravi, another stream, the Dake which rises in the Jammu hills traverses the district. The district is practically a level plain. Its north-eastern boundary is at a distance of about 32 km from the outer line of the Himalayas, but the foot-hills stop short of the district and its surface is level plain broken only by the river Ravi, by more than drainage channels. The general slope as indicated by the lines of drainage is from north-east to south-west (GOP, 2000). inclusion into a categorization algorithm as an input feature \hyperref[b153]{(Ozdogan et al., 2010)}. Using dataset from multiple time periods, the prejudice procedure is based on the different spectral responses of crops according to their phenological evolution \hyperref[b1]{(Abuzar et al., 2001;}\hyperref[b153]{Ozdogan et al., 2010)}. A number of studies have established that using spectral information from two successive seasons in a crop-year is sufficient to identify the paddy/rice fields. However, for each season, the estimates require multiple datasets \hyperref[b1]{(Abuzar et al., 2001;}\hyperref[b152]{Ozdogan et al., 2006;}\hyperref[b153]{Ozdogan et al., 2010)}. This is because single-date analysis in visible cropping intensity often does not take into account planting dates that vary from year to year. Therefore, multi-temporal analysis has greater potential to define paddy/rice fields \hyperref[b14]{(Akbari et al., 2006;}\hyperref[b153]{Ozdogan et al., 2010)}. Eventually, the results of classification are restricted upon the temporal and spatial variability of the spectral signature of the land cover type in question, so suitable datasets The use of the NDVI would comprise direct II. \section[{Research Design and Methods}]{Research Design and Methods}\par must be available for the temporal approach to provide a complete inventory of all crops \hyperref[b153]{(Ozdogan et al., 2010)}.\par RS studies of vegetation normally use specific wavelengths selected to provide information about the vegetation present in the area from which the radiance data emanated. These wavelength regions are selected because they provide a strong signal from the vegetation and also have a spectral contrast from most background resources \hyperref[b210]{(Tucker and Sellers, 1986)}. The wavelength region located in the VIS-NIR transition has been shown to have high information content for vegetation spectra \hyperref[b43]{(Collins, 1978;}\hyperref[b82]{Horler et al., 1983;}\hyperref[b32]{Broge and Leblanc, 2000)}. The spectral reflectance of vegetation in this region is characterized by very low reflectance in the red part of the spectrum followed by an abrupt increase in reflectance at 700-740 nm wavelengths \hyperref[b32]{(Broge and Leblanc, 2000)}. This spectral reflectance pattern of vegetation is generally referred to as the 'red edge'. The red edge position is likewise well correlated with biophysical parameters at the canopy level, but less sensitive to spectral noise caused by the soil background and by atmospheric effects \hyperref[b50]{Demetriades-Shah et al., 1990;}\hyperref[b73]{Guyot et al., 1992;}\hyperref[b126]{Mauser and Bach, 1994;}\hyperref[b32]{Broge and Leblanc, 2000)}.\par Leaf water content governs the reflectance properties beyond 1000 nm, but has practically no effect on the spectral properties in the VIS and NIR regions \hyperref[b32]{(Broge and Leblanc, 2000)}. In fact, chlorophyll concentration was sufficient to absorb nearly all of the blue and red radiation. Reflectance in the green (550 nm) and red-edge (715 nm) bands increase significantly as chlorophyll concentration decrease \hyperref[b46]{(Daughtry et al., 2000)}. Variations of leaf dry matter content affects canopy reflectance by increasing or decreasing the multiple intercellular scattering of the NIR rays. However, for practical RS applications, this effect can be assumed to be negligible, because within-crop variations of leaf dry matter content is very stable \hyperref[b32]{(Broge and Leblanc, 2000)}. Soil compaction negatively affects crop growth characteristics \hyperref[b113]{(Lowery and Schuler, 1991} The MODIS has been supplying a continuous data stream since 2000, lending to comprehensive time series analysis of the global terrestrial environment \hyperref[b70]{(Grogan and Fensholt, 2013)}. Of the available POES datasets, the MODIS reflectance products are favored among many in the research community with a focus on monitoring regional to global vegetation dynamics. The MODIS has a number of advantages when compared to other moderate-to-course resolution sensors, including superior spatial resolution, a broad spectral range (visible to mid-infrared), and superior geolocational accuracy {\ref (Wolfe et} \section[{III.}]{III.} \section[{Results}]{Results}\par The vegetation phenology is important for predicting ecosystem carbon, nitrogen, and water fluxes {\ref (Baldocchi et} The NDVI has been widely used for vegetation monitoring primarily for its simplicity. It is conceived as the normalized difference between the minimum peak of reflectance in the red wavelength and the maximum reflectance in the NIR domain: the higher the index value the better the vegetation conditions in terms of both biomass amount and vegetation health \hyperref[b46]{(Daughtry et al., 2000;}\hyperref[b75]{Haboudane et al., 2002;}\hyperref[b193]{Stroppiana et al., 2006)}.\par Vegetation extraction from satellite imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements and association information \hyperref[b225]{(Xie, 2008} \hyperref[tab_5]{2}.\par Figure {\ref 5} shows classified NDVI 2010, Narowal. After rectification, the NDVI model was applied upon Landsat TM image acquired on 2 nd November, 2010. ArcGIS symbology tool was used to develop NDVI classes and recognize the paddy cropped areas in Narowal. Maximum NDVI, minimum NDVI, mean NDVI and standard deviation is given in Table \hyperref[tab_5]{2}. Figure \hyperref[fig_9]{8} shows image difference or change detection (2001-2010) at Narowal. The findings showed that decreased was 1254.83 km 2 (48.73\%), some decrease 840.27 km 2 (32.64\%), unchanged was 133.95 km 2 (5.20\%), some increase 336.37 km 2 (13.06\%) while increased was 9.58 km 2 (0.37\%). Decreased and some decrease in vegetation cover was much higher as compared to some increase and increased. Accuracy assessment is given in the Table \hyperref[tab_9]{5}.\par Detection of change is the measure of the distinct data framework and thematic change information that can direct to more tangible insights into underlying process involving land cover and land-use changes \hyperref[b186]{(Singh et al., 2013;}. Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities \hyperref[b120]{(Lunetta et al., 2006;} Volume XIV Issue VI Version I \hyperref[tab_10]{6}; Figure \hyperref[fig_25]{21}) to investigate the general trend. Linear forecast trendline showed that fractional yield at Naina Kot was smooth during the entire period. The findings showed that January 2003 was the driest month during the entire period; February 2000 to February 2013. Heavy amount of fertilizer was used for crop growth and soil productivity.\par IV. \section[{Discussion and Conclusions}]{Discussion and Conclusions}\par RS datasets and techniques have already proven to be relevant to many requirements of crop inventory and monitoring \hyperref[b75]{(Haboudane et al., 2002)}. At the present, there is an increased interest in precision farming and the development of smart systems for agricultural resource management; these relatively new approaches aim to increase the productivity, optimize the profitability, and protect the environment. In this context, image-based RS technology is seen as a key tool to provide valuable information that is still lacking or inappropriate to the achievement of sustainable and efficient agricultural practices \hyperref[b133]{(Moran et al., 1997;}\hyperref[b46]{Daughtry et al., 2000;}\hyperref[b75]{Haboudane et al., 2002)}.\par RS provides a key means of measuring and monitoring phenology at continental to global scales and vegetation indices derived from satellite data are now commonly used for this purpose \hyperref[b144]{(Nightingale et al., 2008;}\hyperref[b194]{Tan et al., 2008;}\hyperref[b4]{Ahmad, 2012a;}\hyperref[b9]{2012f)}. The study also identified several data acquisition and processing issues that warrant further investigation. Studies are under way to assess the importance of coordinating and timing field data collection and image acquisition dates as a means of improving the strength of the relationships between image and land condition trend analysis \hyperref[b180]{(Senseman et al., 1996;}\hyperref[b6]{Ahmad, 2012c)} ground-truth data. Recent literature has shown that the narrow bands may be crucial for providing additional information with significant improvements over broad bands in quantifying biophysical characteristics of paddy/rice crop \hyperref[b200]{(Thenkabail et al., 2000)}.\par RS of agricultural resources is based on the measurement of the electromagnetic energy reflected or emitted from the Earth surface as a result of the energy matter interaction. RS data interpretation and processing aim to derive vegetation biophysical properties from its spectral properties \hyperref[b193]{(Stroppiana et al., 2006)}.\par Spectral-based change detection techniques have tended to be performance limited in biologically complex ecosystems due, in larger part, to phenologyinduced errors \hyperref[b118]{(Lunetta et al., 2002;}\hyperref[b119]{Lunetta et al., 2002a;}\hyperref[b120]{Lunetta et al., 2006;}). An important consideration for land cover change detection is the nominal temporal frequency of remote sensor data acquisitions required to adequately characterize change events \hyperref[b117]{(Lunetta et al., 2004;}\hyperref[b120]{Lunetta et al., 2006;}. Ecosystem-specific regeneration rates are important considerations for determining the required frequency of data collections to minimize errors. As part of the natural processes associated with vegetation dynamics, plants undergo intra-annual cycles. During different stages of vegetation growth, plants' structure and associated pigment assemblages can vary significantly \hyperref[b120]{(Lunetta et al., 2006;}.