The following table lists the tools and services available for SMAP data.

Tool Description
HDF-EOS Tools and Information Center Provides example code for accessing and visualizing SMAP data in MATLAB, Python, IDL, and NCL.
NASA visualization tool for browsing full-resolution imagery and downloading the underlying data. To quickly access Worldview layers for a specific SMAP data set, click on "Other Access Options" from the Download Data tab on the individual data set web page.
Earthdata Search NASA's data access tool for subsetting, reprojecting, and reformatting data, including these SMAP customization options.
EASE-Grid Data Web Site Provides tools and documentation for working with EASE-Grid data.
Panoply netCDF, HDF, and GRIB Data Viewer Cross-platform application that plots geo-gridded arrays from netCDF, HDF, and GRIB data sets.
NASA LaRC Satellite Overpass Predictor An interactive tool that allows users to estimate when the SMAP satellite has passed, or will pass, over an area of the Earth. Users specify latitude and longitude or select a location on the map for which to calculate a 5-day sequence of satellite overpasses. All overpasses are returned for which the specified location falls within the 1000 km SMAP swath centered on the nadir track.  Note: Predictions beyond 15 days should not be used as they become increasingly less accurate as a function of time. In general, predictions of up to five days provide a safe margin.
About the Orbit Overpass Predictor Algorithm
The prediction algorithm models the orbit based on the known position information from the latest two-line orbital elements (TLE) records obtained from the North American Aerospace Defense Command (NORAD). The TLE records contain the position sensed during the radar scan. Normally two records are created per day, but sometimes up to three are created. The model is factoring the earth's bulge and also the weight of the continents as there are more above the equator than below. The model does not factor for the positions of continents relative to the satellite nor air resistance due to the rotational motion of the satellite in the atmosphere.
Python Notebooks  A collection of Jupyter notebooks demonstrating how to utilize Python for downloading, plotting, and visualizing SMAP data from the NSIDC DAAC.
AppEEARS The Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) offers a simple and efficient way to access and transform SMAP data. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. 
PODPAC SMAP data are accessible through the Pipeline for Observational Data Processing Analysis and Collaboration (PODPAC). PODPAC Jupyter Notebooks provide examples of accessing, subsetting, projecting, and analyzing SMAP data sets and are available on the PODPAC GitHub page. These notebooks are unsupported by NSIDC.
Subscription Subscribe to have new SMAP data automatically delivered to you as they become available at NSIDC. SMAP customization services, including subsetting, reprojection, and reformatting, can be applied to your subscription request.