SMAP RADAR Processing and Calibration

The Soil Moisture Active Passive (SMAP) mission uses L-band radar and radiometer measurements to estimate soil moisture with 4% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km. Model sensitivities translate the soil moisture accuracy to a radar backscatter accuracy of 1 dB at 3 km resolution and a brightness temperature accuracy of 1.3 K at 40 km resolution. This presentation will describe the level 1 radar processing and calibration challenges and the choices made so far for the algorithms and software implementation. To obtain the desired high spatial resolution the level 1 radar ground processor employs synthetic aperture radar (SAR) imaging techniques. Part of the challenge of the SMAP data processing comes from doing SAR imaging on a conically scanned system with rapidly varying squint angles. The radar echo energy will be divided into range/Doppler bins using time domain processing algorithms that can easily follow the varying squint angle. For SMAP, projected range resolution is about 250 meters, while azimuth resolution varies from 400 meters to 1.2 km. Radiometric calibration of the SMAP radar means measuring, characterizing, and where necessary correcting the gain and noise contributions from every part of the system from the antenna radiation pattern all the way to the ground processing algorithms. The SMAP antenna pattern will be computed using an accurate antenna model, and then validated post-launch using homogeneous external targets such as the Amazon rain forest to look for uncorrected gain variation. Noise subtraction is applied after image processing using measurements from a noise only channel. Variations of the internal electronics are tracked by a loopback measurement which will capture most of the time and temperature variations of the transmit power and receiver gain. Long-term variations of system performance due to component aging will be tracked and corrected using stable external reference targets. Candidate targets include the Amazon rain forest and a model-corrected global ocean measurement. Radio frequency interference (RFI) signals are expected in the L-band frequency window used by the SMAP radar because many other users also operate in this band. Based on results of prior studies at JPL, SMAP L1 radar processing will use a "Slow-time thresholding" or STT algorithm to handle RFI contamination. The STT technique looks at the slow-time series associated with a given range sample, sets an appropriate threshold, and identifies any samples that rise above this threshold as RFI events. The RFI events are removed and the data are azimuth compressed without those samples. Faraday rotation affects L-band signals by rotating the polarization vector during propagation through the ionosphere. This mixes HH, VV, HV, and VH results with each other introducing another source of error. The SMAP radar is not fully polarimetric so the radar data do not provide a correction by themselves. Instead a correction must be derived from other sources. L1 radar processing will use estimates of Faraday rotation derived from externally supplied GPS-based measurements of the ionosphere total electron content (TEC). This work is supported by the SMAP project at the Jet Propulsion Laboratory, California Institute of Technology.