is a repository and portal for methods, techniques and software that are generally applicable to the satellite remote sensing community, including (but not limited to) the Earth science research community.

At the beginning of the 21st century, members of the global change research community use various methods for interpolating, mapping, and searching spatial data. However, significant changes in geospatial analysis tasks have occurred in the last ten to twenty years. In that time, the number of satellites and the number of sensors collecting data have increased dramatically, and sensor resolution and capability have increased the volume of collected data by many orders of magnitude. The sheer volume of collected data creates difficulties in subsetting and processing, even with simultaneously dramatic increases in computing capacity and storage capabilities. Reliable results from scientific algorithms can only be achieved with reliable methods for working with these great volumes of spatial data.

Many of the topics included here are the result of concepts developed in our own work with visible and passive microwave satellite data over the years. We have worked with polar researchers, and have encountered a woefully equator-centric approach in many typical mapping applications that simply ignored the polar projections or, worse, offered vague hand-waving about simply interpolating to a polar projection if that's what a user wanted. We have found that an appeal to the mathematics of map projections, wedded with an understanding of numerical computing and storage methods, provides the concepts and abstractions that are applicable to more general sets of problems as well. We encourage readers to apply these software solutions to the more general problems, and to let us know about successes and failures.

We also encourage developers of software in these same categories to let us know about your work. We would like to include pointers to your projects and software, and encourage a discussion of alternative approaches and utilities.

Interpolating: Many techniques exist for interpolating spatial data, but not all are appropriate for a given application. This section is intended to help you think about this decision, and includes example toolkits for testing your hypotheses.

Mapping: Maps are the core of geospatial analysis. Data stored in a map has a location on the Earth and a spatial relationship to other data in the map. The relationship is more than just "next to". On a map we can know exactly what direction and how far away. This is what makes maps useful and this section is designed to make using digital maps easier.

Searching: In order to be used, data must first be found. This section discusses geographic search strategies and provides links to software that can facilitate more accurate search results throughout the lifecycle of the data.