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@@ -334,17 +334,17 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="notebooks/how-to-cite.html">How to Cite This Cookbook</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="notebooks/01_classification.html">Classification of Sentinel-2 imagery</a></li>
 </ul>
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Floodmapping</span></p>
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 <li class="toctree-l1"><a class="reference internal" href="notebooks/02_floodmapping.html">Reverend Bayes updates our Belief in Flood Detection</a></li>
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+<p aria-level="2" class="caption" role="heading"><span class="caption-text">References</span></p>
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@@ -798,7 +798,7 @@ <h3>Running on Your Own Machine<a class="headerlink" href="#running-on-your-own-
 
           By the <a href="https://projectpythia.org/">Project Pythia</a> Community.
 
-          Last updated on 2 August 2024.
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diff --git a/_sources/notebooks/01_classification.ipynb b/_sources/notebooks/01_classification.ipynb
@@ -4,8 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Classification\n",
-    "Finding forests with satelite imagery\n"
+    "# Classification of Sentinel-2 imagery\n",
+    "**Finding forests with satelite imagery**\n",
+    "\n",
+    "## Data Acquisition\n",
+    "In this chapter, we will employ machine learning techniques to classify a scene using satellite imagery. Specifically, we will utilize ``scikit-learn`` to implement two distinct classifiers and subsequently compare their results. To begin, we need to import the following modules.\n"
    ]
   },
   {
@@ -185,7 +188,7 @@
    "metadata": {},
    "source": [
     "### False Color Image\n",
-    "In addition to the regular RGB Image, we can swap any of the bands from the visible spectrum with any other bands. In this specific case the red band has been changed to the near infrared band. This allows us to see vegetated areas more clearly, since they now appear in a bright red color. This is due to the fact that plants absorb regular red light while reflecting near infrared light [@nasa2020]."
+    "In addition to the regular RGB Image, we can swap any of the bands from the visible spectrum with any other bands. In this specific case the red band has been changed to the near infrared band. This allows us to see vegetated areas more clearly, since they now appear in a bright red color. This is due to the fact that plants absorb regular red light while reflecting near infrared light {cite:p}`nasa2020`."
    ]
   },
   {
@@ -214,7 +217,7 @@
    "metadata": {},
    "source": [
     "### NDVI Image\n",
-    "To get an first impression of the data, we can calculate the NDVI (Normalized Difference Vegetation Index) and plot it. The NDVI is calculated by useing the following formula. [@rouse1974monitoring]\n",
+    "To get an first impression of the data, we can calculate the NDVI (Normalized Difference Vegetation Index) and plot it. The NDVI is calculated by useing the following formula. {cite:p}`rouse1974monitoring`\n",
     "\n",
     "$$\n",
     "NDVI = \\frac{NIR - Red}{NIR + Red}\n",

diff --git a/_sources/notebooks/02_floodmapping.ipynb b/_sources/notebooks/02_floodmapping.ipynb
@@ -5,7 +5,14 @@
    "metadata": {},
    "source": [
     "# Reverend Bayes updates our Belief in Flood Detection\n",
-    "How an 275 year old idea helps map the extent of floods\n"
+    "**How an 275 year old idea helps map the extent of floods**\n",
+    "\n",
+    "![Image from [wikipedia](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)\n",
+    "\n",
+    ":::{note}\n",
+    "This notebooks contains interactive element. These interactive element can only be viewed on Binder by clicking on the Binder badge or 🚀 button.\n",
+    ":::\n",
+    "\n"
    ]
   },
   {
@@ -41,9 +48,9 @@
    "source": [
     "## From Backscattering to Flood Mapping\n",
     "\n",
-    "This notebook explains how microwave ($\\sigma^0$) backscattering (@fig-area) can be used to map the extent of a flood. We replicate in this exercise the work of @bauer-marschallinger_satellite-based_2022 on the TU Wien Bayesian-based flood mapping algorithm.\n",
+    "This notebook explains how microwave ($\\sigma^0$) backscattering  can be used to map the extent of a flood. We replicate in this exercise the work of {cite:t}`bauer-marschallinger_satellite-based_2022` on the TU Wien Bayesian-based flood mapping algorithm.\n",
     "\n",
-    "In the following lines we create a map with EOmaps [@quast_getting_2024] of the $\\sigma^0$ backscattering values."
+    "In the following lines we create a map with EOmaps {cite:p}`quast_getting_2024` of the $\\sigma^0$ backscattering values."
    ]
   },
   {
@@ -99,7 +106,7 @@
     "$$P(NF|\\sigma^0)P(\\sigma^0) = P(\\sigma^0|NF)P(NF).$$\n",
     "\n",
     "\n",
-    "The forward probability of $\\sigma^0$ given the occurrence of flooding ($P(\\sigma^0|F)$) and $\\sigma^0$ given no flooding ($P(\\sigma^0|NF)$) can be extracted from past information on backscattering over land and water surfaces. As seen in the sketch below (@fig-sat), the characteristics of backscattering over land and water differ considerably.\n",
+    "The forward probability of $\\sigma^0$ given the occurrence of flooding ($P(\\sigma^0|F)$) and $\\sigma^0$ given no flooding ($P(\\sigma^0|NF)$) can be extracted from past information on backscattering over land and water surfaces. As seen in the sketch below , the characteristics of backscattering over land and water differ considerably.\n",
     "\n",
     "![Schematic backscattering over land and water. Image from [Geological Survey Ireland](https://www.gsi.ie/images/images/SAR_mapping_land_water.jpg)](https://www.gsi.ie/images/images/SAR_mapping_land_water.jpg){#fig-sat}\n",
     "\n",

diff --git a/_sources/notebooks/references.ipynb b/_sources/notebooks/references.ipynb
@@ -6,13 +6,8 @@
    "source": [
     "# References\n",
     "\n",
-    "Bauer-Marschallinger, Bernhard, Senmao Cao, Mark Edwin Tupas, Florian Roth, Claudio Navacchi, Thomas Melzer, Vahid Freeman, and Wolfgang Wagner. 2022. “Satellite-Based Flood Mapping Through Bayesian Inference from a Sentinel-1 SAR Datacube.” Remote Sensing 14 (15): 3673. https://doi.org/10.3390/rs14153673.\n",
-    "\n",
-    "NASA. 2020. “Earth Observatory.” 2020. https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php.\n",
-    "\n",
-    "Quast, Raphael. n.d. “EOmaps: A Python Package to Visualize and Analyze Geographical Datasets.” https://doi.org/10.5281/zenodo.6459598.\n",
-    "\n",
-    "Rouse, John Wilson, Rüdiger H Haas, John A Schell, Donald W Deering, et al. 1974. “Monitoring Vegetation Systems in the Great Plains with ERTS.” NASA Spec. Publ 351 (1): 309."
+    "```{bibliography}\n",
+    "```\n"
    ]
   }
  ],

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-<li class="toctree-l1"><a class="reference internal" href="notebooks/01_classification.html">Classification</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/01_classification.html">Classification of Sentinel-2 imagery</a></li>
 </ul>
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Floodmapping</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="notebooks/02_floodmapping.html">Reverend Bayes updates our Belief in Flood Detection</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">References</span></p>
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           By the <a href="https://projectpythia.org/">Project Pythia</a> Community.
 
-          Last updated on 2 August 2024.
+          Last updated on 18 August 2024.
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