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<body class="fullcontent">
<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<main class="content quarto-banner-title-block" id="quarto-document-content">
<section id="spatial-mapping-in-r" class="level1">
<h1>Spatial Mapping in R</h1>
<section id="sample-dataset" class="level2">
<h2 class="anchored" data-anchor-id="sample-dataset">Sample dataset</h2>
<p>we will use global dataset from two <code>NASA</code> satellites/ instruments- <code>MODIS for NDVI</code> and <code>SMAP for surface (~5cm) soil moisture</code>. We will use global aridity estimates (from [Global Aridity and PET Database] (<a href="https://cgiarcsi.community/data/global-aridity-and-pet-database/" class="uri">https://cgiarcsi.community/data/global-aridity-and-pet-database/</a>) as a general <code>climate</code> classification (Hyper-arid, arid, semi-arid, sub-humid and humid).</p>
<table class="table">
<tbody>
<tr class="odd">
<td style="text-align: left;"><ol type="1">
<li>Global surface soil moisture from NASA’s Soil Moisture Active Passive (SMAP) satellite <span class="citation" data-cites="entekhabi2009">(<a href="#ref-entekhabi2009" role="doc-biblioref">Entekhabi, Njoku, and O’Neill 2009</a>)</span></li>
</ol>
<p><a href="https://smap.jpl.nasa.gov/" class="uri">https://smap.jpl.nasa.gov/</a></p></td>
<td style="text-align: center;"><img src="images/SMAP.jpg" class="img-fluid" width="242"></td>
</tr>
<tr class="even">
<td style="text-align: left;"><ol start="2" type="1">
<li>Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) <span class="citation" data-cites="huete1999modis">(<a href="#ref-huete1999modis" role="doc-biblioref">Huete, Justice, and Van Leeuwen 1999</a>)</span></li>
</ol>
<p><a href="https://modis.gsfc.nasa.gov/data/dataprod/mod13.php" class="uri">https://modis.gsfc.nasa.gov/data/dataprod/mod13.php</a></p></td>
<td style="text-align: center;"><img src="images/modis.jpg" class="img-fluid" width="250"></td>
</tr>
<tr class="odd">
<td style="text-align: left;"><ol start="3" type="1">
<li>Global climate reference land regions from Coupled Model Intercomparison Project (CMIP) project <span class="citation" data-cites="iturbide2020">(<a href="#ref-iturbide2020" role="doc-biblioref">Iturbide et al. 2020</a>)</span></li>
</ol>
<p><a href="https://essd.copernicus.org/articles/12/2959/2020/" class="uri">https://essd.copernicus.org/articles/12/2959/2020/</a></p></td>
<td style="text-align: center;"><img src="images/IPCC_CRR.png" class="img-fluid" width="706"></td>
</tr>
<tr class="even">
<td style="text-align: left;"><ol start="4" type="1">
<li>Climate classification (Hyper-arid, arid, semi-arid, sub-humid and humid) based on Global Aridity Index Database <span class="citation" data-cites="zomer2022version">(<a href="#ref-zomer2022version" role="doc-biblioref">Zomer, Xu, and Trabucco 2022</a>)</span></li>
</ol>
<p><a href="https://csidotinfo.wordpress.com/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v3/" class="uri">https://csidotinfo.wordpress.com/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v3/</a></p></td>
<td style="text-align: center;"><img src="images/aridity_index.PNG" class="img-fluid" width="497"></td>
</tr>
</tbody>
</table>
<p>The sample dataset for this resource is uploaded to <code>GitHub</code> for easy and open access. Download the sample data manually as a zip file from: <code>https://github.com/Vinit-Sehgal/SampleData</code> . Once downloaded, extracting the zip folder to the current working directory.</p>
<p>Alternatively, use the following script to programmatically download and extract the sample data from the <code>GitHub</code> repository.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">###############################################################</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co">#~~~ Import sample data from GitHub repository</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">dir.exists</span>(<span class="st">"SampleData-master"</span>)<span class="sc">==</span><span class="cn">FALSE</span>){ </span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="co"># First we check if the folder already exists. If not, the download begins</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">download.file</span>(<span class="at">url =</span> <span class="st">"https://github.com/Vinit-Sehgal/SampleData/archive/master.zip"</span>,</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="at">destfile =</span> <span class="st">"SampleData-master.zip"</span>) <span class="co"># Download ".Zip"</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Unzip the downloaded .zip file</span></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="fu">unzip</span>(<span class="at">zipfile =</span> <span class="st">"SampleData-master.zip"</span>)</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="co"># getwd() # Current working directory</span></span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="fu">list.files</span>(<span class="st">"./SampleData-master"</span>) <span class="co"># List folder contents. Do you see sample datasets?</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> [1] "CMIP_land"
[2] "functions"
[3] "images"
[4] "Largescale_geospatial_analysis_2022.html"
[5] "Largescale_geospatial_analysis_2023.html"
[6] "location_points.xlsx"
[7] "ne_10m_coastline"
[8] "raster_files"
[9] "README.md"
[10] "sample_pdfs"
[11] "SMAP_L3_USA.nc"
[12] "SMAPL4_H5"
[13] "SMOS_nc"
[14] "USA_states"
[15] "Workbook_DVGAR21-Part1.html"
[16] "Workbook_DVGAR21-Part2.html" </code></pre>
</div>
</div>
</section>
</section>
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" role="list">
<div id="ref-entekhabi2009" class="csl-entry" role="listitem">
Entekhabi, Dara, Eni Njoku, and Peggy O’Neill. 2009. <span>“The Soil Moisture Active and Passive Mission (SMAP): Science and Applications.”</span> <em>2009 IEEE Radar Conference</em>. <a href="https://doi.org/10.1109/radar.2009.4977030">https://doi.org/10.1109/radar.2009.4977030</a>.
</div>
<div id="ref-huete1999modis" class="csl-entry" role="listitem">
Huete, Alfredo, Chris Justice, and Wim Van Leeuwen. 1999. <span>“MODIS Vegetation Index (MOD13).”</span> <em>Algorithm Theoretical Basis Document</em> 3 (213): 295–309.
</div>
<div id="ref-iturbide2020" class="csl-entry" role="listitem">
Iturbide, Maialen, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, et al. 2020. <span>“An Update of IPCC Climate Reference Regions for Subcontinental Analysis of Climate Model Data: Definition and Aggregated Datasets.”</span> <em>Earth System Science Data</em> 12 (4): 2959–70. <a href="https://doi.org/10.5194/essd-12-2959-2020">https://doi.org/10.5194/essd-12-2959-2020</a>.
</div>
<div id="ref-zomer2022version" class="csl-entry" role="listitem">
Zomer, Robert J, Jianchu Xu, and Antonio Trabucco. 2022. <span>“Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.”</span> <em>Scientific Data</em> 9 (1): 409.
</div>
</div></section></div></main>
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