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Mehmet Kurum

Publications and source records attributed to Mehmet Kurum.

6 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.

Dylan Boyd↗
NASA NTRS2020

Cramer-Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R’s capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the CRLB is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as 2 dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a trade-off study between the available measurements and SM profile.

Signals of Opportunity (SoOp)↗
NASA NTRS2020

SMAP Validation Experiment 2019-2020 (SMAPVEX19/20): Detection of Soil Moisture Under Forest Canopy

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2020 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in June-July 2020 and the other one in October 2020. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

Andreas Colliander↗
NASA NTRS2020

The Backscattering Contribution of Soybean Pods at L-band

L-band (1.25 GHz) radar measurements of a soybean canopy indicate that the emergence of seed pods is a significant contributor to the backscatter during the late stages of the growing season. In order to validate the measured data, a realistic scattering model of the soybean canopy is developed. The parameters of the soybean canopy and underlying soil used in the model vary over the growing season based on in situ measurements. Scattering amplitudes for soybean leaves are modeled analytically by using a thin disk approximation; stem and pods are jointly modeled using a numerical electromagnetic field solver. These scattering amplitudes are together incorporated into a coherent scattering model to obtain the backscattering coefficient for VV- and HH-polarizations. The modeling results show good agreement with the radar field measurements, having RMSEs of 0.51 dB for VV-pol and 1.1 dB for HH-pol. Both measured data and modeled results show that the change of soil moisture can be accurately monitored by L-band backscatter. It is also found that the difference between HH- and VV-polarized backscatter increases as the size of the soybean pods becomes larger. A method is developed here to estimate the number of pods in a soybean canopy based on polarimetric radar backscatter at L-band.

Radar backscatter↗