feat: Add NDVI calculation explanation and formula to the notebook

Author: roberto_esaclear

notebooks/3_interesect_S2_S1.ipynb

@@ -1596,17 +1596,40 @@
     "# Calculate NDVI"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f618310e",
+   "metadata": {},
+   "source": [
+    "### NDVI (Normalized Difference Vegetation Index)\n",
+    "\n",
+    "The **Normalized Difference Vegetation Index (NDVI)** quantifies vegetation greenness and health by comparing the reflectance in the near-infrared (NIR) and red (RED) spectral bands.\n",
+    "\n",
+    "**Formula:**\n",
+    "\n",
+    "NDVI = \\frac{(NIR - RED)}{(NIR + RED)}\n",
+    "\n",
+    "**Where:**\n",
+    "- **NIR** → Reflectance in the near-infrared band (typically 0.76–0.90 µm)  \n",
+    "- **RED** → Reflectance in the red band (typically 0.63–0.69 µm)\n",
+    "\n",
+    "**Typical NDVI interpretation:**\n",
+    "\n",
+    "| NDVI range | Surface type / Vegetation condition |\n",
+    "|:-----------:|:------------------------------------|\n",
+    "| -1.0 – 0.0 | Water, snow, clouds (no vegetation) |\n",
+    "| 0.0 – 0.2  | Bare soil or sparse vegetation |\n",
+    "| 0.2 – 0.5  | Moderate vegetation (grasslands, crops) |\n",
+    "| 0.5 – 1.0  | Dense, healthy vegetation (forests, tropical areas) |\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "71b13ef8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import rasterio\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "# File paths for NIR (B08) and Red (B04) bands\n",
     "band_paths = {\n",
     "    'nir': './out/S2B_MSIL1C_20240320T095649_N0510_R122_T32TQM_20240320T120139.SAFE/GRANULE/L1C_T32TQM_A036756_20240320T100108/IMG_DATA/T32TQM_20240320T095649_B08.jp2',\n",