By Mahta Moghaddam et al.
Journal of Spacecraft and Rockets
2023
In early satellite mission design, requirements are not yet fixed, cost is sometimes negotiable, and designs are relatively unconstrained. During this period of design freedom, multi-objective optimization can provide a useful lens into the design space by showing theoretical performance limits and illuminating design tradeoffs. This work optimizes a radar constellation for a potential soil moisture mission. Several different optimization cases with different variables are considered and contrasted. The optimization of the instrument and constellation parameters is considered jointly and separately to better understand the effect of coupling on the optimization performance. A science-driven optimization based on soil moisture retrieval error is compared with a performance-metric-driven optimization. Pareto analysis and association rule mining are performed on the generated designs to provide insight into driving features. Design recommendations are made for several cost caps. Results show that optimization that considers the instrument and constellation design together find superior revisit metrics than treating instrument and constellation separately. The use of the science value metric as an optimization objective shows that while cost may always be increased to improve instrument and constellation performance, the difference in science value may be negligible. These findings can inform tradespace exploration studies for similar problems.
By Mahta Moghaddam et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2023
Surface soil organic carbon (SOC) content is among the first-order controls on the rate and extent of Arctic permafrost thaw. There is a large discrepancy in current SOC estimates in Arctic tundra, where sparse measurements are unable to capture SOC complexity over the vast tundra region. Synthetic aperture radar (SAR) data are sensitive to surface vegetation, roughness, and moisture conditions, and may provide useful information on surface SOC properties. Here, we investigated the potential of multitemporal Sentinel-1 C-band SAR data for regional SOC mapping in the Arctic tundra through principal component analysis (PCA). Multiple in situ SOC datasets in the Alaska North Slope were assembled to generate a consistent surface (0–10 cm) SOC and bulk density dataset ( n = 97). The radar VV backscatter shows a strong correlation with surface SOC, but the correlation varies greatly with surface snow, moisture, and freeze/thaw conditions. However, the first principal component (PC1) of radar backscatter time series from different years shows spatial consistency representing dominant and persistent surface backscatter behavior. The PC1 also shows a strong linear correlation with surface SOC concentration ( R = 0.65, p <0.01), and an exponential relationship with bulk density ( R = −0.65, p <0.01). The resulting predicted SOC maps show much lower soil bulk density and higher SOC concentration in the southern shrub tundra area than in the northern coastal region, consistent with in situ data. Our analysis shows that it is possible to separate the effects of different factors on the radar backscatter response using PCA and multitemporal SAR data, which may lead to more effective satellite-based methods for Arctic SOC mapping.
By Mahta Moghaddam et al.
We describe a method to calibrate a Software-Defined Radar (SDRadar) system mounted on an uncrewed aerial vehicle (UAV) with an ultra-wideband (UWB) waveform operated in the near-field region. Radar calibration is a prerequisite for using the full capabilities of the radar system to retrieve geophysical parameters accurately. We introduce a framework and process to calibrate the SDRadar with the UWB waveform in the 675 MHz–3 GHz range in the near-field region. Furthermore, we present the framework for computing the near-field radar cross section (RCS) of an external passive calibration target, a trihedral corner reflector (CR), using HFSS software and with consideration for specific antennas. The calibration performance was evaluated with various distances between the calibration target and radar antennas. The necessity for the knowledge of the near-field RCS to calibrate SDRadar was demonstrated, which sets this work apart from the standard method of using a trihedral CR for backscatter radar calibration. We were able to achieve approximately 0.5 dB accuracy when calibrating the SDRadar in the anechoic chamber using a trihedral CR. In outdoor field conditions, where the ground rough surface scattering effects are present, the calibration performance was lower, approximately 1.5 dB. A solution is proposed to overcome the ground effect by elevating the CR above the ground level, which enables applying time-gating around the CR echo, excluding the reflection from the ground.
