Small corrections for Time Series and Spectral Indices

sources/02c-time-series.ipynb

@@ -367,7 +367,6 @@
     "\n",
     "ncols = 5\n",
     "fig, axs = plt.subplots(nrows=math.ceil(len(output_files) / ncols), ncols=ncols, figsize=(18,18))\n",
-    "plt.suptitle('Visualization of NDVI in 2019')\n",
     "fig.tight_layout(h_pad=2)\n",
     "axs = axs.flatten() \n",
     "\n",
@@ -387,9 +386,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "metadata": {},
    "source": [
+    "The above plot is given as an impression of plant health development in the area of interest for the entire year. \n",
+    "\n",
     "## Average NDVI\n",
     "\n",
     "The average NDVI over the area is calculated using a multiprocessing pool.[^performance_comparison]\n",

sources/02d-spectral-indices.ipynb

@@ -19,7 +19,8 @@
     "  - nbr -- normalized burn ratio\n",
     "  - ndvi -- normalized difference in vegetation\n",
     "  - ndwi -- normalized difference in water\n",
-    "- `fill_value`, the value which is used to represent invalid pixels to handle division by zero.\n",
+    "\n",
+    "The formulas for the indices below are ported from versions [provided by Sentinel Hub](https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/indexdb/), which implements indices listed in the [Index DataBase](https://www.indexdatabase.de/).\n",
     "\n",
     "When running this notebook interactively, _Kernel → Restart and Run All Cells_ can be used to re-evaluate all cells in this notebook after configuring the pipeline.\n",
     "The path of the output file containing the processed values will be printed below the last cell.\n",
@@ -181,7 +182,7 @@
     "    bands = set()\n",
     "    for element in formula:\n",
     "        if type(element) == tuple:\n",
-    "            # recur for subformulas\n",
+    "            # recur for sub-formula\n",
     "            for band in get_bands(element):\n",
     "                bands.add(band)\n",
     "        elif type(element) == str:\n",