\par Validation is a key issue in RS based studies of phenology over large areas \hyperref[b86]{(Huete, 1999;}\hyperref[b179]{Schwartz and Reed, 1999;}\hyperref[b228]{Zhang et al., 2003;}\hyperref[b7]{Ahmad, 2012d)}. While a variety of field programs for monitoring phenology have been initiated \hyperref[b178]{(Schwartz, 1999;}\hyperref[b228]{Zhang et al., 2003;}\hyperref[b7]{Ahmad, 2012d)}, these programs provide data that is typically specie-specific and which is collected at scales that are not compatible with coarse resolution RS observations.\begin{figure}[htbp] \noindent\textbf{1}\includegraphics[]{image-2.png} \caption{\label{fig_0}Figure 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2}\includegraphics[]{image-3.png} \caption{\label{fig_1}Figure 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3}\includegraphics[]{image-4.png} \caption{\label{fig_2}Figure 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-5.png} \caption{\label{fig_3}}\end{figure} \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-6.png} \caption{\label{fig_4}}\end{figure} \begin{figure}[htbp] \noindent\textbf{4}\includegraphics[]{image-7.png} \caption{\label{fig_5}Figure 4}\end{figure} \begin{figure}[htbp] \noindent\textbf{45}\includegraphics[]{image-8.png} \caption{\label{fig_6}Figure 4 :Figure 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6}\includegraphics[]{image-9.png} \caption{\label{fig_7}Figure 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7}\includegraphics[]{image-10.png} \caption{\label{fig_8}Figure 7}\end{figure} \begin{figure}[htbp] \noindent\textbf{8}\includegraphics[]{image-11.png} \caption{\label{fig_9}Figure 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-12.png} \caption{\label{fig_10}}\end{figure} \begin{figure}[htbp] \noindent\textbf{9}\includegraphics[]{image-13.png} \caption{\label{fig_11}Figure 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{9}\includegraphics[]{image-14.png} \caption{\label{fig_12}Figure 9}\end{figure} \begin{figure}[htbp] \noindent\textbf{10}\includegraphics[]{image-15.png} \caption{\label{fig_13}Figure 10}\end{figure} \begin{figure}[htbp] \noindent\textbf{10}\includegraphics[]{image-16.png} \caption{\label{fig_14}Figure 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11}\includegraphics[]{image-17.png} \caption{\label{fig_15}Figure 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12}\includegraphics[]{image-18.png} \caption{\label{fig_16}Figure 12 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{13}\includegraphics[]{image-19.png} \caption{\label{fig_17}Figure 13 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{14}\includegraphics[]{image-20.png} \caption{\label{fig_18}Figure 14 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{15}\includegraphics[]{image-21.png} \caption{\label{fig_19}Figure 15 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{16}\includegraphics[]{image-22.png} \caption{\label{fig_20}Figure 16 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{17}\includegraphics[]{image-23.png} \caption{\label{fig_21}Figure 17 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{18}\includegraphics[]{image-24.png} \caption{\label{fig_22}Figure 18 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{19}\includegraphics[]{image-25.png} \caption{\label{fig_23}Figure 19 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{20}\includegraphics[]{image-26.png} \caption{\label{fig_24}Figure 20 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{21}\includegraphics[]{image-27.png} \caption{\label{fig_25}Figure 21 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.85\textwidth}} ; Sesnie et al., 2008;\\ Karaburun, 2010; Ahmad, 2012f; Ahmad and Shafique,\\ 2013a; Ahmad et al., 2013).\\ The NDVI is chlorophyll sensitive; the EVI (Liu\\ and Huete, 1995;\end{longtable} \par \caption{\label{tab_1}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.85\textwidth}} The MODIS (Terra) EVI/NDVI (MOD13Q1) data\\ products for research area were acquired, in this case\\ data were downloaded from the Land Processes\\ Distributed Active Archive Center (LPDAAC). Tile\\ number covering this area is h24v05, reprojected from\\ the Integerized Sinusoidal projection to a Geographic\\ Lat/Lon projection, and Datum WGS84 (GSFC/NASA,\\ 2003; Ahmad, 2012a; 2012b; Ahmad et al., 2013). A\\ gapless time series of MODIS (Terra) EVI/NDVI\\ composite raster data from February, 2000 to February,\\ 2013 with a spatial resolution of 250 m (Table 1) was\\ utilized for calculation of the rice fractional yield. The\\ datasets provide frequent information at the spatial\\ scale at which the majority of human-driven land cover\\ changes occur (Townshend and Justice, 1988;\\ Verbesselt et al., 2010; Ahmad, 2012a; Ahmad et al.,\\ 2013). MODIS products are designed to provide\\ consistent spatial and temporal comparisons between\\ different global vegetation conditions that can be used\\ to monitor photosynthetic activity and forecast crop\\ yields (Vazifedoust et al., 2009; Cheng and Wu, 2011;\\ Ahmad et al., 2013). Details documenting the MODIS\\ (Terra) EVI/NDVI compositing process and Quality\\ Assessment Science Data Sets can be found at NASA's\\ MODIS web site (MODIS, 1999; USGS, 2008; Ahmad et\\ al., 2013). This study explored the suitability of the\\ MODIS (Terra) EVI/NDVI (MOD13Q1) pixels obtained\\ from a paddy/rice cultivated area, Naina Kot over\\ thirteen years (February, 2000 to February, 2013), to\\ explore rice fractional yield (Mulianga et al., 2013).\end{longtable} \par \caption{\label{tab_2}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.85\textwidth}} The application of the NDVI (Rouse et al., 1973;\\ Tucker, 1979; Ahmad, 2012a) in ecological studies has\\ enabled quantification and mapping of green vegetation\\ with the goal of estimating above ground net primary\\ productivity and other landscape-level fluxes (Wang et\\ al., 2003; Pettorelli et al., 2005; Aguilar et al., 2012;\\ Ahmad, 2012a).\end{longtable} \par \caption{\label{tab_3}}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.4346590909090909\textwidth}P{0.09659090909090909\textwidth}P{0.15454545454545454\textwidth}P{0.038636363636363635\textwidth}P{0.12556818181818183\textwidth}} Image Acquisition Date\tabcellsep Maximum NDVI\tabcellsep \multicolumn{2}{l}{Minimum NDVI Mean NDVI}\tabcellsep Standard Deviation\\ 30 th September, 2001 (Landsat ETM+)\tabcellsep 0.56\tabcellsep -0.42\tabcellsep 0.05\tabcellsep 0.11\\ 2 nd November, 2010 (Landsat TM)\tabcellsep 0.65\tabcellsep -0.40\tabcellsep 0.13\tabcellsep 0.11\end{longtable} \par \caption{\label{tab_5}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.4878767123287671\textwidth}P{0.1117808219178082\textwidth}P{0.013972602739726026\textwidth}P{0.11876712328767124\textwidth}P{0.05123287671232877\textwidth}P{0.06636986301369863\textwidth}} Image Acquisition Date\tabcellsep Classes\tabcellsep \multicolumn{2}{l}{Area (km 2 )}\tabcellsep Area (\%)\tabcellsep Accuracy Assessment (\%)\\ \tabcellsep River Bed/Floodplain\tabcellsep \tabcellsep 498.69\tabcellsep 19.37\tabcellsep 87.42\\ \tabcellsep Paddy Fields\tabcellsep \tabcellsep 430.88\tabcellsep 16.73\tabcellsep 85.44\\ 30 th September, 2001 (Landsat ETM+)\tabcellsep Stagnant Water Vegetation Cover Other Crops\tabcellsep \tabcellsep 382.97 294.12 565.24\tabcellsep 14.87 11.42 21.95\tabcellsep 87.08 88.45 92.20\\ \tabcellsep Fallow Land\tabcellsep \tabcellsep 403.10\tabcellsep 15.66\tabcellsep 87.29\\ \tabcellsep SUM\tabcellsep \tabcellsep 2575\tabcellsep 100\tabcellsep -\\ \multicolumn{3}{l}{Figure 6 shows supervised classification 2001,}\tabcellsep \multicolumn{3}{l}{of 565.24 km 2 (21.95\%). Accuracy assessment is given}\\ \multicolumn{3}{l}{Narowal. The classification was applied upon Landsat}\tabcellsep in the\tabcellsep \\ \multicolumn{3}{l}{ETM+ image acquired on 30 th September, 2001. The}\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{findings showed that the river bed/floodplain covered}\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{the area of 498.69 km 2 (19.37\%), paddy fields 430.88}\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{vegetation cover 294.12 km 2 (11.42\%), fallow land}\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{403.10 km 2 (15.66\%) while other crops covered the area}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_6}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.85\textwidth}} Year 2014\\ 42\\ ( B )\\ Global Journal of Human Social Science\end{longtable} \par {\small\itshape [Note: © 2014 Global Journals Inc. (US) km 2 (16.73\%), stagnant water 382.97 km 2 (14.87\%),]} \caption{\label{tab_7}Table 3 .