## 7 Access and Use of Polar Remote Sensing Data
However, unlike the other two, MODIS has a wide range of processing levels and products, all of which are searchable and freely downloadable through a variety of platforms. MERIS requires a (simple) application process to ESA, and many VIIRS products are still in development and only available through NOAA, not a traditional source of remote sensing data for many users. It is hardly coincidental that in polar science, MODIS is by far the most widely used of the three, and it strongly suggests that NASA's is the model of data processing and access that other agencies should seek to follow and indeed to improve upon. Another case study for data access is that of NASA's Operation IceBridge. These airborne missions to study ice masses in the Arctic and Antarctic are meant to bridge between ICESat satellites (ICESat data are available 2003-2009, and ICESat-2 is scheduled for launch in 2017). This means that while surface elevation is a key parameter in IceBridge studies, many other sensors have also been included on the various aircraft, including (but not exhaustively) snow radar, multiple LiDAR instruments, radar altimeters, radio echo sounders, a magnetometer and a gravimeter. All IceBridge data, once they are processed, are available via NSIDC (see Table 2). Although NSIDC's expertise lies in data management and distribution, IceBridge instruments are PI-driven, which means there can be significant differences in data descriptions and a lag time between collection and availability. There is no overarching mechanism and standard processing in place for these data, as there is, for example, behind the Landsat archive. Still, because they are linked spatially, at least IceBridge data are all in one place and searchable in a map interface. This is a good example of diverse types of data being able to be used in a suite. Taking a step back in time; originally, all remote sensing data were expensive. In the last few decades, the field has seen a huge change. Increasingly, the challenge is no longer just getting any image; it is getting the right image(s) and being able to process them through to a meaningful result, often in an expedient manner. Although there are still restrictions on permitted use and sharing (especially with data from commercial partnerships), the bigger stumbling blocks are often file transfer, processing requirements and access time. The sheer volume of free data available requires expertise from the remote sensing researcher to select the right data and process it with the right steps, whether registration, atmospheric correction, orthorectification or some other necessary step. The cryospheric community is quite rapidly churning out high quality science from large masses of data. Increasingly, it is incumbent upon the remote sensing/cryospheric scientist to also be a _de facto_ software engineer in order to do the best science possible, a variable that comes down to both training and collaboration; both important, but not the focus of this paper. Looking both into the future and historically, a developing trend is not just processing large data sets of recently acquired data; old data enabling new science through data rescue and new ancillary data are increasingly pushing forward our understanding of the poles and the Earth's cryosphere. As discussed in the historical aerial photography section, new calibrations and tie points are allowing researchers to make quantitative use of old images. Data rescue projects are putting old data in useable forms again, for example to extend our knowledge of Arctic and Antarctic sea ice back to 1964 [174]. This latter study shows that the difficulty in pushing back remote sensing time series is often finding the data to begin with. Therefore, how does one discover and use the best data? It is our hope that this paper provides a starting point and will be sufficient for many. However, the real key to data discovery and use lies in one word: metadata.