Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar.

The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of sub-grid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here we compare 3-arc-sec (~100-m) spatial resolution AirMOSS RZSM retrievals from P-band radar acquisitions over 9 separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Hydrology Model (Flux-PIHM). The two products demonstrate comparable levels of accuracy when evaluated against ground-based soil moisture products and a significant level of temporal cross-correlation. However, relative to the AirMOSS RZSM retrievals, Flux-PIHM RZSM estimates generally demonstrate much lower levels of spatial and temporal variability, and the spatial patterns captured by both products are poorly correlated. Nevertheless, based on a discussion of likely error sources affecting both products, it is argued that the spatial analysis of AirMOSS and Flux-PIHM RZSM fields provide meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.

Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active-Passive Satellite and Evaluation at Core Validation Sites.

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

Investigation of multilevel amplitude modulation for a dual-wavelength free-space optical communications system using realistic channel estimation and minimum mean-squared-error linear equalization.

Fog is a highly dispersive medium at optical wavelengths, and the received pulse waveform may suffer significant distortion. Thus it is desirable to have the impulse response of the propagation channel to recover data transmitted through fog. The fog particle density and the particle size distribution both strongly influence the channel impulse response, yet it is difficult to estimate these parameters. We present a method using a dual-wavelength free-space optical system for estimating the average particle diameter and the particle number density and for approximating the particle distribution function. These parameters serve as inputs to estimate the atmospheric channel impulse response using simulation based on the modified vector radiative transfer theory. The estimated channel response is used to design a minimum mean-square-error equalization filter to improve the bit error rate by correcting distortion in the received signal waveform due to intersymbol interference and additive white Gaussian noise.

Numerical studies on time-domain responses of on-off-keyed modulated optical signals through a dense fog.

We present a numerical technique to simulate the propagation characteristics of an on-off-keyed modulated optical signal through fog. The on-off-keyed modulated light (a square wave) is decomposed into a finite number of harmonic components, and a numerical solution for the vector radiative transfer equation is obtained for each harmonic that corresponds to the modulation frequency. With this method we study the distortion and the pulse spread in the received signal due to attenuation and scattering. We investigate the propagation characteristic of the modulated signal with different communication system parameters. This information can be used to study communication channel reliability.