Another nit (#56)

* add links

* fix formatting

* fix links

Author: e-marshall

book/conclusion/summary.md

@@ -29,12 +29,12 @@ We saw many examples of situations where it would have been very difficult to ac
 ## Data visualization
 We used a number of visualization tools in these tutorials, each of which are appropriate for different situations and use-cases. These include interactive and static visualizations and plotting tools optimized for n-d array and vector datasets. 
 
-- [ITS_LIVE, nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/3_combining_raster_vector_data.html#crop-vector-data-to-spatial-extent-of-raster-data), [Adding basemaps to plots of raster and vector data, ITS_LIVE nb4](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/4_exploratory_data_analysis_single.html#load-raster-data-and-visualize-with-vector-data), [Interactive visualization of vector data cubes with Xvec and GeoPandas](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/5_exploratory_data_analysis_group.html#visualize-velocity-data), [Interactive visualization of 2-d data with Xarray and holoviz, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#interactive-visualization-of-layover-shadow-maps), [Using xr.FacetGrid for data inspection and cleaning](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#d-duplicate-time-steps), [Different ways of plotting 2-d data side-by-side, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#f-data-visualization), [Interactive visualization of time series data with Xarray and holoviz](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#backscatter-time-series), and [Visual dataset comparison](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/5_comparing_s1_rtc_datasets.html#d-visualize-comparisons).
+*See: [Plotting raster and vector data](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/3_combining_raster_vector_data.html#crop-vector-data-to-spatial-extent-of-raster-data), [Adding basemaps to plots of raster and vector data](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/4_exploratory_data_analysis_single.html#load-raster-data-and-visualize-with-vector-data), [Interactive visualization of vector data cubes with Xvec and GeoPandas](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/itslive/nbs/5_exploratory_data_analysis_group.html#visualize-velocity-data), [Interactive visualization of 2-d data with Xarray and holoviz, Sentinel-1 nb3](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#interactive-visualization-of-layover-shadow-maps), [Using xr.FacetGrid for data inspection and cleaning](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#d-duplicate-time-steps), [Different ways of plotting 2-d data side-by-side](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#f-data-visualization), [Interactive visualization of time series data with Xarray and holoviz](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/3_asf_exploratory_analysis.html#backscatter-time-series), and [Visual dataset comparison](https://e-marshall.github.io/cloud-open-source-geospatial-datacube-workflows/sentinel1/nbs/5_comparing_s1_rtc_datasets.html#d-visualize-comparisons).*
 
 ## Making analysis-ready data cubes
 Several notebooks focused on the tasks of organizing data cubes so that they were appropriate representations of physical observables and attaching relevant metadata to relevant variables and along appropriate dimensions.
 
-This process, often called 'data tidying,' refers to the steps required to prepare a dataset for analysis. Depending on your data and how you want to use it, it can become quite involved. Data tidying requires a detailed understanding of both the data that you want to analyze and the 'data model' of the tools you are using to work with the data. The following page contains more discussion on tidying steps for n-dimensional array datasets.
+This process, often called 'data tidying,' refers to the steps required to prepare a dataset for analysis. Depending on your data and how you want to use it, it can become quite involved. Data tidying requires a detailed understanding of both the data that you want to analyze and the 'data model' of the tools you are using to work with the data. The following pages contains more discussion on tidying steps for n-dimensional array datasets.
 
 
 *See: [Inspection and exploratory analysis of ice velocity data](../itslive/nbs/4_exploratory_data_analysis_single.ipynb), [Wrangle metadata of a Sentinel-1 RTC dataset](../sentinel1/nbs/2_wrangle_metadata.ipynb) and [Comparing two datasets](../sentinel1/nbs/5_comparing_s1_rtc_datasets.ipynb).*

book/intro/1_getting_started.md

@@ -50,7 +50,7 @@ Each tutorial focuses on a different type of remote sensing dataset and demonstr
 This tutorial focuses on [ITS_LIVE](https://its-live.jpl.nasa.gov/), a NASA MEASURES project and publicly accessible dataset stored in an AWS S3 repo as Zarr data cubes. 
 
