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diff --git a/_sources/PC_RTC.ipynb b/_sources/PC_RTC.ipynb
diff --git a/_sources/asf_inspect.ipynb b/_sources/asf_inspect.ipynb
diff --git a/_sources/asf_local_vrt.ipynb b/_sources/asf_local_vrt.ipynb
diff --git a/_sources/ch1_root.md b/_sources/ch1_root.md
@@ -4,10 +4,10 @@ The first chapter of this tutorial will demonstrate reading in and organizing Se
 
 ## Data processed and downloaded from Alaska Satellite Facility
 
-The first [notebook](asf_local_vrt.ipynb) demonstrates working with data that was processed by Alaska Satellite Facility through their [Hyp3 On-Demand service](https://hyp3-docs.asf.alaska.edu/v2-transition/) using HyP-3 SDK to submit jobs programmatically. The processed data is then downloaded locally. This notebook shows one approach for working with that data once its been downloaded locally.
+The first [notebook](asf_local_vrt.ipynb) (GDAL VRT approach) demonstrates working with data that was processed by Alaska Satellite Facility through their [Hyp3 On-Demand service](https://hyp3-docs.asf.alaska.edu/v2-transition/). The processed data is then downloaded locally. This notebook shows one approach for working with that data once downloaded locally.
 
-The second [notebook](asf_inspect.ipynb) shows preliminary dataset inspection of the ASF dataset once it has been read-in and organized.
+The second [notebook](asf_inspect.ipynb) (ASF-processed RTC data inspection) shows preliminary dataset inspection of the ASF dataset once it has been read in and organized.
 
 ## Data processed and accessed from Microsoft Planetary Computer
 
-This [notebook](PC_RTC.ipynb) demonstrates accessing data from Microsoft Planetary Computer's catalog. Microsoft Planetary Computer performs RTC processing of Sentinel-1 imagery similarly to ASF. It is then made available as cloud-optimized geotiffs and hosted on Microsoft Planetary Computer. This notebook demonstrates using STAC tools such as `pystac` and `stackstac` to access the cloud-hosted data locally. Microsoft Planetary Computer also hosts a jupyter hub server which you could access and use instead of doing so locally. Microsoft Planetary Computer requires a subscription (currently free). You can find out more about getting access [here](https://planetarycomputer.developer.azure-api.net/).
+This [notebook](PC_RTC.ipynb) (Microsoft Planetary Computer Sentinel-1 RTC Imagery) demonstrates accessing data from Microsoft Planetary Computer's catalog. Microsoft Planetary Computer performs RTC processing of Sentinel-1 imagery similarly to ASF. It is then made available as cloud-optimized GeoTIFFs and hosted on Microsoft Planetary Computer. This notebook demonstrates using STAC tools such as `pystac` and `stackstac` to access the cloud-hosted data locally. Microsoft Planetary Computer also hosts a jupyter hub server, which you could use instead of working with the data locally. Microsoft Planetary Computer requires a subscription (which is currently free). You can find out more about getting access [here](https://planetarycomputer.developer.azure-api.net/).
diff --git a/_sources/dataset_comparison.ipynb b/_sources/dataset_comparison.ipynb
diff --git a/_sources/intro.md b/_sources/intro.md
@@ -1,21 +1,20 @@
 # Introduction 
 
-This tutorial will demonstrate various ways to access, process and work with Sentinel-1 Synthetic Aperture Radar (SAR) Radiometrically Terrain Corrected (RTC) imagery using the python package and open source project `xarray` as well as a number of other open source software packages. These notebooks are designed to show examples of data access, manipulation, processing and exploratory analytical workflows with complex datasets as well as to demonstrate common functionality for working with multi-dimensional gridded data using [`xarray`](https://docs.xarray.dev/en/stable/). 
+This tutorial will demonstrate various ways to access, process, and work with Sentinel-1 Synthetic Aperture Radar (SAR) Radiometrically Terrain Corrected (RTC) imagery using the python package and open source project [xarray](https://docs.xarray.dev/en/stable/#), and a number of other open source software packages. These notebooks are designed to show examples of data access, manipulation, processing, and exploratory analytical workflows with complex datasets as well as to demonstrate common functionality for working with multi-dimensional gridded data using xarray.
 
 # Overview
 
 This tutorial contains a number of jupyter notebooks that focus on working with Sentinel-1 imagery that has already undergone Radiometric Terrain Correction (RTC). Because SAR imagery is collected from a side-looking sensor, it can contain distortions related to the viewing geometry of the sensor and the surface topography of the area being imaged. 
 
