Open Source GIS Connectors to the NASA GES DISC Satellite Data
Tuesday, 16 December 2014
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) houses a suite of satellite-derived GIS data including high spatiotemporal resolution precipitation, air quality, and modeled land surface parameter data. The data are extremely useful to various GIS research and applications at regional, continental, and global scales, as evidenced by the growing GIS user requests to the data. On the other hand, we also found that some GIS users, especially those from the ArcGIS community, having difficulties in obtaining, importing, and using our data, primarily due to the unfamiliarity of the users with our products and GIS software’s lack of capabilities in dealing with the predominately raster form data in various sometimes very complicated formats. In this presentation, we introduce a set of open source ArcGIS data connectors that significantly simplify the access and use of our data in ArcGIS. With the connectors, users do not need to know the data access URLs, the access protocols or syntaxes, and data formats. Nor do they need to browse through a long list of variables that are often embedded into one single science data file and whose names may sometimes be confusing to those not familiar with the file (such as variable CH4_VMR_D for “CH4 Volume mixing ratio from the descending orbit” and variable EVPsfc for “Total Evapotranspiration”). The connectors will expose most GIS-related variables to the users with easy to understand names. User can simply define the spatiotemporal range of their study, select interested parameter(s), and have the needed data be downloaded, imported, and displayed in ArcGIS. The connectors are python text files and there is no installation process. They can be placed at any user directory and be started by simply clicking on it. In the presentation, we’ll also demonstrate how to use the tools to load GES DISC time series air quality data with a few clicks and how such data depict the spatial and temporal patterns of air quality in different parts of the world during the past decade.