diff --git a/doc/equalize-histogram.rst b/doc/equalize-histogram.rst
index 63e82fb..4a92923 100644
--- a/doc/equalize-histogram.rst
+++ b/doc/equalize-histogram.rst
@@ -69,12 +69,14 @@ equalization in :func:`xlandsat.equalize_histogram`.
 .. tip::
 
     It can be helpful to do a bit of contrast stretching first, but to a lesser
-    degree than we did previously.
+    degree than we did previously. It's also a good idea to use "float32" for
+    the composite to give it a larger range of color values (but this requires
+    more RAM).
 
 .. jupyter-execute::
 
-    rgb = xls.composite(scene, rescale_to=(0, 0.6))
-    rgb_eq = xls.equalize_histogram(rgb, clip_limit=0.02, kernel_size=300)
+    rgb = xls.composite(scene, rescale_to=(0, 0.8), dtype="float32")
+    rgb_eq = xls.equalize_histogram(rgb, clip_limit=0.04, kernel_size=300)
 
     fig, ax = plt.subplots(1, 1, figsize=(10, 6))
     rgb_eq.plot.imshow(ax=ax)
diff --git a/doc/missing-values.rst b/doc/missing-values.rst
index b9df8d3..0108010 100644
--- a/doc/missing-values.rst
+++ b/doc/missing-values.rst
@@ -1,7 +1,7 @@
 .. _missing-values:
 
-Filling missing values
-======================
+Fill missing values
+===================
 
 Landsat Level 2 data can sometimes contain missing values, particularly around
 bright clouds with dark shadows. These pixels will have a value of
@@ -11,7 +11,8 @@ can use the values of neighboring pixels to interpolate the missing values with
 
 Let's use our sample scene of the December 2015 eruption of `Momotombo volcano
 <https://en.wikipedia.org/wiki/Momotombo>`__, Nicaragua, to demonstrate how
-it's done.
+it's done. The Level 2 version of this scene has several holes because of the
+clouds and smoke from the volcano affecting the atmospheric correction method.
 
 First, we'll import the required packages and load the sample scene:
 
@@ -32,7 +33,7 @@ the missing values don't simply show up as white:
 .. jupyter-execute::
 
     # Make the composite and add it to the scene
-    rgb = xls.composite(scene, rescale_to=(0.04, 0.17))
+    rgb = xls.composite(scene, rescale_to=(0, 0.2))
 
     fig, ax = plt.subplots(1, 1, figsize=(10, 6))
 
@@ -49,7 +50,7 @@ We can fill these values with reasonable estimates using interpolation:
 
     scene_filled = xls.interpolate_missing(scene)
 
-    rgb_filled = xls.composite(scene_filled, rescale_to=(0.04, 0.17))
+    rgb_filled = xls.composite(scene_filled, rescale_to=(0, 0.2))
 
     fig, ax = plt.subplots(1, 1, figsize=(10, 6))