 #### Part 4: {{part3_title}} [$\tiny \nearrow$](../sentinel1/s1_intro.md)
-This tutorial focuses on another satellite dataset: [Sentinel-1](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1) Radiometric Terrain Corrected imagery. Sentinel-1 is a satellite-based imaging radar. More specifically, it is a synthetic aperture radar (SAR). SAR sensors look to the side rather than straight-down like conventional optical and infrared satellite sensors. This side-looking geometry causes geometric distortions that must be addressed prior to analysis. SAR data undergoes different types of processing for different scientific applications. Part 2 demonstrates how to access this data from two publicly available online repositories: Alaska Satellite Facility and Microsoft Planetary Computer. These notebooks demonstrate the different ways to read this data and prepare it for analysis, as well as an initial comparison of the two datasets. 
+This tutorial focuses on another satellite dataset: [Sentinel-1](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1) Radiometric Terrain Corrected imagery. Sentinel-1 is a satellite-based imaging radar. More specifically, it is a synthetic aperture radar (SAR). SAR sensors look to the side rather than straight-down like conventional optical and infrared satellite sensors. This side-looking geometry causes geometric distortions that must be addressed prior to analysis. SAR data undergoes different types of processing for different scientific applications. Part 4 demonstrates how to access this data from two publicly available online repositories: Alaska Satellite Facility and Microsoft Planetary Computer. These notebooks demonstrate the different ways to read this data and prepare it for analysis, as well as an initial comparison of the two datasets. 
 
 ### {{part4_title}} [$\tiny \nearrow$](../conclusion/wrapping_up.md)
 

book/intro/2_learning_objectives.md

@@ -3,20 +3,20 @@ By the end of this book, you will have familiarity with and have seen examples d
 
 ## *Data cubes and array data structures*
 - Understanding fundamental structures of data cubes and how to organize earth observation datasets within this data model
-- Using Xarray label-based indexing and selection to manipulate and organize multi-dimensional data objects
+- Using [Xarray](https://xarray.dev/) label-based indexing and selection to manipulate and organize multi-dimensional data objects
 - Performing dimensional computations and visualizations with Xarray
-- Visualizing and querying vector datasets with GeoPandas
-- Using Xvec to create and use vector datasets, combining the functionality of Xarray and GeoPandas 
+- Visualizing and querying vector datasets with [GeoPandas](https://geopandas.org/en/stable/)
+- Using [Xvec](https://xvec.readthedocs.io/en/stable/) to create and use vector datasets, combining the functionality of Xarray and GeoPandas 
 - Aligning and comparing two objects with different spatial resolutions
 
 ## *Working with large datasets*
-- How to read very large local data into memory using GDAL VRT 
+- How to read very large local data into memory using GDAL virtual format files ([VRT](https://gdal.org/en/stable/drivers/raster/vrt.html)) 
 - Parallelizing local data reads with Xarray `open_mfdataset()`
-- Using dask to parallelize workflows in order to work with larger-than-memory data
+- Using [Dask](https://www.dask.org/) to parallelize workflows in order to work with larger-than-memory data
 
 ## *Reading and writing data*
 - Reading raster data from cloud object storage
-- Handling geospatial metadata and geospatial operations involving array data using Xarray and RioXarray
-- Understanding STAC metadata specification and how to use it to query and read data from cloud object storage
-- Reading and writing Zarr data cubes with Xarray
+- Handling geospatial metadata and geospatial operations involving array data using Xarray and [RioXarray](https://corteva.github.io/rioxarray/stable/index.html)
+- Understanding [STAC](https://stacspec.org/en) metadata specification and how to use it to query and read data from cloud object storage
+- Reading and writing [Zarr](https://zarr.readthedocs.io/en/stable/) data cubes with Xarray
 

book/intro/3_open_source_setting.md

@@ -2,7 +2,7 @@
 ## *Xarray, Zarr, and the Pangeo software stack*
 
 
-**[Pangeo](https://www.pangeo.io/)** is a community scientists, developers, and practitioners dedicated to promoting open, reproducible, and scalable science. Members of the Pangeo community develop and contribute to open-source software libraries within the geosciences and across scientific domains, including Xarray, Dask, Zarr, and Jupyter.
+**[Pangeo](https://www.pangeo.io/)** is a community scientists, developers, and practitioners dedicated to promoting open, reproducible, and scalable science. Members of the Pangeo community develop and contribute to open-source software libraries within the geosciences and across scientific domains, including [Xarray](https://xarray.dev/), [Dask](https://www.dask.org/), [Zarr](https://zarr.readthedocs.io/en/stable/), and [Jupyter](https://jupyter.org/).
 
 In addition to these software libraries, the Pangeo community emphasizes the development of educational resources (see [Project Pythia](https://foundations.projectpythia.org/landing-page.html)), regular community [meetings](https://www.pangeo.io/meetings) and [showcase talks](https://www.pangeo.io/showcase), and working groups on specialized topics.