diff --git a/doc/io.rst b/doc/io.rst
index 4aac5e0b6f7..956d9394653 100644
--- a/doc/io.rst
+++ b/doc/io.rst
@@ -26,7 +26,7 @@ The recommended way to store xarray data structures is `netCDF`__, which
 is a binary file format for self-described datasets that originated
 in the geosciences. xarray is based on the netCDF data model, so netCDF files
 on disk directly correspond to :py:class:`Dataset` objects (more accurately,
-a group in a netCDF file directly corresponds to a to :py:class:`Dataset` object.
+a group in a netCDF file directly corresponds to a :py:class:`Dataset` object.
 See :ref:`io.netcdf_groups` for more.)
 
 NetCDF is supported on almost all platforms, and parsers exist
diff --git a/doc/plotting.rst b/doc/plotting.rst
index 3903ea5cde9..f1d100d66f8 100644
--- a/doc/plotting.rst
+++ b/doc/plotting.rst
@@ -37,7 +37,7 @@ For more extensive plotting applications consider the following projects:
   Integrates well with pandas.
 
 - `HoloViews <http://holoviews.org/>`_
-  and `GeoViews <http://geo.holoviews.org/>`_: "Composable, declarative
+  and `GeoViews <https://geoviews.org/>`_: "Composable, declarative
   data structures for building even complex visualizations easily." Includes
   native support for xarray objects.
 
@@ -955,4 +955,4 @@ One can also make line plots with multidimensional coordinates. In this case, ``
     f, ax = plt.subplots(2, 1)
     da.plot.line(x="lon", hue="y", ax=ax[0])
     @savefig plotting_example_2d_hue_xy.png
-    da.plot.line(x="lon", hue="x", ax=ax[1])
\ No newline at end of file
+    da.plot.line(x="lon", hue="x", ax=ax[1])
diff --git a/doc/quick-overview.rst b/doc/quick-overview.rst
index 09b0d4c6fbb..e3d1456f017 100644
--- a/doc/quick-overview.rst
+++ b/doc/quick-overview.rst
@@ -46,7 +46,7 @@ Here are the key properties for a ``DataArray``:
 Indexing
 --------
 
-xarray supports four kind of indexing. Since we have assigned coordinate labels to the x dimension we can use label-based indexing along that dimension just like pandas. The four examples below all yield the same result (the value at `x=10`) but at varying levels of convenience and intuitiveness.
+xarray supports four kinds of indexing. Since we have assigned coordinate labels to the x dimension we can use label-based indexing along that dimension just like pandas. The four examples below all yield the same result (the value at `x=10`) but at varying levels of convenience and intuitiveness.
 
 .. ipython:: python
 
diff --git a/doc/whats-new.rst b/doc/whats-new.rst
index 0ff11048901..80ddf815bb4 100644
--- a/doc/whats-new.rst
+++ b/doc/whats-new.rst
@@ -19,7 +19,7 @@ What's New
 v0.16.1 (2020-09-20)
 ---------------------
 
-This patch release fixes an incompatability with a recent pandas change, which
+This patch release fixes an incompatibility with a recent pandas change, which
 was causing an issue indexing with a ``datetime64``. It also includes
 improvements to ``rolling``, ``to_dataframe``, ``cov`` & ``corr`` methods and
 bug fixes. Our documentation has a number of improvements, including fixing all