small fix (#22)

Author: e-marshall

book/background/context_motivation.md

@@ -1,8 +1,6 @@
 # Context & Motivation
 
-:::{admonition} Motivation
 This book demonstrates scientific workflows using publicly-available, cloud-optimized geospatial datasets and open-source scientific software tools in order to address the need for educational resources related to new technologies and reduce barriers to entry to working with earth observation data. The tutorials in this book focus on the complexities inherent to working with n-dimensional, gridded datasets and use the core stack of software packages built on and around the Xarray data model.
-:::
 
 ### *I. Moving away from the 'download model' of scientific data analysis*
 

book/intro/open_source_setting.md

@@ -8,7 +8,6 @@ scale: 5%
 align: right
 ---
 ```
-**[Xarray](https://docs.xarray.dev/en/stable/#)** is a Python library and open-source project that introduces a data structure with named dimensions, coordinates and attributes that wrap NumPy-like arrays. This allows the co-location of raw data and metadata for complex, n-dimensional datasets in ways that streamline scientific analysis, enabling more consise, readable and intuitive workflows. 
 
 ```{figure} zarr_logo.png
 ---
@@ -17,7 +16,6 @@ align: right
 ---
 ```
 
-**[Zarr](https://zarr.readthedocs.io/en/stable/#)** is a Python library for reading and writing data stored as Zarr groups and arrays. Zarr is built for array-like data and has flexible storage backends making it suitable data stored in local file systems as well as cloud object storage. 
 
 ```{figure} logos/pangeo_logo.png
 ---

book/tutorial2/nbs/5_comparing_s1_rtc_datasets.ipynb

@@ -519,7 +519,7 @@
    "source": [
     "fig, axs = plt.subplots(ncols=3, nrows=2, figsize=(12,5), layout='constrained')\n",
     "\n",
-    "backscatter_kwargs = {'cmap':'Greys_r', 'cbar_kwargs':{'label':'backscatter'}}\n",
+    "backscatter_kwargs = {'cmap':'Greys_r', 'cbar_kwargs':{'label':'dB'}}\n",
     "ls_kwargs = {'cmap':'tab20b', 'vmin':0, 'vmax':32, 'cbar_kwargs':{'label':'layover-shadow map'}}\n",
     "\n",
     "s1_tools.power_to_db(pc_subset.isel(time=1).sel(band='vv')).plot(ax=axs[0][0], **backscatter_kwargs)\n",
@@ -729,7 +729,7 @@
     "fig.suptitle(f'{date_actual}: Comparing backscatter images ' + r\"$\\bf{after}$\" + ' masking')\n",
     "s1_tools.power_to_db(asf_da.sel(band='vv').sel(time=date_target, method='nearest')).plot(cmap=plt.cm.Greys_r, ax=ax[0])\n",
     "ax[0].set_title('ASF')\n",
-    "s1_tools.power_to_db(pc_mask.sel(band='vv').sel(time=date_target, method='nearest')).plot(cmap=plt.cm.Greys_r,ax=ax[1], cbar_kwargs={'label':'backscatter'})\n",
+    "s1_tools.power_to_db(pc_mask.sel(band='vv').sel(time=date_target, method='nearest')).plot(cmap=plt.cm.Greys_r,ax=ax[1], cbar_kwargs={'label':'dB'})\n",
     "ax[1].set_title('PC')\n",
     "\n",
     "ax[0].set_ylabel(None)\n",