The quality of a data set is very important to its user. Not knowing about data quality can lead to misleading interpretations and erroneous results. However, readily accessible data quality information enables the user to assess the limitations of a data set and to interpret the data accordingly.
Several of NSIDC’s data sets include quality information, which can range from flags, masks, or fields in the data or metadata to separate data quality summary files. Additional quality information is also often included in data set documentation and linked peer-reviewed literature. Thus, it can be a laborious process for data users to locate and interpret all of the quality information for a particular data set.
Fortunately, users of AMSR-E data sets can access all of this information in one convenient location. The new AMSR-E Data Quality Web pages provide information on the quality flags and files as well as data uncertainty reports provided by AMSR-E Principal Investigators. The data uncertainty reports are separated by parameter, such as sea ice and soil moisture, and provide the following:
- A synopsis on sources of uncertainty
- Best estimates of data uncertainty under optimal conditions for each measurement, including a confidence interval where possible
- A description of how to interpret quality flags to understand the conditions under which uncertainty may be greater
If you are an AMSR-E data user, check out this one-stop shop for data quality information.
International flags at the South Pole, pictured here, are symbolic of the Antarctic Treaty, just as data quality flags are symbolic of the quality of a data set. Credit: Ted Scambos, NSIDC
Soil moisture is a key variable in understanding land surface hydrology and in modeling ecosystems, weather, and climate. Among NSIDC’s most popular data sets is the “AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, & QC EASE-Grids” (AE_Land3) data set. This data set is distributed in HDF-EOS format and one of the biggest hurdles encountered by many users is simply learning how to display the data. Here are some tips on how to get started.
The HDF Group provides sample code for access and visualization of HDF data into IDL, MATLAB, and NCL. Access to the sample code for AMSR-E HDF data is provided on the HDF Group’s HDF-EOS Comprehensive Examples Web page.
If you are more familiar with GeoTIFF format, you may choose to utilize the HDF-EOS to GeoTIFF (HEG) Tool to convert AMSR-E Daily Soil Moisture HDF data into GeoTIFF format. This tool also allows you to subset the data with spatial or parameter constraints, as well as change the output projection. These HEG Tool services are also available as an option when ordering these data through the Reverb search and order interface. Instructions on how to use these data services in Reverb can be found in this Online Support article.
If you are interested in importing the data into ArcGIS, you can either use the GeoTIFF files generated by the HEG Tool or downloaded from Reverb, or you can perform a few steps to import the native HDF-EOS files into ArcMap. Using the ArcToolbox, you can easily extract a data field from an HDF file and save it in a different raster format that you are more familiar with. Instructions detailing how to do this can be found in this Online Support article.
AMSR-E L3 Daily Soil Moisture plot, 07/01/2002.
New services that can be applied to AMSR-E and MODIS data have recently been added to the Reverb search and order tool. NASA Reverb|ECHO and NSIDC recently implemented these services in preparation for the decommissioning of the Data Pool search and order interface. Service options include reformatting the data from HDF-EOS to GeoTIFF, reprojection, and spatial or parameter subsetting. We created a tutorial to help data users access these data services.
The Data Pool search and order interface will be decommissioned on 28 August 2013. If you used the Data Pool search and order interface to reformat, reproject, or subset data, please use the new data services in Reverb. The Data Pool FTP site that provides direct access to data granules will remain available after the Data Pool search and order interface is decommissioned.
Hierarchical Data Format (HDF) supports a variety of data types and allows for the transfer and manipulation of scientific data across diverse operating systems and computer platforms. It was developed by the HDF Group at the University of Illinois and is the standard data format for all NASA Earth Observing System (EOS) data.
A variety of HDF data are distributed at NSIDC, including data from the AMSR-E, MODIS, and GLAS sensors as well as the NISE data set.
Despite the versatility of HDF, some data users have had difficulty in reading and displaying HDF data and often ask if we have sample code for reading HDF data in programs such as IDL and MATLAB.
Fortunately, the HDF Group provides sample code for access and visualization of HDF data into IDL, MATLAB, and NCL. Access to the sample code for NSIDC HDF data is provided on the HDF Group’s HDF-EOS Comprehensive Examples Web page.
Also note that this information can be found in our Online Support under the AMSR-E, MODIS, GLAS, and NISE forums.