-SAR data is collected in slant range, which is the viewing geometry of the side-looking sensor and has two dimensions: range and azimuth. These are the along-track and across-track directions of the imaged swath. As data is transformed from radar coordinates (slant range) to geocoded coordinates, the spaces that are represented by individual pixels in the two coordinate systems do not always align and distortions can arise due to certain viewing angle geometries and surface topography features. In addition, radiometric distortion can arise due to scattering responses from multiple scattering features within a single pixel. Radiometric terrain correction is a processing step that accounts for these distortions and the transformation from radar to geocoded coordinates that prepares SAR data for analysis. For a much more detailed explanation of this check out the Alaska Satellite Facility (ASF) [product guide for RTC imagery](https://hyp3-docs.asf.alaska.edu/guides/rtc_product_guide/). Chapter 2 of the [SAR Handbook](https://gis1.servirglobal.net/TrainingMaterials/SAR/Chp2Content.pdf) also has very useful descriptions of these concepts. 
+SAR data is collected in slant range, which is the viewing geometry of the side-looking sensor and has two dimensions: range and azimuth. These are the along-track and across-track directions of the imaged swath. As data is transformed from radar coordinates (slant range) to geocoded coordinates, the spaces represented by individual pixels in the two coordinate systems do not always align, and distortions can arise due to certain viewing angle geometries and surface topography features. In addition, radiometric distortion can arise due to scattering responses from multiple scattering features within a single pixel. Radiometric terrain correction is a processing step that accounts for these distortions and the transformation from radar to geocoded coordinates that prepares SAR data for analysis. For a much more detailed explanation of this, check out the Alaska Satellite Facility (ASF) [product guide for RTC imagery](https://hyp3-docs.asf.alaska.edu/guides/rtc_product_guide/). Chapter 2 of the [SAR Handbook](https://gis1.servirglobal.net/TrainingMaterials/SAR/Chp2Content.pdf) also has very useful descriptions of these concepts. 
 
-There are multiple algorithms that perform radiometric terrain correction and it is important to understand the components of whichever dataset you use as well as their relative benefits and tradeoffs. This book will demonstrate accessing and working with two different (but similar) datasets of Sentinel-1 RTC imagery. Processing of SAR imagery can be very computationally intensive, so we focus on datasets that leverage cloud-computing resources, including both cloud-processed and cloud-hosted data as well as data that is processed in the cloud and then downloaded locally. 
+Multiple algorithms perform radiometric terrain correction, and it is important to understand the components of whichever dataset you use and their relative benefits and tradeoffs. This book will demonstrate working with two different (but similar) datasets of Sentinel-1 RTC imagery. Processing of SAR imagery can be very computationally intensive, so we focus on datasets that leverage cloud-computing resources, including both cloud-processed and cloud-hosted data, as well as data that is processed in the cloud and then downloaded locally. 
 
 # Learning objectives
 
-This tutorial demonstrates accessing and working with different Sentinel-1 SAR RTC datasets. The learning goals include **domain specific steps related to working with synthetic aperture radar data** as well as specific *python and xarray techniques*. 
+This tutorial demonstrates accessing and working with different Sentinel-1 SAR RTC datasets. The learning goals include **domain-specific steps related to working with synthetic aperture radar data** and specific *python and xarray techniques*. 
 
 - **Find and access data from Microsoft Planetary Computer** *Use `pystac_client` to navigate STAC-oriented data and `stackstac` to read data as xarray objects*
-- **Programmatically submit RTC processing jobs to be executed by Alaska Satellite Facility On-Demand Processing server using the HyP3 SDK package** *Organize, query and access processed data on your local machine*
 - **Compare two similar datasets and evaluate differences, suitability for certain use cases** *read in data as xarray objects, organize and perform comparison*
 - **Time series analysis of SAR RTC imagery** *Use xarray tools such as grouping, resampling and reductions as well as rioxarray functionality to organize and analyze SAR backscatter time series data*
 
@@ -28,4 +27,4 @@ This tutorial demonstrates accessing and working with different Sentinel-1 SAR R
 
 ## Chapter 2: Dataset comparison and preliminary analysis
 
-[Chapter 2](ch2_root.md) makes use of the datasets prepared in chapter 1. The first notebook demonstrates a comparison the RTC imagery processed by ASF and by Microsoft Planetary Computer. The second contains an example of preliminary time series analysis of backscatter variability over proglacial lakes in the Himalaya. 
+[Chapter 2](ch2_root.md) makes use of the datasets prepared in chapter 1. The first notebook demonstrates a comparison of the RTC imagery processed by ASF and by Microsoft Planetary Computer. The second contains an example of preliminary time series analysis of backscatter variability over proglacial lakes in the Himalayas. 
diff --git a/_sources/software.ipynb b/_sources/software.ipynb
@@ -113,8 +113,8 @@
     "## Data\n",
     "\n",
     "This tutorial works with two different datasets of Sentinel-1 RTC imagery. \n",
-    "1. The first is processed by Alaska Satellite Facility (ASF) using their On-Demand platform and downloaded locally as directories for each scene containing geotiff files and associated metadata. Access to ASF data and SAR data processing is available to anyone with NASA Earthdata login credentials which you can learn more about [here](https://www.earthdata.nasa.gov/). The ASF-processed data used in this tutorial is available [here](https://zenodo.org/record/7236413#.Y1rNi37MJ-0).\n",
-    "2. The second is processed and hosted by Microsoft Planetary Computer as cloud-optimized geotiffs (COGs). "
+    "1. The first is processed by Alaska Satellite Facility (ASF) using their On-Demand platform and downloaded locally as directories for each scene containing GeoTIFF files and associated metadata. Access to ASF data and SAR data processing is available to anyone with NASA Earthdata login credentials, which you can learn more about [here](https://www.earthdata.nasa.gov/). The ASF-processed data used in this tutorial is available [here](https://zenodo.org/record/7236413#.Y1rNi37MJ-0).\n",
+    "2. The second is processed and hosted by Microsoft Planetary Computer as cloud-optimized GeoTIFF (COGs). "
    ]
   },
   {
@@ -132,6 +132,18 @@
    "language": "python",
    "name": "python3"
   },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  },
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