small update to NDVI exercise for uniformity

Author: Sara Safavi

jupyter-notebooks/in-class-exercises/band-math-generate-ndvi/generate-ndvi-exercise.ipynb

@@ -39,7 +39,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To create this vegetation index, we're going to use PlanetScope's SR _(Surface Reflectance)_ data product. You can learn [more about Surface Reflectance (SR) and Planet data here](https://support.planet.com/hc/en-us/sections/115003720348-Surface-Reflectance), but for the purposes of this exercise, all you need to know is: SR data is satellite data that has been algorithmically corrected to remove atmospheric interference."
+    "To create this vegetation index, we're going to use PlanetScope's SR _(Surface Reflectance)_ data product. You can learn [more about Surface Reflectance (SR) and Planet data here](https://support.planet.com/hc/en-us/sections/115003720348-Surface-Reflectance), but for the purposes of this exercise, all you need to know is: SR data is satellite data that has been algorithmically corrected to remove atmospheric interference. If you'd like to learn much more about reflectance conversions and atmospheric interference, see [this in-class exercise](../convert-radiance-to-reflectance/convert-radiance-to-reflectance-key.ipynb)."
    ]
   },
   {
@@ -198,9 +198,8 @@
    "source": [
     "import rasterio\n",
     "\n",
-    "filename = \"data/_____\"\n",
-    "\n",
-    "# < update the filename above to reflect your actual raster image >\n",
+    "filename = None\n",
+    "# < update above with your own code >\n",
     "\n",
     "\n",
     "# Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN\n",