Update documentation

Author: spatialthoughts

_sources/notebooks/xarray_processing_satellite_images.ipynb

@@ -9,7 +9,9 @@
       "source": [
         "# Processing Satellite Images with XArray\n",
         "\n",
-        "The tutorial shows how to take satellite data from widely used sensors and process it using XArray. This tutorial uses data downloaded from the providers and the methods shown is suitable for uses in offline environments or when cloud-hosted datasets cannot be used.\n",
+        "The tutorial shows how to take satellite data from widely used sensors and process it using XArray. This tutorial uses data downloaded from the providers and the methods shown is suitable for uses in **offline environments** or when cloud-hosted datasets cannot be used.\n",
+        "\n",
+        "> For cloud-based processing of satellite images, see our full course [Scalable Remote Sensing Workflows with Xarray](https://courses.spatialthoughts.com/python-remote-sensing.html)\n",
         "\n",
         "> For processing of ResourceSat-2/2A data, see our detailed post [LISS4 Image Processing using XArray and Dask\n",
         "](https://spatialthoughts.com/2023/12/25/liss4-processing-xarray/).\n",
@@ -422,7 +424,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 40,
+      "execution_count": null,
       "id": "HGR85ZGnCNOM",
       "metadata": {
         "colab": {
@@ -457,7 +459,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": null,
       "id": "LDL-_XDfVgdQ",
       "metadata": {
         "colab": {
@@ -544,7 +546,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": null,
       "id": "9WQ01EC5XKDc",
       "metadata": {
         "colab": {
@@ -596,7 +598,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": null,
       "id": "7LfOvUlDYBaT",
       "metadata": {
         "colab": {
@@ -648,7 +650,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 44,
+      "execution_count": null,
       "id": "9BVqlLjOYv_s",
       "metadata": {
         "colab": {
@@ -940,7 +942,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 45,
+      "execution_count": null,
       "id": "dbb0bf6b",
       "metadata": {
         "id": "dbb0bf6b",
@@ -1116,7 +1118,7 @@
         }
       },
       "id": "tqWXxper2ukh",
-      "execution_count": 46,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",

notebooks/xarray_processing_satellite_images.html

@@ -494,7 +494,10 @@ <h2> Contents </h2>
 
   <section id="processing-satellite-images-with-xarray">
 <h1>Processing Satellite Images with XArray<a class="headerlink" href="#processing-satellite-images-with-xarray" title="Link to this heading">#</a></h1>
-<p>The tutorial shows how to take satellite data from widely used sensors and process it using XArray. This tutorial uses data downloaded from the providers and the methods shown is suitable for uses in offline environments or when cloud-hosted datasets cannot be used.</p>
+<p>The tutorial shows how to take satellite data from widely used sensors and process it using XArray. This tutorial uses data downloaded from the providers and the methods shown is suitable for uses in <strong>offline environments</strong> or when cloud-hosted datasets cannot be used.</p>
+<blockquote>
+<div><p>For cloud-based processing of satellite images, see our full course <a class="reference external" href="https://courses.spatialthoughts.com/python-remote-sensing.html">Scalable Remote Sensing Workflows with Xarray</a></p>
+</div></blockquote>
 <blockquote>
 <div><p>For processing of ResourceSat-2/2A data, see our detailed post <a class="reference external" href="https://spatialthoughts.com/2023/12/25/liss4-processing-xarray/">LISS4 Image Processing using XArray and Dask
 </a>.</p>