Use Cases for Combining Web Services with ArcPython Tools for Enabling Quality Control of Land Remote Sensing Data Products.

Three major obstacles facing big Earth data users include data storage, management, and analysis. As the amount of satellite remote sensing data increases, so does the need for better data storage and management strategies to exploit the plethora of data now available. Standard GIS tools can help big Earth data users whom interact with and analyze increasingly large and diverse datasets. In this presentation we highlight how NASA's Land Processes Distributed Active Archive Center (LP DAAC) is tackling these big Earth data challenges. We provide a real life use case example to describe three tools and services provided by the LP DAAC to more efficiently exploit big Earth data in a GIS environment. First, we describe the Open-source Project for a Network Data Access Protocol (OPeNDAP), which calls to specific data, minimizing the amount of data that a user downloads and improves the efficiency of data downloading and processing. Next, we cover the LP DAAC's Application for Extracting and Exploring Analysis Ready Samples (AppEEARS), a web application interface for extracting and analyzing land remote sensing data. From there, we review an ArcPython toolbox that was developed to provide quality control services to land remote sensing data products. Locating and extracting specific subsets of larger big Earth datasets improves data storage and management efficiency for the end user, and quality control services provides a straightforward interpretation of big Earth data. These tools and services are beneficial to the GIS user community in terms of standardizing workflows and improving data storage, management, and analysis tactics.