}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.15049180327868852\textwidth}P{0.26754098360655737\textwidth}P{0.15049180327868852\textwidth}P{0.12262295081967213\textwidth}P{0.15885245901639344\textwidth}} Image Acquisition Date\tabcellsep Classes\tabcellsep Area (km 2 )\tabcellsep Area (\%)\tabcellsep Accuracy Assessment (\%)\\ \tabcellsep River Bed/Floodplain\tabcellsep 481.90\tabcellsep 18.71\tabcellsep 87.02\\ \tabcellsep Paddy Fields\tabcellsep 400.14\tabcellsep 15.53\tabcellsep 88.04\\ 2 nd November, 2010 (Landsat TM)\tabcellsep Stagnant Water Vegetation Cover Other Crops\tabcellsep 359.31 320.48 467.01\tabcellsep 13.95 12.45 18.14\tabcellsep 92.04 85.42 90.20\\ \tabcellsep Fallow Land\tabcellsep 546.16\tabcellsep 21.22\tabcellsep 87.09\\ \tabcellsep SUM\tabcellsep 2575\tabcellsep 100\tabcellsep -\end{longtable} \par \caption{\label{tab_8}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.2537914691943128\textwidth}P{0.18127962085308058\textwidth}P{0.21753554502369665\textwidth}P{0.19739336492890996\textwidth}} \tabcellsep \tabcellsep During 2001 to 2010\tabcellsep \\ Classes\tabcellsep Area (km 2 )\tabcellsep Area (\%)\tabcellsep Accuracy Assessment\\ \tabcellsep \tabcellsep \tabcellsep (\%)\\ Decreased\tabcellsep 1254.83\tabcellsep 48.73\tabcellsep 87.31\\ Some Decrease\tabcellsep 840.27\tabcellsep 32.64\tabcellsep 90.19\\ Unchanged\tabcellsep 133.95\tabcellsep 5.20\tabcellsep 87.22\\ Some Increase\tabcellsep 336.37\tabcellsep 13.06\tabcellsep 85.79\\ Increased\tabcellsep 9.58\tabcellsep 0.37\tabcellsep 92.14\\ SUM\tabcellsep 2575\tabcellsep 100\tabcellsep -\end{longtable} \par \caption{\label{tab_9}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.1782258064516129\textwidth}P{0.08160522273425498\textwidth}P{0.08225806451612903\textwidth}P{0.10445468509984639\textwidth}P{0.16125192012288786\textwidth}P{0.0737711213517665\textwidth}P{0.07442396313364055\textwidth}P{0.09400921658986175\textwidth}} Image\tabcellsep EVI\tabcellsep NDVI\tabcellsep Fractional\tabcellsep Image\tabcellsep EVI\tabcellsep NDVI\tabcellsep Fractional\\ Acquisition\tabcellsep Pixel\tabcellsep Pixel\tabcellsep Yield\tabcellsep Acquisition\tabcellsep Pixel\tabcellsep Pixel\tabcellsep Yield\\ (Month/Year)\tabcellsep Value\tabcellsep Value\tabcellsep (\%)\tabcellsep (Month/Year)\tabcellsep Value\tabcellsep Value\tabcellsep (\%)\\ Feb. 2000\tabcellsep 3524\tabcellsep 8008\tabcellsep 44.01\tabcellsep Feb. 2007\tabcellsep 4061\tabcellsep 7586\tabcellsep 53.53\\ May 2000\tabcellsep 1775\tabcellsep 2289\tabcellsep 77.54\tabcellsep May 2007\tabcellsep 1590\tabcellsep 2557\tabcellsep 62.18\\ Aug. 2000\tabcellsep 3516\tabcellsep 7839\tabcellsep 44.85\tabcellsep Aug. 2007\tabcellsep 4531\tabcellsep 7971\tabcellsep 56.84\\ Nov. 2000\tabcellsep 1411\tabcellsep 2874\tabcellsep 49.10\tabcellsep Nov. 2007\tabcellsep 1585\tabcellsep 3025\tabcellsep 52.40\\ Feb. 2001\tabcellsep 2363\tabcellsep 6118\tabcellsep 38.62\tabcellsep Feb. 2008\tabcellsep 3564\tabcellsep 7055\tabcellsep 50.52\\ May 2001\tabcellsep 1677\tabcellsep 2332\tabcellsep 71.91\tabcellsep May 2008\tabcellsep 1602\tabcellsep 2447\tabcellsep 65.47\\ Aug. 2001\tabcellsep 3847\tabcellsep 6021\tabcellsep 63.89\tabcellsep Aug. 2008\tabcellsep 2607\tabcellsep 7832\tabcellsep 33.29\\ Nov. 2001\tabcellsep 1687\tabcellsep 3317\tabcellsep 50.86\tabcellsep Nov. 2008\tabcellsep 1984\tabcellsep 3079\tabcellsep 64.44\\ Feb. 2002\tabcellsep 3415\tabcellsep 6524\tabcellsep 52.35\tabcellsep Feb. 2009\tabcellsep 4595\tabcellsep 6857\tabcellsep 67.01\\ May 2002\tabcellsep 1782\tabcellsep 1957\tabcellsep 91.06\tabcellsep May 2009\tabcellsep 1491\tabcellsep 2121\tabcellsep 70.30\\ Aug. 2002\tabcellsep 3988\tabcellsep 7373\tabcellsep 54.09\tabcellsep Aug. 2009\tabcellsep 4786\tabcellsep 7202\tabcellsep 66.45\\ Nov. 2002\tabcellsep 1904\tabcellsep 3596\tabcellsep 52.95\tabcellsep Nov. 2009\tabcellsep 1485\tabcellsep 3416\tabcellsep 43.47\\ Feb. 2003\tabcellsep 3506\tabcellsep 7671\tabcellsep 45.70\tabcellsep Feb. 2010\tabcellsep 3510\tabcellsep 6422\tabcellsep 54.66\\ May 2003\tabcellsep 1669\tabcellsep 1707\tabcellsep 98.12\tabcellsep May 2010\tabcellsep 1205\tabcellsep 2068\tabcellsep 58.27\\ Aug. 2003\tabcellsep 4981\tabcellsep 8101\tabcellsep 61.49\tabcellsep Aug. 2010\tabcellsep 4740\tabcellsep 7610\tabcellsep 62.29\\ Nov. 2003\tabcellsep 1699\tabcellsep 3922\tabcellsep 43.32\tabcellsep Nov. 2010\tabcellsep 1816\tabcellsep 3405\tabcellsep 53.33\\ Feb. 2004\tabcellsep 4858\tabcellsep 7968\tabcellsep 60.97\tabcellsep Feb. 2011\tabcellsep 3994\tabcellsep 6968\tabcellsep 57.32\\ May 2004\tabcellsep 2133\tabcellsep 1792\tabcellsep 119.03\tabcellsep May 2011\tabcellsep 1602\tabcellsep 1961\tabcellsep 81.70\\ Aug. 2004\tabcellsep 4214\tabcellsep 8057\tabcellsep 52.30\tabcellsep Aug. 2011\tabcellsep 2929\tabcellsep 7303\tabcellsep 40.08\\ Nov. 2004\tabcellsep 1937\tabcellsep 4090\tabcellsep 47.36\tabcellsep Nov. 2011\tabcellsep 1951\tabcellsep 3409\tabcellsep 57.23\\ Feb. 2005\tabcellsep 2863\tabcellsep 7701\tabcellsep 37.18\tabcellsep Feb. 2012\tabcellsep 3559\tabcellsep 5639\tabcellsep 63.11\\ May 2005\tabcellsep 1684\tabcellsep 2324\tabcellsep 61.82\tabcellsep May 2012\tabcellsep 1206\tabcellsep 2283\tabcellsep 52.83\\ Aug. 2005\tabcellsep 3252\tabcellsep 7920\tabcellsep 41.06\tabcellsep Aug. 2012\tabcellsep 4804\tabcellsep 7263\tabcellsep 66.14\\ Nov. 2005\tabcellsep 1497\tabcellsep 3240\tabcellsep 46.20\tabcellsep Nov. 2012\tabcellsep 1500\tabcellsep 3205\tabcellsep 46.80\\ Feb. 2006\tabcellsep 3481\tabcellsep 7309\tabcellsep 47.63\tabcellsep Feb. 2013\tabcellsep 3576\tabcellsep 6584\tabcellsep 54.31\\ May 2006\tabcellsep 1578\tabcellsep 2434\tabcellsep 64.83\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Aug. 2006\tabcellsep 2441\tabcellsep 7710\tabcellsep 31.66\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Nov. 2006\tabcellsep 1907\tabcellsep 3292\tabcellsep 57.93\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_10}Table 6 :}\end{figure} \footnote{© 2014 Global Journals Inc. (US)} \footnote{Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal} \backmatter \begin{bibitemlist}{1} \bibitem[Springer]{b38}\label{b38} \textit{}, Netherlands Springer . \textit{Environmental Monitoring and Assessment} 119 (1-3) p. . \bibitem[ Remote Sensing of Environment]{b88}\label{b88} \textit{}, \textit{Remote Sensing of Environment} 59 p. . \bibitem[Reed et al. ()]{b167}\label{b167} \textit{}, B C Reed , J F Brown , D Vanderzee , T R Loveland , J W Merchant , D O Ohlen . 1994. \bibitem[El-Magd and Tanton ()]{b56}\label{b56} \textit{}, I A El-Magd , T W Tanton . \textit{International Journal of Remote Sensing} 2003. 24 p. . \bibitem[Baugh and Groeneveld ()]{b28}\label{b28} \textit{}, W M Baugh , D P Groeneveld . \textit{Journal of Remote Sensing} 2006. 27 (21) p. . \bibitem[Nightingale et al. ()]{b144}\label{b144} \textit{}, J M Nightingale , W E Esaias , R E Wolfe , J E Nickeson , J A Pedelty . 2008. \bibitem[ (2008)]{b131}\label{b131} \textit{}, \xref{http://dx.doi.org/10.1109/IGARSS.2008.4779447}{10.1109/IGARSS.2008.4779447}. July, 2008. Boston, Massachusetts, U.S.A.. \bibitem[Lyon et al. ()]{b121}\label{b121} ‘A change detection experiment using vegetation indices’. J G Lyon , D Yuan , R S Lunetta , C D Elvidge . \textit{Photogrammetric Engineering \& Remote Sensing} 1998. 64 (2) p. . \bibitem[Verhoef et al. ()]{b219}\label{b219} ‘A colour composite of NOAA-AVHRR NDVI based on time series analysis (1981-1992)’. W Verhoef , M Meneti , S Azzali . \textit{International Journal of Remote Sensing} 1996. 17 (2) p. . \bibitem[Huete et al. ()]{b87}\label{b87} \textit{A comparison of vegetation indices over a global set of TM images for EOS-MODIS}, A R Huete , H Q Liu , K Batchily , Van W Leeuwen . 1997. \bibitem[Liu and Huete ()]{b109}\label{b109} ‘A feedback based modification of the NDVI to minimize canopy background and atmospheric noise’. H Q Liu , A R Huete . \textit{IEEE Transactions on Geoscience and Remote Sensing} 1995. 33 (2) p. . \bibitem[Jolly et al. ()]{b97}\label{b97} ‘A generalized, bioclimatic index to predict foliar phenology in response to climate’. W M Jolly , R Nemani , S W Running . \textit{Global Change Biology} 2005. 11 p. . \bibitem[Solano et al. ()]{b190}\label{b190} ‘A global 1 o by 1 o NDVI data set for climate studies, Part 2: The adjustment of the NDVI and generation of global fields of terrestrial biophysical parameters’. R Solano , K Didan , A Jacobson , A R Huete , P J Sellers , C J Tucker , G J Collatz , S Los , C O Justice , D A Dazlich , D A Randall . \textit{International Journal of Remote Sensing} 2010. 1994. 15 p. . (MODIS vegetation index User's Guide. Collection 5, Vegetation Index and 176) \bibitem[Dorman and Sellers ()]{b54}\label{b54} ‘A Global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the simple biosphere model (SiB)’. J L Dorman , P J Sellers . \textit{Journal of Applied Meteorology} 1989. 28 p. . \bibitem[Botta et al. ()]{b31}\label{b31} ‘A global prognostic scheme of leaf onset using satellite data’. A Botta , N Viovy , P Ciais , P Friedlingstein , P Monfray . \textit{Global Change Biology} 2000. 6 (7) p. . \bibitem[Myneni et al. ()]{b139}\label{b139} ‘A large carbon sink in the woody biomass of Northern forests’. R B Myneni , J Dong , C J Tucker , R K Kaufmann , P E Kauppi , J Liski , L Zhou , V Alexeyev , M K Hughes . \textit{Proceedings of the National Academy of Sciences of the United States of America} 2001. 98 p. . \bibitem[Los et al. ()]{b111}\label{b111} ‘A method to convert AVHRR normalized difference vegetation index time series to a standard viewing and illumination geometry’. S O Los , P R J North , W M F Grey , M J Barnsley . \textit{Remote Sensing of Environment} 2005. 99 (4) p. . \bibitem[Ozdogan and Gutman ()]{b151}\label{b151} ‘A new methodology to map irrigated areas using multitemporal MODIS and ancillary data: An application example in the continental US’. M Ozdogan , G Gutman . \textit{Remote Sensing of Environment} 2008. 112 p. . \bibitem[Lloyd ()]{b114}\label{b114} ‘A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery’. D Lloyd . \textit{International Journal of Remote Sensing} 1990. 11 (12) p. . \bibitem[Macleod and Congalton ()]{b122}\label{b122} ‘A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data’. R D Macleod , R G Congalton . \textit{Photogrammetric Engineering and Remote Sensing} 1998. 64 (3) p. . \bibitem[Ahmad ()]{b5}\label{b5} ‘A review of remote sensing data change detection: Comparison of Faisalabad and Multan Districts’. F Ahmad . \textit{Journal of Geography and Regional Planning} 2012b. 5 (9) p. . \bibitem[Huete ()]{b90}\label{b90} \textit{A soil adjusted vegetation index (SAVI)}, A R Huete . 1988. 25 p. . (Remote Sensing of Environment) \bibitem[Gillies et al. ()]{b66}\label{b66} ‘A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature’. R R Gillies , T N Carlson , J Cui , W O Kustas , K S Humes . \textit{International Journal of Remote Sensing} 1997. 18 p. . \bibitem[Wolfe et al. ()]{b223}\label{b223} ‘Achieving sub-pixel geolocation accuracy in support of MODIS land science’. R E Wolfe , M Nishihama , A J Fleig , J A Kuyper , D P Roy , J C Storey , F S Patt . \textit{Remote Sensing of Environment} 2002. 83 (1-2) p. . \bibitem[Schwartz ()]{b178}\label{b178} ‘Advancing to full bloom: Planning phenological research for the 21st century’. M D Schwartz . \textit{International Journal of Biometeorology} 1999. 42 (3) p. . \bibitem[Grau et al. ()]{b69}\label{b69} ‘Agriculture expansion and deforestation in seasonally dry forests of north-west Argentina’. H R Grau , N I Gasparri , T M Aide . \textit{Environmental Conservation} 2005. 