move back to one use case and add ground truth data to repo

Author: jreiberkyle

jupyter-notebooks/analysis-ready-data/README.md

@@ -1,11 +1,10 @@
 # Analysis Ready Data
 
-The tutorials in this directory cover how to use the orders api to create Analysis Ready Data. These tutorials cover two use cases:
-1. *As a software engineer at an ag-tech company, I'd like to be able to order Planet imagery programmatically in a way that enables the data scientist at my organization to create time-series algorithms (e.g. monitoring ndvi curves over time) without further data cleaning and processing.*
+The tutorials in this directory cover how to use the data and orders APIs to create Analysis Ready Data. These tutorials provide an introduction to concepts necessary in creating Analysis Ready Data and an introduction to the data and orders APIs, best practices for utilizing the data and orders APIs to create Analysis Ready Data, and a walkthrough of a real-world use case. 
 
-2. *As an agriculture customer, I'd like to create an imagery pipeline that provides for trialing different fungicides by ordering Planet imagery within a single field (AOI), cutting the imagery into multiple field blocks (grid within AOI), and comparing values across blocks in two ways. First, comparison is performed by extracting median, mean, variance NDVI values for each day (using random point sampling) in each block. Second, comparison is performed by random point selection in each block.*
+The use case covered is:
+*As an agriculture customer, I'd like to create an imagery pipeline that provides for trialing different fungicides by ordering Planet imagery within a single field (AOI), cutting the imagery into multiple field blocks (grid within AOI), and comparing values across blocks in two ways. First, comparison is performed by extracting median, mean, variance NDVI values for each day (using random point sampling) in each block. Second, comparison is performed by random point selection in each block.*
 
 The notebooks are meant to be worked through in sequence:
 1. [Analysis Ready Data Tutorial Part 1: Introduction and Best Practices](ard_1_intro_and_best_practices.ipynb), provides an introduction to Analysis Ready Data and the Orders and Data APIs and provides best practices for using the APIs to prepare Analysis Ready Data.
-1. [Analysis Ready Data Tutorial Part 2: Use Case 1](ard_2_use_case_1.ipynb) runs through the first use case, preparing an NDVI time stack. This part includes a second notebook, [Analysis Ready Data Tutorial Part 2: Use Case 1 - Visualize Images](ard_2_use_case_1_visualize_images.ipynb), for visualizing the NDVI imagery.
-1. [Analysis Ready Data Tutorial Part 3: Use Case 2](ard_3_use_case_2.ipynb) runs through the second use case, preparing a gridded NDVI time stack of a field and performing comparisons across the grid blocks.
+1. [Analysis Ready Data Tutorial Part 2: Use Case 1](ard_2_use_case_1.ipynb) runs through the use case, preparing an NDVI time stack. This part includes a second notebook, [Analysis Ready Data Tutorial Part 2: Use Case 1 - Visualize Images](ard_2_use_case_1_visualize_images.ipynb), for visualizing the NDVI imagery.

jupyter-notebooks/analysis-ready-data/ard_1_intro_and_best_practices.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "Time-series analysis (e.g. change detection and trend detection) is a powerful application of satellite imagery. However, a great deal of processing is required to prepare imagery for analysis. Analysis Ready Data (ARD), preprocessed time-series stacks of overhead imagery, allow for time-series analysis without any additional processing of the imagery. See [Analysis Data Defined](https://medium.com/planet-stories/analysis-ready-data-defined-5694f6f48815) for an excellent introduction and discussion on ARD.\n",
     "\n",
-    "This tutorial shows how [Planet APIs](https://developers.planet.com/docs/apis/) can simplify production of ARD by demonstrating best practices and then by walking through a real world use case. This tutorial is targeted to users who have little to no geospatial knowledge but have experience working with APIs. The goal of this tutorial is to teach the user the how and whys of using the Data and Orders APIs to create and interpret ARD for both use cases. This first part of the totorial focuses on best practices. The following parts will focus on two real-world use cases.\n",
+    "This tutorial shows how [Planet APIs](https://developers.planet.com/docs/apis/) can simplify production of ARD by demonstrating best practices and then by walking through a real world use case. This tutorial is targeted to users who have little to no geospatial knowledge but have experience working with APIs. The goal of this tutorial is to teach the user the how and whys of using the Data and Orders APIs to create and interpret ARD for both use cases. This first part of the totorial focuses on best practices. The following part will focus on a real-world use case.\n",
     "\n",
     "\n",
     "## APIs\n",