32 p. . \bibitem[Yang et al. ()]{b226}\label{b226} ‘An assessment of AVHRR/NDVI-ecoclimatological relations in’. Y Yang , L Yang , J W Merchant . \textit{International Journal of Remote Sensing} 1997. 18 (10) p. . \bibitem[Lunetta et al. ()]{b118}\label{b118} ‘An assessment of NALC/Mexico land-cover mapping results: Implications for assessing landscape change’. R S Lunetta , R Alvarez , C M Edmonds , J G Lyon , C D Elvidge , R Bonifaz . \textit{International Journal of Remote Sensing} 2002. 23 (16) p. . \bibitem[Huete and Liu ()]{b89}\label{b89} ‘An error and sensitivity analysis of the atmospheric-and soilcorrecting variants of the NDVI for the MODIS-EOS’. A R Huete , H Q Liu . \textit{IEEE Transactions on Geoscience and Remote Sensing} 1994. 32 p. . \bibitem[Justice et al. ()]{b98}\label{b98} ‘Analysis of the phenology of global vegetation using meteorological satellite data’. C O Justice , J R G Townshend , B N Holben , C J Tucker . \textit{International Journal of Remote Sensing} 1985. 6 p. . \bibitem[Paruelo et al. ()]{b154}\label{b154} ‘ANPP estimates from NDVI for the Central Grassland Region of the United States’. J M Paruelo , H E Epstein , W K Lauenroth , I C Burke . \textit{Ecology} 1997. 78 (3) p. . \bibitem[Budde et al. ()]{b33}\label{b33} ‘Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis’. M E Budde , G Tappan , J Rowland , J Lewis , L L Tieszen . \textit{Journal of Arid Environments} 2004. 59 (3) p. . \bibitem[Assessing the effect of climate change on honey bees using scale hive records and satellite derived vegetation phenology products ()]{b145}\label{b145} \textit{Assessing the effect of climate change on honey bees using scale hive records and satellite derived vegetation phenology products}, \url{http://www.igarss08.org/Abstracts/pdfs/2282.pdf} 2011. 10. \bibitem[Chen et al. ()]{b37}\label{b37} \textit{Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: An example at corn fields in western Mexico}, P Y Chen , G Fedosejevs , M Tiscareño-López , J G Arnold . 2006. \bibitem[Chen et al. ()]{b40}\label{b40} ‘Assessment of NDVI composite using merged NOAA-14 and NOAA-15 AVHRR data’. P Y Chen , R Srinivasan , G Fedosejevs , A D Báez-González , P Gong . \textit{Geographic Information Sciences} 2002. 8 (1) p. . \bibitem[Vazifedoust et al. ()]{b215}\label{b215} ‘Assimilation of satellite data into agrohydrological models to improve crop yield forecasts’. M Vazifedoust , J C Van Dam , W G M Bastiaanssen , R A Feddes . \textit{International Journal of Remote Sensing} 2009. 30 (10) p. . \bibitem[Myneni and Asrar ()]{b138}\label{b138} ‘Atmospheric effects and spectral vegetation indices’. R B Myneni , G Asrar . \textit{Remote Sensing of Environment} 1994. 47 p. . \bibitem[Eckhardt et al. ()]{b55}\label{b55} ‘Automated update of an irrigated lands GIS using SPOT HRV imagery’. D W Eckhardt , J P Verdin , G R Lyford . \textit{Photogrammetric Engineering \& Remote Sensing} 1990. 56 p. . \bibitem[Hoffer ()]{b81}\label{b81} ‘Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data’. R M Hoffer . \textit{Remote Sensing: The quantitative approach}, P H Swam, S M Davis (ed.) (McGraw-Hill, USA) 1978. p. . \bibitem[Woomer et al. ()]{b224}\label{b224} ‘Carbon stocks in Senegal's Sahel transition zone’. P L Woomer , A Touré , M Sall . \textit{Journal of Arid Environments} 2004. 59 (3) p. . \bibitem[Lambin ()]{b107}\label{b107} ‘Change detection at multiple temporal scales -seasonal and annual variations in landscape variables’. E F Lambin . \textit{Photogrammetric Engineering \& Remote Sensing} 1996. 62 (8) p. . \bibitem[Singh (ed.) ()]{b189}\label{b189} \textit{Change detection in the tropical forest environment of northeastern India using Landsat remote sensing and tropical land management: Remote Sensing and}, A Singh . Tropical Land Management. Eden, M.J. and Perry, J.T. (ed.) 1986. New York: John Wiley \& Sons, Inc. p. . \bibitem[Adia and Rabiu (2008)]{b2}\label{b2} ‘Change detection of vegetation cover, using multitemporal remote sensing data and GIS techniques’. S O Adia , A B Rabiu . \url{http://gisdevelopment.net/application/environment/ffm/adia.htm.} \textit{Proceedings of Map India}, (Map India) 2008. 6-8 February 2008. 2011. 16. (Accessed on September) \bibitem[Rahman et al. ()]{b165}\label{b165} ‘Change detection of winter crop coverage and the use of Landsat data with GIS’. Md R Rahman , A H M H Islam , Md S Hassan . \textit{The Journal of Geo-Environment} 2004. 4 p. . \bibitem[Lu et al. ()]{b115}\label{b115} ‘Change detection techniques’. D Lu , P Mausel , E Brondizios , E Moran . \textit{International Journal of Remote Sensing} 2004. 25 (12) p. . \bibitem[Ozdogan et al. ()]{b152}\label{b152} ‘Changes in summer irrigated crop area and water use in southeastern Turkey from 1993 to 2002: Implications for current and future water resources’. M Ozdogan , C E Woodcock , G D Salvucci , H Demir . \textit{Water Resources Management} 2006. 20 p. . \bibitem[Abuzar et al. ()]{b1}\label{b1} ‘Classification of seasonal images for monitoring irrigated crops in a salinity-affected area of Australia’. M Abuzar , A Mcallister , M Morris . \textit{International Journal of Remote Sensing} 2001. 22 (5) p. . \bibitem[Zhang et al. ()]{b227}\label{b227} ‘Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data’. X Zhang , M A Friedl , C B Schaaf , A H Strahler . \textit{Global Change Biology} 2004. 10 p. . \bibitem[Zoran and Stefan (2006)]{b232}\label{b232} ‘Climatic changes effects on spectral vegetation indices for forested areas analysis from satellite data’. M Zoran , S Stefan . \textit{Proceedings of the 2 nd Environmental Physics Conference}, (the 2 nd Environmental Physics ConferenceAlexandria, Egypt) 2006. 18-22 February 2006. p. . \bibitem[Lucht et al. ()]{b116}\label{b116} ‘Climatic control of the high-latitude vegetation greening trend and Pinatubo effect’. W Lucht , I C Prentice , R B Myneni , S Sitch , P Friedlingstein , W Cramer , P Bousquet , W Buermann , B Smith . \textit{Science} 2002. 296 (5573) p. . \bibitem[Broge and Leblanc ()]{b32}\label{b32} ‘Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of density’. N H Broge , E Leblanc . \textit{Remote Sensing of Environment} 2000. 76 (2) p. . \bibitem[Senseman et al. ()]{b180}\label{b180} ‘Correlation of rangeland cover measures to satellite-imagery-derived vegetation indices’. G M Senseman , C F Bagley , S A Tweddale . \textit{Geocarto International} 1996. 11 (3) p. . \bibitem[Ruecker et al. (2007)]{b173}\label{b173} \textit{Cotton yield estimation in Uzbekistan integrating MODIS, Landsat ETM+ and field data}, G R Ruecker , Z Shi , M Mueller , C Conrad , N Ibragimov , J P A Lamers , C Martius , G Strunz , S W Dech . \url{http://www.isprs.org/proceedings/XXXVI/8-W48/123\textunderscore XXXVI-8-W48.pdf} 2007. January 20. 2013. p. . \bibitem[Akbari et al. ()]{b14}\label{b14} ‘Crop and land cover classification in Iran using Landsat 7 imagery’. M Akbari , A R Mamanpoush , A Gieske , M Miranzadeh , M Torabi , H R Salemi . \textit{International Journal of Remote Sensing} 2006. 27 (19) p. . \bibitem[Doraiswamy et al. ()]{b53}\label{b53} ‘Crop conditions and yield simulations using Landsat and MODIS’. P C Doraiswamy , J L Hatfield , T J Jackson , B Akhmedov , J Prueger , A Stern . \textit{Remote Sensing of Environment} 2003. 92 p. . \bibitem[Jakubauskas et al. ()]{b92}\label{b92} ‘Crop identification using harmonic analysis of time-series AVHRR NDVI data’. M E Jakubauskas , R David , J H Kastens . \textit{Computers and Electronics in Agriculture} 2002. 37 (1-3) p. . \bibitem[Fearnside ()]{b58}\label{b58} ‘Deforestation in Brazilian Amazonia: History, rates, and consequences’. P M Fearnside . \textit{Conservation Biology} 2005. 19 p. . \bibitem[Epiphanio and Huete ()]{b57}\label{b57} ‘Dependence of NDVI and SAVI on sun/sensor geometry and its effect on fPAR relationships in Alfalfa’. J C N Epiphanio , A R Huete . \textit{Remote Sensing of Environment} 1995. 51 p. . \bibitem[Jenkins et al. ()]{b94}\label{b94} ‘Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern U’. J P Jenkins , B H Braswell , S E Frolking , J D Aber . \textit{S. Geophysical Research Letters} 2002. 29 (24) p. . \bibitem[Frank and Menz (2003)]{b61}\label{b61} ‘Detecting seasonal changes in a semi-arid environment using hyperspectral vegetation indices’. M Frank , G Menz . \textit{Proceedings of 3rd EARSel Workshop on Imaging Spectroscopy}, (3rd EARSel Workshop on Imaging SpectroscopyHerrschingen, Germany) 2003. May 2003. p. . \bibitem[Ahmad and Shafique ()]{b10}\label{b10} ‘Detection of change in vegetation cover using multi-spectral and multi-temporal information for District Sargodha’. F Ahmad , K Shafique . \textit{Global Journal of Human Social Sciences: Geography \& Environmental Geo-Sciences} 2013. 13 (1) p. . \bibitem[Singh ()]{b188}\label{b188} ‘Digital change detection techniques using remotely sensed data’. A Singh . \textit{International Journal of Remote Sensing} 1989. 10 (6) p. . \bibitem[Mahmoodzadeh ()]{b123}\label{b123} ‘Digital change detection using remotely sensed data for monitoring green space destruction in Tabriz’. H Mahmoodzadeh . \textit{International Journal of Environmental Research} 2007. 1 (1) p. . \bibitem[Thomas and Leason ()]{b203}\label{b203} ‘Dunefield activity response to climate variability in the southwest Kalahari’. D S G Thomas , H C Leason . \textit{Geomorphology} 2005. 64 (1-2) p. . \bibitem[Fensholt ()]{b59}\label{b59} ‘Earth observation of vegetation status in the Sahelian and Sudanian West Africa: Comparison of Terra MODIS and NOAA AVHRR satellite data’. R Fensholt . \textit{International Journal of Remote Sensing} 2004. 10 p. . \bibitem[Earth Resources Observation and Science Center USGS (2008)]{b213}\label{b213} ‘Earth Resources Observation and Science Center’. \url{http://glovis.usgs.gov} \textit{USGS} 2008. September 09. 2013. \bibitem[Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. National Research Council of the National Academies NRC ()]{b146}\label{b146} ‘Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. National Research Council of the National Academies’. \url{http://www.nap.edu/openbook.php?record\textunderscore id=11820\&page=428.} \textit{NRC} 2007. 2011. The National Academies Press. 17 p. 428. (Accessed on September) \bibitem[Asrar et al. ()]{b17}\label{b17} ‘Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat’. G Asrar , M Fuch , E T Kanemasu . \textit{Agronomy Journal} 1984. 76 p. . \bibitem[Daughtry et al. ()]{b46}\label{b46} ‘Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance’. C S T Daughtry , C L Walthall , M S Kim , E B Colstoun , Iii Mcmurtrey , JE . \textit{Remote Sensing of Environment} 2000. 74 (2) p. . \bibitem[Beeri et al. ()]{b29}\label{b29} ‘Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie’. O Beeri , R Phillips , J Hendrickson . \textit{Remote Sensing of Environment} 2007. 110 p. . \bibitem[Heller and Johnson ()]{b79}\label{b79} ‘Estimating irrigated land acreage from Landsat imagery’. R C Heller , K A Johnson . \textit{Photogrammetric Engineering \& Remote Sensing} 1979. 45 p. . \bibitem[Karaburun ()]{b100}\label{b100} ‘Estimation of C factor for soil erosion modeling using NDVI in Büyükçekmece watershed’. A Karaburun . \textit{Ozean Journal of Applied Sciences} 2010. 3 (1) p. . \bibitem[Chen et al. ()]{b39}\label{b39} ‘Evaluating different NDVI composite techniques using NOAA-14 AVHRR data’. P Y Chen , R Srinivasan , G Fedosejevs , J R Kiniry . \textit{International Journal of Remote Sensing} 2003. 24 p. . \bibitem[Hashemi ()]{b78}\label{b78} ‘Evaluating phenological events of shrubs land by AVHRR data’. S A Hashemi . \textit{American Journal of Scientific Research} 2010. (12) p. . \bibitem[Moran et al. ()]{b134}\label{b134} ‘Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output’. M S Moran , R D Jackson , P N Slater , P M Teillet . \textit{Remote Sensing of Environment} 1992. 41 (2-3) p. . \bibitem[Miura et al. ()]{b130}\label{b130} ‘Evaluation of spectral vegetation index translation equations for the development of long-term data records’. T Miura , H Yoshioka , T Suzuki . \textit{Proceedings of IEEE International Geoscience \& Remote Sensing Symposium}, (IEEE International Geoscience \& Remote Sensing Symposium) 2008. p. . \bibitem[Tucker et al. ()]{b211}\label{b211} ‘Expansion and contraction of the Sahara Desert from 1980 to’. C J Tucker , H E Dregne , W W Newcomb . \textit{Science} 1991. 1990. 253 p. . \bibitem[Grogan and Fensholt ()]{b70}\label{b70} ‘Exploring patterns and effects of aerosol quantity flag anomalies in MODIS surface reflectance products in the tropics’. K Grogan , R Fensholt . \textit{Remote Sensing} 2013. 5 p. . \bibitem[Mulianga et al. ()]{b136}\label{b136} ‘Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI’. B Mulianga , A Bégué , M Simoes , P Todoroff . \textit{Remote Sensing} 2013. 5 p. . \bibitem[Sader et al. ()]{b175}\label{b175} ‘Forest change monitoring of a remote biosphere reserve’. S A Sader , D J Hayes , J A Hepinstall , M Coan , C Soza . \textit{International Journal of Remote Sensing} 2001. 22 (10) p. . \bibitem[Shinners and Binversie ()]{b184}\label{b184} ‘Fractional yield and moisture of corn stover biomass produced in the Northern US Corn Belt’. K J Shinners , B N Binversie . \textit{Biomass and Bioenergy} 2007. 31 p. . \bibitem[Fontana ()]{b60}\label{b60} \textit{From single pixel to continental scale: using AVHRR and MODIS to study land surface parameters in mountain regions}, F M A Fontana . \url{http://www.climatestudies.unibe.ch/students/theses/phd/31.pdf.(AccessedonOctober15} 2009. 2012. Switzerland. Institute of Geography, University of Bern (Ph.D. dissertation) \bibitem[Milich and Weiss ()]{b129}\label{b129} ‘GAC NDVI interannual coefficient of variation (CoV) images: Ground truth sampling of the Sahel along northsouth transects’. L Milich , E Weiss . \textit{International Journal of Remote Sensing} 2000. 21 p. . \bibitem[Thenkabail et al. ()]{b199}\label{b199} ‘Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data’. P S Thenkabail , M Schull , H Turral . \textit{Remote Sensing of Environment} 2005. 95 p. . \bibitem[Babu et al. ()]{b20}\label{b20} ‘Generation of action plans for sustainable agriculture development for rain shadow regions using GIS technology’. D B S Babu , P K Tummalapalli , V M Rao , I V Muralikrishna . \textit{International Journal of Earth Sciences and Engineering} 2011. 4 (6) p. . \bibitem[Ramachandra and Kumar (2004)]{b166}\label{b166} ‘Geographic resources decision support system for land use, land cover dynamics analysis’. T V Ramachandra , U Kumar . \url{http://www.ces.iisc.ernet.in/energy/paper/grdss/viewpaper.pdf.} \textit{Proceedings of the FOSS/GRASS Users Conference}, (the FOSS/GRASS Users ConferenceBangkok, Thailand) 2004. September 2004. 2013. 29 p. . (Accessed on September) \bibitem[Defries et al. ()]{b48}\label{b48} ‘Global discrimination of land cover types from metrics derived from AVHRR pathfinder data’. R S Defries , M Hansen , J R G Townshend . \textit{Remote Sensing of Environment} 1995. 54 (3) p. . \bibitem[Los et al. ()]{b112}\label{b112} ‘Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation’. S O Los , G J Collatz , L Bounoua , P J Sellers , C J Tucker . \textit{Journal of Climate} 2001. 14 p. . \bibitem[Townshend et al. ()]{b206}\label{b206} ‘Global land cover classification by remote sensing: present capabilities and future possibilities’. J R G Townshend , C O Justice , W Li , C Gurney , J Mcmanus . \textit{Remote Sensing of Environment} 1991. 35 (2-3) p. . \bibitem[Defries et al. ()]{b49}\label{b49} ‘Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers’. R S Defries , M Hansen , J R G Townshend , R Sohlberg . \textit{International Journal of Remote Sensing} 1998. 19 p. . \bibitem[Moulin et al. ()]{b135}\label{b135} ‘Global scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements’. S Moulin , L Kergoat , N Viovy , G Dedieu . \textit{Journal of Climate} 1997. 10 p. . \bibitem[Huete (2005)]{b84}\label{b84} ‘Global variability of terrestrial surface properties derived from MODIS visible to thermal-infrared measurements’. A R Huete . \url{http://ieeexplore.ieee.org/iel5/10226/32601/01526} \textit{Proceedings of IGARSS 05 Geoscience and Remote Sensing Symposium}, (IGARSS 05 Geoscience and Remote Sensing Symposium) 2005. July 2005. p. . \bibitem[Tarpley et al. ()]{b195}\label{b195} ‘Global vegetation indices from NOAA-7 meteorological satellite’. J D Tarpley , S R Schnieder , R L Money . \textit{Journal of Climate and Applied Meteorology} 1984. 23 p. . \bibitem[Gsfc/Nasa (2003)]{b71}\label{b71} Gsfc/Nasa . \url{http://modis.gsfc.nasa.gov/about/specs.html} \textit{MODIS technical specifications}, 2003. September 05, 2011. \bibitem[Jakubauskas et al. ()]{b93}\label{b93} ‘Harmonic analysis of time-series AVHRR NDVI data’. M E Jakubauskas , D R Legates , J H Kastens . \textit{Photogrammetric Engineering \& Remote Sensing} 2001. 67 (4) p. . \bibitem[Demetriades-Shah et al. ()]{b50}\label{b50} ‘High resolution derivative spectra in remote sensing’. T H Demetriades-Shah , M D Steven , J A Clark . \textit{Remote Sensing of Environment} 1990. 33 (1) p. . \bibitem[Tucker et al. ()]{b209}\label{b209} ‘Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999’. C J Tucker , D A Slayback , J E Pinzon , S O Los , R B Myneni , M G Taylor . \textit{International Journal of Biometeorology} 2001. 45 (4) p. . \bibitem[Nicandrou (2010)]{b142}\label{b142} ‘Hydrological assessment and modelling of the river Fani catchment’. A Nicandrou . \url{http://dspace1.isd.glam.ac.uk/dspace/handle/10265/461} \textit{Albania. Faculty of Advanced Technology} 2010. November 11. 2012. University of Glamorgan \bibitem[Mcgwire et al. ()]{b127}\label{b127} ‘Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments’. K Mcgwire , T Minor , L Fenstermaker . \textit{Remote Sensing of Environment} 2000. 72 (3) p. . \bibitem[Thenkabail et al. ()]{b200}\label{b200} ‘Hyperspectral vegetation indices and their relationships with agricultural crop characteristics’. P S Thenkabail , R B Smith , E Pauw , De . \textit{Remote Sensing of Environment} 2000. 71 (2) p. . \bibitem[Guyot et al. ()]{b73}\label{b73} ‘Imaging spectroscopy for vegetation studies’. G Guyot , F Baret , S Jacquemoud . \textit{Imaging spectroscopy: Fundamentals and prospective applications}, F Toselli, J Bodechtel (ed.) (Dordrecht) 1992. Kluwer Academic Press. p. . \bibitem[Mauser and Bach (ed.) ()]{b126}\label{b126} \textit{Imaging spectroscopy in hydrology and agriculturedetermination of model parameters}, W Mauser , H Bach . Hill, J. and Mégier, J. (ed.) 1994. Dordrecht: Kluwer Academic Press. p. . (Imaging spectrometry -a tool for environmental observations) \bibitem[Lunetta et al. ()]{b117}\label{b117} ‘Impacts of imagery temporal frequency on land-cover change detection monitoring’. R L Lunetta , D M Johnson , J G Lyon , J Crotwell . \textit{Remote Sensing of Environment} 2004. 89 (4) p. . \bibitem[Lunetta et al. ()]{b119}\label{b119} ‘Impacts of vegetation dynamics on the identification of landcover change in a biologically complex community in North Carolina USA’. R S Lunetta , J Ediriwickrema , D M Johnson , J G Lyon , A Mckerrow . \textit{Remote Sensing of Environment} 2002a. 82 p. . \bibitem[Townshend (1992)]{b204}\label{b204} ‘Improved global data for land applications: A proposal for a new high resolution data set’. J R G Townshend . \url{http://library.wur.nl/WebQuery/clc/916308.(Accessedon} \textit{International Geosphere-Biosphere Program} 1992. June 04. 2012. 20. (Report) \bibitem[Zhao et al. ()]{b229}\label{b229} ‘Improvements of the MODIS terrestrial gross and net primary production global data set’. M Zhao , F A Heinsch , R R Nemani , S Running . \textit{Remote Sensing of Environment} 2005. 95 p. . \bibitem[Myneni et al. ()]{b140}\label{b140} ‘Increased plant growth in the northern high latitudes from 1981-1991’. R B Myneni , C D Keeling , C J Tucker , G Asrar , R R Nemani . \textit{Nature} 1997. 386 p. . \bibitem[Debien et al. ()]{b47}\label{b47} ‘Influence of satellite-derived rainfall patterns on plague occurrence in northeast Tanzania’. A Debien , S Neerinckx , D Kimaro , H Gulinck . \textit{International Journal of Health Geographics} 2010. 9 p. . \bibitem[Richardson et al. ()]{b169}\label{b169} ‘Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests’. A D Richardson , D Y Hollinger , D B Dail , J T Lee , J W Munger , J O'keefe . \textit{Tree Physiology} 2009. 29 (3) p. . \bibitem[Haboudane et al. ()]{b75}\label{b75} ‘Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture’. D Haboudane , J R Miller , N Tremblay , P J Zarco-Tejada , L Dextraze . \textit{Remote Sensing of Environment} 2002. 81 (2-3) p. . \bibitem[Sesnie et al. ()]{b181}\label{b181} ‘Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments’. S E Sesnie , P E Gessler , B Finegan , S Thessler . \textit{Remote Sensing of Environment} 2008. 112 p. . \bibitem[Ayman and Ashraf ()]{b19}\label{b19} ‘Integration of Misrsat-1 and SPOT-2 data for quantitative change detection applications’. H N Ayman , K H Ashraf . \textit{ICGST-GVIP Journal} 2009. 9 (5) p. . \bibitem[Nasr and Helmy ()]{b141}\label{b141} ‘Integration of Misrsat-1 and SPOT-2 data for quantitative change detection applications’. A H Nasr , A K Helmy . \url{http://www.icgst.com/gvip/volume9/Issue5/GVIP\textunderscore V9\textunderscore I5.pdf.} \textit{ICGST-GVIP Journal} 2009. 2011. 9 (5) p. . (Accessed on November 04) \bibitem[Ouyang et al. ()]{b150}\label{b150} ‘Integration of multi-sensor data to assess grassland dynamics in a Yellow River subwatershed’. W Ouyang , F H Hao , A K Skidmore , T A Groen , A G Toxopeus , T Wang . \textit{Ecological Indicators} 2012. 18 p. . \bibitem[Prasad et al. ()]{b161}\label{b161} ‘Inter-annual variability of vegetation cover and rainfall over India’. A K Prasad , S Sudipta , R P Singh , M Kafatos . \textit{Advances in Space Research} 2007. 39 (1) p. . \bibitem[Piao et al. ()]{b158}\label{b158} ‘Interannual variations of monthly and seasonal NDVI in China from 1982 to 1999’. S L Piao , J Y Fang , L M Zhou , Q H Guo , M Henderson , W Ji , Y Li , S Tao . \textit{Journal of Geophysical Research} 2003. 108 (D14) p. . \bibitem[Campbell ()]{b34}\label{b34} \textit{Introduction to remote sensing}, J B Campbell . 1987. New York: The Guilford Press. p. 281. \bibitem[Jensen ()]{b95}\label{b95} \textit{Introductory digital image processing: A remote sensing perspective}, J R Jensen . 2005. (3rd) \bibitem[Thenkabail et al. ()]{b198}\label{b198} ‘Irrigated area maps and statistics of India using remote sensing and national statistics’. P S Thenkabail , V Dheeravath , C M Biradar , O R P Gangalakunta , P Noojipady , C Gurappa , M Velpuri , M Gumma , Y Li . \textit{Remote Sensing} 2009. 1 p. . \bibitem[Manavalan et al. ()]{b124}\label{b124} ‘Irrigated crops monitoring through seasons using digital change detection analysis of IRS-LISS 2 data’. P Manavalan , K Kesavasamy , S Adiga . \textit{International Journal of Remote Sensing} 1995. 16 (4) p. . \bibitem[Ahmad and Shafique ()]{b11}\label{b11} ‘Land degradation pattern using geo-information technology for Kot Addu’. F Ahmad , K Shafique . \textit{Global Journal of Human Social Sciences: Geography \& Environmental Geo-Sciences} 2013a. 13 (1) p. . (Version 1.0) \bibitem[Shah ()]{b182}\label{b182} \textit{Land resource inventory and agricultural land use plan of Narowal district. National Agricultural Land use Plan (Project), Soil Survey of Pakistan}, A H Shah . 2007. Lahore, Pakistan. \bibitem[Singh et al. ()]{b186}\label{b186} ‘Land use and land cover change detection: a comparative approach using post classification change matrix and discriminate function change detection methodology of Allahabad city’. A Singh , S Singh , P K Garg , K Khanduri . \textit{International Journal of Current Engineering and Technology} 2013. 3 (1) p. . \bibitem[Baldi and Paruelo (2008)]{b23}\label{b23} ‘Land use and land cover dynamics in South American temperate Grasslands’. G Baldi , J M Paruelo . \url{http://www.ecologyandsociety.org/vol13/iss2/art6/(Accessedon} \textit{Ecology and Society} 2008. January 11. 2012. 13 (2) . \bibitem[Lunetta et al. ()]{b120}\label{b120} ‘Land-cover change detection using multi-temporal MODIS NDVI data’. R S Lunetta , J F Knight , J Ediriwickrema , J G Lyon , L D Worthy . \textit{Remote Sensing of Environment} 2006. 105 (2) p. . \bibitem[Ahmad ()]{b7}\label{b7} ‘Landsat ETM+ and MODIS EVI/NDVI data products for climatic variation and agricultural measurements in Cholistan Desert’. F Ahmad . \textit{Global Journal of Human Social Science: Geography \& Environmental Geo-Sciences} 2012d. 12 (13) p. . \bibitem[Villalba et al. ()]{b217}\label{b217} ‘Large-scale temperature changes across the southern Andes: 20th century variations in the context of the past 400 years’. R Villalba , A Lara , J A Boninsegna , M Masiokas , S Delgado , J C Aravena , F A Roig , A Schmelter , A Wolodarsky , A Ripalta . \textit{Climatic Change} 2003. 59 p. . \bibitem[Begue ()]{b30}\label{b30} ‘Leaf area index, intercepted photosynthetically active radiation, and spectral vegetation indices: A sensitivity analysis for regular-clumped canopies’. A Begue . \textit{Remote Sensing of Environment} 1993. 46 p. . \bibitem[Baldi et al. ()]{b22}\label{b22} ‘Longterm satellite NDVI datasets: Evaluating their ability to detect ecosystem functional changes in South America’. G Baldi , M D Nosetto , R Aragón , F Aversa , J M Paruelo , E G Jobbágy . \textit{Sensors} 2008. 8 p. . \bibitem[Alexandridis et al. ()]{b16}\label{b16} ‘Mapping irrigated area in Mediterranean basins using low cost satellite Earth Observation’. T K Alexandridis , G C Zalidis , N G Silleos . \textit{Computers and Electronics in Agriculture} 2008. 64 (2) p. . \bibitem[Thelin and Heimes ()]{b197}\label{b197} ‘Mapping irrigated cropland from Landsat data for determination of water use from the high plains aquifer in parts of Colorado’. G P Thelin , F J Heimes . US Geological Survey Professional Paper 1987. \bibitem[Cheng and Wu ()]{b41}\label{b41} ‘Mapping paddy rice yield in Zhejiang Province using MODIS spectral index’. Q Cheng , X Wu . \textit{Turkish Journal of Agriculture \& Forestry} 2011. 35 (6) p. . \bibitem[Keene and Conley ()]{b103}\label{b103} ‘Measurement of irrigated acreage in Western Kansas from LANDSAT images’. K M Keene , C D Conley . \textit{Environmental Geology} 1980. 3 (2) p. . \bibitem[Measuring phenological variability from satellite imagery Journal of Vegetation Science]{b168}\label{b168} ‘Measuring phenological variability from satellite imagery’. \textit{Journal of Vegetation Science} 5 (5) p. . \bibitem[Meneses-Tovar ()]{b128}\label{b128} C L Meneses-Tovar . \textit{NDVI as indicator of degradation. Unasylva}, 2011. 62 p. . \bibitem[Baret et al. ()]{b26}\label{b26} ‘Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands’. F Baret , S Jacquemoud , G Guyot , C Leprieur . \textit{Remote Sensing of Environment} 1992. 41 (2-3) p. . \bibitem[Propastin and Kappas ()]{b163}\label{b163} ‘Modeling net ecosystem exchange for grassland in central Kazakhstan by combining remote sensing and field data’. P Propastin , M Kappas . \textit{Remote Sensing} 2009. 1 p. . \bibitem[Gao and Mas (2008)]{b65}\label{b65} \textit{MODIS EVI as an ancillary data for an object-based image analysis with multi-spectral MODIS data}, Y Gao , J F Mas . \url{http://www.isprs.org/proceedings/XXXVIII/4-C1/Sessions/Session5/6590\textunderscore YGao\textunderscore Proc\textunderscore poster.pdf} 2008. September 07. 2011. \bibitem[Vermote et al. ()]{b220}\label{b220} \textit{MODIS surface reflectance User's Guide. MODIS Land Surface Reflectance Science Computing Facility}, E F Vermote , S Y Kotchenova , J P Ray . 2011. NASA, College Park, MD, USA. p. . \bibitem[Huete et al. ()]{b86}\label{b86} \textit{MODIS vegetation index (MOD 13) algorithm theoretical basis document version 3}, A R Huete , C O Justice , Van W Leeuwen . \url{http://modis.gsfc.nasa.gov/data/atbd/atbd\textunderscore mod13.pdf} 1999. 2011. 17. (Accessed on September) \bibitem[MODIS Vegetation Index (MOD 13): Algorithm theoretical basis document pp MODIS ()]{b132}\label{b132} ‘MODIS Vegetation Index (MOD 13): Algorithm theoretical basis document pp’. \url{http://modis.gsfc.nasa.gov/data/atbd/atbd\textunderscore mod13.pdf.} \textit{MODIS} 1999. 2008. (3) p. . (Accessed on December 04) \bibitem[Stow (ed.) ()]{b192}\label{b192} \textit{Monitoring ecosystem response to global change: Multitemporal remote sensing analyses}, D Stow . Moreno, J. and Oechel, W. (ed.) 1995. New York: Springer-Verlag. p. . (Anticipated Effects of a Changing Global Environment in Mediterranean Type Ecosystems) \bibitem[Mas ()]{b125}\label{b125} ‘Monitoring land-cover changes: a comparison of change detection techniques’. J-F Mas . \textit{International Journal of Remote Sensing} 1999. 20 (1) p. . \bibitem[Tucker et al. ()]{b208}\label{b208} ‘Monitoring large scale vegetation dynamics in the Nile delta and river valley from NOAA AVHRR data’. C J Tucker , J Gatlin , S R Schnieder , M A Kuchinos . \textit{Proceedings of the Conference on Remote Sensing of Arid and Semi-Arid Lands}, (the Conference on Remote Sensing of Arid and Semi-Arid LandsCairo, Egypt) 1982. p. . \bibitem[Starbuck and Tamayo ()]{b191}\label{b191} ‘Monitoring vegetation change in Abu Dhabi Emirate from 1996 to 2000 and 2004 using Landsat satellite imagery’. M J Starbuck , J Tamayo . \textit{Arab Gulf Journal of Scientific Research} 2007. 25 (1-2) p. . \bibitem[Rouse et al. (1973)]{b172}\label{b172} \textit{Monitoring vegetation systems in the Great Plains with ERTS. 3rd ERTS Symposium, NASA SP-351 I}, J W Rouse , R H Haas , J A Schell , D W Deering . \url{http://geol.hu/data/online\textunderscore help/Vegetation\textunderscore Indices.html} 1973. January 10. 2012. p. . \bibitem[Pauchard et al. ()]{b155}\label{b155} ‘Multiple effects of urbanization on the biodiversity of developing countries: The case of a fast-growing metropolitan area’. A Pauchard , M Aguayo , E Peña , R Urrutia . \textit{Biological Conservation} 2006. 127 p. . \bibitem[Barnes et al. (1996)]{b27}\label{b27} ‘Multispectral remote sensing and site-specific agriculture: Examples of current technology and future possibilities’. E M Barnes , M S Moran , Jr P J Pinter , T R Clarke . \textit{Proceedings of the 3rd International Conference on Precision Agriculture}, (the 3rd International Conference on Precision AgricultureMinneapolis, Minnesota, USA) 1996. June 1996. p. . \bibitem[Aguilar et al. ()]{b13}\label{b13} ‘NDVI as an indicator for changes in water availability to woody vegetation’. C Aguilar , J C Zinnert , M J Polo , D R Young . \textit{Ecological Indicators} 2012. 23 p. . \bibitem[Piao et al. ()]{b157}\label{b157} ‘NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China’. S L Piao , A Mohammat , J Y Fang , Q Cai , J Feng . \textit{Global Environmental Change} 2006. 16 (4) p. . \bibitem[Gao ()]{b63}\label{b63} ‘NDWI -A normalized difference water index for remote sensing of vegetation liquid water from space’. B-C Gao . \textit{Remote Sensing of Environment} 1996. 58 (3) p. . \bibitem[Ahmad ()]{b8}\label{b8} ‘NOAA AVHRR NDVI/MODIS NDVI predicts potential to forest resource management in Çatalca district of Turkey’. F Ahmad . \textit{Global Journal of Science Frontier Research: Environment \& Earth Sciences} 2012e. 12 (3) p. . \bibitem[Ahmad ()]{b9}\label{b9} ‘NOAA AVHRR satellite data for evaluation of climatic variation and vegetation change in the Punjab Province’. F Ahmad . \textit{Pakistan. Journal of Food, Agriculture \& Environment} 2012f. 10 (2) p. . \bibitem[Moran et al. ()]{b133}\label{b133} ‘Opportunities and limitations for image-based remote sensing in precision crop management’. M S Moran , Y Inoue , E M Barnes . \textit{Remote Sensing of Environment} 1997. 61 (3) p. . \bibitem[Gao et al. ()]{b64}\label{b64} ‘Optical-biophysical relationships of vegetation spectra without background contamination’. X Gao , A R Huete , W Ni , T Miura . \textit{Remote Sensing of Environment} 2000. 74 (3) p. . \bibitem[Rondeaux et al. ()]{b171}\label{b171} ‘Optimization of soil-adjusted vegetation indices’. G Rondeaux , M Steven , F Baret . \textit{Remote Sensing of Environment} 1996. 55 p. . \bibitem[Huete et al. ()]{b85}\label{b85} ‘Overview of the radiometric and biophysical performance of the MODIS vegetation indices’. A R Huete , K Didan , T Miura , E P Rodriguez , X Gao , L G Ferreira . \textit{Remote Sensing of Environment} 2002. 83 (1-2) p. . \bibitem[Taylor and Gardner ()]{b196}\label{b196} ‘Penetration of cotton seedlings tap roots as influenced by bulk density, moisture content and strength of soil’. H M Taylor , H R Gardner . \textit{Soil Science} 1963. 96 p. . \bibitem[Verbesselt et al. ()]{b218}\label{b218} ‘Phenological change detection while accounting for abrupt and gradual trends in satellite image time series’. J Verbesselt , R Hyndman , A Zeileis , D Culvenor . \textit{Remote Sensing of Environment} 2010. 114 (12) p. . \bibitem[Ahmad ()]{b4}\label{b4} ‘Phenologically-tuned MODIS NDVI-based time series’. F Ahmad . \textit{Global Journal of Human Social Science: Geography \& Environmental Geo-Sciences} 2012a. 2000-2012. 12 (13) p. . \bibitem[Ahmad ()]{b3}\label{b3} ‘Pixel Purity Index algorithm and n-Dimensional Visualization for ETM+ image analysis: A case of District Vehari’. F Ahmad . \textit{Global Journal of Human Social Science: Arts and Humanities} 2012. 12 (15) p. . \bibitem[Population Census Organization, Statistic Division, Government of Pakistan GOP ()]{b68}\label{b68} ‘Population Census Organization, Statistic Division, Government of Pakistan’. \textit{GOP} 2000. 1998. p. . (District census report of Narowal) \bibitem[Baret and Guyot ()]{b25}\label{b25} ‘Potentials and limits of vegetation indices for LAI and APAR assessment’. F Baret , G Guyot . \textit{Remote Sensing of Environment} 1991. 35 p. . \bibitem[Aber et al. ()]{b0}\label{b0} ‘Predicting the effects of climate change on water yield and forest production in the northeastern United States’. J D Aber , S V Ollinger , C A Federer , P B Reich , M L Goulden , D W Kicklighter , J M Melillo , R G Lathrop . \textit{Climate Research} 1995. 5 p. . \bibitem[Baldocchi et al. ()]{b24}\label{b24} ‘Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data’. D D Baldocchi , T A Black , P S Curtis , E Falge , J D Fuentes , A Granier , L Gu , A Knohl , K Pilegaard , H P Schmid , R Valentini , K Wilson , S Wofsy , L Xu , S Yamamoto . \textit{International Journal of Biometeorology} 2005. 49 p. . \bibitem[Hall et al. ()]{b76}\label{b76} ‘Radiometric rectification: Toward a common radiometric response among multi-date, multi-sensor images’. F G Hall , D E Strebel , J E Nickeson , S J Goetz . \textit{Remote Sensing of Environment} 1991. 35 p. . \bibitem[Huang et al. ()]{b83}\label{b83} ‘Rapid loss of Paraguay's Atlantic forest and the status of protected areas: A Landsat assessment’. C Huang , S Kim , A Altstatt , J R G Townshend , P Davis , K Song , C J Tucker , O Rodas , A Yanosky , R Clay , J Musinsky . \textit{Remote Sensing of Environment} 2007. 106 p. . \bibitem[Tucker ()]{b207}\label{b207} ‘Red and photographic infrared linear combinations for monitoring vegetation’. C J Tucker . \textit{Remote Sensing of Environment} 1979. 8 (2) p. . \bibitem[Running and Nemani ()]{b174}\label{b174} ‘Regional hydrologic and carbon balance responses of forests resulting from potential climate change’. S W Running , R R Nemani . \textit{Climatic Change} 1991. 19 p. . \bibitem[Zhou et al. ()]{b230}\label{b230} ‘Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999’. L M Zhou , R K Kaufmann , Y Tian , R B Myneni , C J Tucker . \textit{Journal of Geophysical Research} 2003. 108 (D1) p. p. \bibitem[Glenn et al. ()]{b67}\label{b67} ‘Relationship between remotelysensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape’. E P Glenn , A R Huete , P L Nagler , S G Nelson . \textit{Sensors} 2008. 8 p. . \bibitem[Choudhury ()]{b42}\label{b42} ‘Relationship between vegetation indices, radiation absorption, and net photosynthesis evaluated by sensitivity analysis’. B J Choudhury . \textit{Remote Sensing of Environment} 1987. 22 p. . \bibitem[Guo ()]{b72}\label{b72} ‘Relationships between NDVI and climatological variability in the Prairie ecozone of Canada’. X Guo . \textit{Prairie Perspectives} 2003. 6 p. . \bibitem[Lillesand and Kiefer ()]{b108}\label{b108} \textit{Remote Sensing and Image Interpretation}, T M Lillesand , R W Kiefer . 1994. New York: John Wiley \& Sons, Inc. p. . (3rd Edition) \bibitem[Prenzel and Treitz ()]{b162}\label{b162} ‘Remote sensing change detection for a watershed in north Sulawesi’. B Prenzel , P Treitz . \textit{Indonesia. Progress in Planning} 2004. 61 p. . \bibitem[Haack et al. ()]{b74}\label{b74} ‘Remote sensing change detection of irrigated agriculture in Afghanistan’. B Haack , J Wolf , R English . \textit{Geocarto International} 1998. 13 (2) p. . \bibitem[Dong et al. ()]{b52}\label{b52} ‘Remote sensing estimates of boreal and temperate forest woody biomass, carbon pools, sources and sinks’. J Dong , R K Kaufmann , R B Myneni , C J Tucker , P E Kauppi , J Liski , W Buermann , V Alexeyev , M K Hughes . \textit{Remote Sensing of Environment} 2003. 84 p. . \bibitem[Xie ()]{b225}\label{b225} ‘Remote sensing imagery in vegetation mapping: A review’. Y Xie . \textit{Journal of Plant Ecology} 2008. 1 (1) p. . \bibitem[Collins ()]{b43}\label{b43} ‘Remote sensing of crop type and maturity’. W Collins . \textit{Photogrammetric Engineering \& Remote Sensing} 1978. 44 p. . \bibitem[Ozdogan et al. ()]{b153}\label{b153} ‘Remote sensing of irrigated agriculture: Opportunities and challenges’. M Ozdogan , Y Yang , G Allez , C Cervantes . \textit{Remote Sensing} 2010. 2 p. . \bibitem[Lobell et al. ()]{b110}\label{b110} ‘Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties. Agriculture’. D B Lobell , G P Asner , J I Ortiz-Monasterio , T L Benning . \textit{Ecosystems and Environment} 2003. 94 p. . \bibitem[Niel and Mcvicar (2001)]{b143}\label{b143} \textit{Remote sensing of rice-based irrigated agriculture: A review}, T G V Niel , T R Mcvicar . \url{http://prijipati.library.usyd.edu.au/bitstream/2123/175/1/P1105TR01-01.pdf} 2001. January 23. 2014. \bibitem[Hobbs ()]{b80}\label{b80} ‘Remote sensing of spatial and temporal dynamics of vegetation’. R J Hobbs . \textit{Remote Sensing of Biosphere Functioning}, R J Hobbs, H A Mooney (ed.) (New York) 1989. Springer-Verlag. p. . \bibitem[Schowengerdt ()]{b177}\label{b177} \textit{Remote Sensing: Models and methods for image processing}, R A Schowengerdt . 2006. (3rd) \bibitem[Dai and Khorram ()]{b45}\label{b45} ‘Remotely sensed change detection based on artificial neural networks’. X L Dai , S Khorram . \textit{Photogrammetric Engineering \& Remote Sensing} 1999. 65 (10) p. . \bibitem[Stroppiana et al. ()]{b193}\label{b193} ‘Remotely sensed estimation of rice nitrogen concentration for forcing crop growth models’. D Stroppiana , M Boschetti , P A Brivio , S Bocchi . \textit{Italian Journal of Agrometeorology} 2006. 3 p. . \bibitem[Wessels et al. ()]{b222}\label{b222} ‘Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa’. K J Wessels , K Steenkamp , G Maltitz , S Archibald . \textit{Applied Vegetation Science} 2011. 14 (1) p. . \bibitem[Karnieli and Dall'olmo ()]{b101}\label{b101} ‘Remotesensing monitoring of desertification, phenology, and droughts’. A Karnieli , G Dall'olmo . \textit{An International Journal} 2003. 14 (1) p. . (Management of Environmental Quality) \bibitem[Kumar et al. ()]{b106}\label{b106} ‘Review of hyperspectral remote sensing and vegetation science’. L Kumar , K S Schmidt , S Dury , A K Skidmore . \textit{Imaging spectrometry: Basic Principles and Prospective Applications}, Meer, F D Van Der, S M Jong (ed.) (Dordrecht) 2001. Kluwer Academic Press. p. . \bibitem[Shao et al. ()]{b183}\label{b183} ‘Rice monitoring and production estimation using multitemporal RADARSAT’. Y Shao , X Fan , H Liu , J Xiao , S Ross , B Brisco , R Brown , G Staples . \textit{Remote Sensing of Environment} 2001. 76 (3) p. . \bibitem[Romero and Ordenes ()]{b170}\label{b170} H Romero , F Ordenes . \textit{Emerging urbanization in the Southern Andes: and Development}, 2004. 24 p. . \bibitem[Murthy et al. ()]{b137}\label{b137} ‘Satellite derived crop calendar for canal operation schedule in Bhadra project command area’. C S Murthy , P V Raju , S Jonna , K A Hakeem , S Thiruvengadachari . \textit{India. International Journal of Remote Sensing} 1998. 19 (15) p. . \bibitem[Tucker and Sellers ()]{b210}\label{b210} ‘Satellite remote sensing of primary production’. C J Tucker , P J Sellers . \textit{International Journal of Remote Sensing} 1986. \bibitem[Thiruvengadachari ()]{b202}\label{b202} ‘Satellite sensing of irrigation patterns in semiarid areas: An Indian study’. S Thiruvengadachari . \textit{Photogrammetric Engineering \& Remote Sensing} 1981. 47 p. . \bibitem[Townshend and Justice ()]{b205}\label{b205} ‘Selecting the spatial-resolution of satellite sensors required for global monitoring of land transformations’. J R G Townshend , C O Justice . \textit{International Journal of Remote Sensing} 1988. 9 (2) p. . \bibitem[Zhang et al. ()]{b228}\label{b228} ‘Short communication: Monitoring vegetation phenology using MODIS’. X Zhang , M A Friedl , C B Schaaf , A H Strahler , J C F Hodges , F Gao , B C Reed , A R Huete . \textit{Remote Sensing of Environment} 2003. 84 (3) p. . \bibitem[Unger and Kasper ()]{b212}\label{b212} ‘Soil compaction and root growth: A review’. P W Unger , T C Kasper . \textit{Agronomy Journal} 1994. 86 (5) p. . \bibitem[Johnson et al. ()]{b96}\label{b96} ‘Soybean growth and yield as affected by surface and subsoil compaction’. J F Johnson , W B Voorhees , W W Nelson , G W Randall . \textit{Agronomy Journal} 1990. 82 (5) p. . \bibitem[Asrar et al. ()]{b18}\label{b18} ‘Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study’. G Asrar , R B Myneni , B J Choudhury . \textit{Remote Sensing of Environment} 1992. 41 p. . \bibitem[Nuarsa et al. ()]{b147}\label{b147} ‘Spectral characteristics and mapping of rice plants using multi-temporal Landsat data’. I W Nuarsa , F Nishio , C Hongo . \textit{Journal of Agricultural Science} 2011. 3 (1) p. . \bibitem[Nuarsa et al. ()]{b148}\label{b148} ‘Spectral characterization of rice field using multi-temporal Landsat ETM+ data’. I W Nuarsa , S Kanno , Y Sugimori , F Nishio . \textit{International Journal of Remote Sensing and Earth Sciences} 2005. 2 p. . \bibitem[Galio et al. ()]{b62}\label{b62} ‘Spectral estimation of absorbed photosynthetically active radiation in corn canopies’. K P Galio , C S T Daughtry , M E Bauer . \textit{Agronomy Journal} 1985. 78 p. . \bibitem[Kulkarni and Bajwa ()]{b105}\label{b105} ‘Spectral response of cotton canopy to soil compaction’. S S Kulkarni , S G Bajwa . \textit{Paper Number: 051066, The Society for engineering in agricultural, food, and biological systems}, (USA) 2005. p. . \bibitem[Ahmad ()]{b6}\label{b6} ‘Spectral vegetation indices performance evaluated for Cholistan Desert’. F Ahmad . \textit{Journal of Geography and Regional Planning} 2012c. 5 (6) p. . \bibitem[Hamel et al. ()]{b77}\label{b77} ‘Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates’. S Hamel , M Garel , M Festa-Bianchet , J-M Gaillard , S D Côté . \textit{Journal of Applied Ecology} 2009. 46 p. . \bibitem[Schwartz and Reed ()]{b179}\label{b179} ‘Surface phenology and satellite sensor-derived onset of greenness: An initial comparison’. M D Schwartz , B C Reed . \textit{International Journal of Remote Sensing} 1999. 20 (17) p. . \bibitem[Schowengerdt ()]{b176}\label{b176} \textit{Techniques for image processing and classifications in remote sensing}, R A Schowengerdt . 1983. USA: Academic Press. p. . (1st Edition) \bibitem[Lowery and Schuler ()]{b113}\label{b113} ‘Temporal effects of subsoil compaction on soil strength and plant growth’. B Lowery , R T Schuler . \textit{Soil Science Society of America Journal} 1991. 55 (1) p. . \bibitem[Wang et al. ()]{b221}\label{b221} ‘Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA’. J Wang , P M Rich , K P Price . \textit{International Journal of Remote Sensing} 2003. 24 p. . \bibitem[Potter et al. ()]{b160}\label{b160} ‘Terrestrial ecosystem production, a process model based on global satellite and surface data’. C S Potter , J T Randerson , C B Field , P A Matson , P M Vitousek , H A Mooney , S A Klooster . \textit{Global Biogeochemical Cycles} 1993. 7 p. . \bibitem[Thiam ()]{b201}\label{b201} ‘The causes and spatial pattern of land degradation risk in southern Mauritania using multitemporal AVHRR-NDVI imagery and field data’. A Thiam . \textit{Land Degradation \& Development} 2003. 14 p. . \bibitem[Dennison and Roberts ()]{b51}\label{b51} ‘The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral’. P E Dennison , D A Roberts . \textit{Remote Sensing of Environment} 2003. 87 (2-3) p. . \bibitem[Kolm and Case ()]{b104}\label{b104} ‘The identification of irrigated crop types and estimation of acreages from Landsat imagery’. K E Kolm , H L Case . \textit{Photogrammetric Engineering \& Remote Sensing} 1984. 50 p. . \bibitem[Canziani et al. ()]{b35}\label{b35} ‘The influence of climate and dam construction on the Iberá wetlands’. G A Canziani , R M Ferrati , C Rossi , D Ruiz-Moreno . \textit{Argentina. Regional Environmental Change} 2006. 6 p. . \bibitem[Justice et al. ()]{b99}\label{b99} ‘The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research’. C O Justice , E Vermote , J R G Townshend , R S Defries , D P Roy , D K Hall , V V Salomonson , J Privette , G Riggs , A Strahler , W Lucht , R Myneni , Y Knjazihhin , S Running , R Nemani , Z Wan , A R Huete , W Leeuwen , R E Wolfe , L Giglio , J.-P Muller , P Lewis , M Barnsley . \textit{IEEE Transactions on Geoscience and Remote Sensing} 1998. 36 (4) p. . \bibitem[Horler et al. ()]{b82}\label{b82} ‘The red edge of plant leaf reflectance’. D N H Horler , M Dockray , J Barber . \textit{International Journal of Remote Sensing} 1983. 4 (2) p. . \bibitem[Pax-Lenney et al. ()]{b156}\label{b156} ‘The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM’. M Pax-Lenney , C E Woodcock , J B Collins , H Hamdi . \textit{Remote Sensing of Environment} 1996. 56 p. . \bibitem[Kauth and Thomas (1976)]{b102}\label{b102} ‘The tasseled cap -A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat’. R J Kauth , G S Thomas . \textit{Proceedings of the Symposium on Machine Processing of Remotely Sensed Data}, (the Symposium on Machine Processing of Remotely Sensed DataWest Lafayette, IN, USA) 1976. 29 June to 01 July, 1976. p. . \bibitem[Quarmby et al. ()]{b164}\label{b164} ‘The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction’. N A Quarmby , M Milnes , T L Hindle , N Silleos . \textit{International Journal of Remote Sensing} 1993. 14 p. . \bibitem[Al-Awadhi et al. ()]{b15}\label{b15} \textit{The use of remote sensing \& geographical information systems to identify vegetation: The case of Dhofar Governorate}, T Al-Awadhi , A Al-Shukili , Q Al-Amri . \url{http://www.isprs.org/proceedings/2011/ISRSE-34/211104015Final00239.pdf.} 2011. 2011. Oman. 10. (Assessed on September) \bibitem[Ahmad et al. ()]{b12}\label{b12} ‘The utilization of MODIS and Landsat TM/ETM+ for cotton fractional yield estimation in Burewala’. F Ahmad , K Shafique , S R Ahmad , S.-Ur Rehman , R M A Khan , A Raoof . \textit{Global Journal of Human Social Science: Geography, Geo-Sciences, Environmental Disaster Management} 2013. 13 (7) p. . \bibitem[Villalba et al. ()]{b216}\label{b216} ‘Tree-ring evidence for long-term precipitation changes in subtropical South America’. R Villalba , H R Grau , J A Boninsegna , G C Jacoby , A Ripalta . \textit{International Journal of Climatology} 1998. 18 p. . \bibitem[Báez-González et al. ()]{b21}\label{b21} ‘Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico’. A D Báez-González , P Y Chen , M Tiscareño-López , R Srinivasan . \textit{Crop Science} 2002. 42 (6) p. . \bibitem[Pettorelli et al. ()]{b159}\label{b159} ‘Using the satellite-derived NDVI to assess ecological responses to environmental change’. N Pettorelli , J O Vik , A Mysterud , J M Gaillard , C J Tucker , N C Stenseth . \textit{Trends in Ecology \& Evolution} 2005. 20 (9) p. . \bibitem[Chandola et al. (2010)]{b36}\label{b36} ‘Using time series segmentation for deriving vegetation phenology indices from MODIS NDVI data’. V Chandola , D Hui , L Gu , B Bhaduri , R R Vatsavai . \textit{IEEE International Conference on Data Mining Workshops}, (Sydney, Australia) 2010. 13th December 2010. p. . \bibitem[Nuarsa et al. ()]{b149}\label{b149} ‘Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province’. I W Nuarsa , F Nishio , C Hongo , I G Mahardika . \textit{Indonesia. International Journal of Remote Sensing} 2012. 33 (17) p. . \bibitem[Zhou et al. ()]{b231}\label{b231} ‘Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999’. L M Zhou , C J Tucker , R K Kaufmann , D Slayback , N V Shabanov , R B Myneni . \textit{Journal of Geophysical Research} 2001. 106 p. . \bibitem[Singh et al. ()]{b187}\label{b187} ‘Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India’. R P Singh , S Roy , F Kogan . \textit{International Journal of Remote Sensing} 2003. 24 (22) p. . \bibitem[Cyr et al. ()]{b44}\label{b44} ‘Vegetation indices derived from remote sensing for an estimation of soil protection against water erosion’. L Cyr , F Bonn , A Pesant . \textit{Ecological Modelling} 1995. 79 (1-3) p. . \bibitem[Hufkens et al. ()]{b91}\label{b91} \textit{Vegetation phenology from MODIS/AVHRR/ PhenoCam: Scaling and validation possibilities}, K Hufkens , M A Friedl , A D Richardson , T Milliman , M Migliavacca . \url{http://modis.gsfc.nasa.gov/sci\textunderscore team/meetings/201001/presentations/posters/land/hufkens.pdf} 2010. 2011. 10. \bibitem[Tan et al. ()]{b194}\label{b194} \textit{Vegetation phenology metrics derived from temporally smoothed and gap-filled MODIS data}, B Tan , J T Morisette , R E Wolfe , F Gao , G A Ederer , J M Nightingale , J A Pedelty . \url{URL:www.igarss08.org/Abstracts/pdfs/2347.pdf.} 2008. 2011. 10. (Accessed on November) \bibitem[What is NDVI? United States Geological Survey Science for Changing World (2010)]{b214}\label{b214} ‘What is NDVI? United States Geological Survey’. \url{http://ivm.cr.usgs.gov/} \textit{Science for Changing World} 2010. Accessed on September 10, 2011. (USGS) \bibitem[Shippert (2004)]{b185}\label{b185} \textit{Why use hyperspectral imagery? Photogrammetric Engineering \& Remote Sensing}, P Shippert . \url{http://www.iro.umontreal.ca/\textasciitilde mignotte/IFT6150/ComplementCours/HyperspectralImagery.pdf.(Accessedon} 2004. November 20. 2011. p. . \end{bibitemlist} \end{document}